CN111326237A - Reordering processing method and device, electronic equipment and storage medium - Google Patents
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Abstract
The present disclosure relates to a reordering processing method and apparatus, an electronic device, and a storage medium, wherein the method includes: acquiring a plurality of target objects arranged in a first order; according to an identification analysis strategy, carrying out reordering processing on the plurality of target objects arranged in the first sequence to obtain the plurality of target objects arranged in a second sequence; the first order is different from the second order. By adopting the method and the device, reordering is realized, and the user directly faces the reordering processing result without manual reordering in looking up.
Description
Technical Field
The present disclosure relates to the field of sorting, and in particular, to a method and an apparatus for processing reordering, an electronic device, and a storage medium.
Background
In the related art, through sorting, a group of "unordered" target objects may be adjusted to an "ordered" target object sequence, such as sorting the target objects from large to small or from small to large in order. However, in some application scenarios, such as the case where the target object includes pathological sections, in order to better identify whether a pathological section is positive or negative, so that the user can better deal with the pathological nature of different pathological sections when consulting, a reordering process is required, however, no effective solution exists in the related art.
Disclosure of Invention
In view of this, the present disclosure provides a technical solution for reordering processing.
According to an aspect of the present disclosure, there is provided a reordering processing method, including:
acquiring a plurality of target objects arranged in a first order;
according to an identification analysis strategy, carrying out reordering processing on the plurality of target objects arranged in the first sequence to obtain the plurality of target objects arranged in a second sequence;
the first order is different from the second order.
By adopting the method and the device, through the identification and analysis strategy, the plurality of target objects arranged in the first sequence can be reordered to obtain the plurality of target objects arranged in the second sequence different from the first sequence, so that the reordering is realized, and a user directly faces the reordering processing result without manual reordering during looking up.
In a possible implementation manner, the reordering, according to the recognition analysis policy, the plurality of target objects arranged in the first order to obtain the plurality of target objects arranged in the second order includes:
according to the identification analysis network, carrying out reordering processing on the plurality of target objects arranged in the first sequence to obtain the plurality of target objects arranged in a second sequence;
the recognition analysis network is a trained recognition analysis network obtained by training according to a training sample and used for realizing the recognition analysis strategy.
By adopting the method and the device, the reordering processing is realized through the trained recognition analysis network which is obtained by realizing the recognition analysis strategy and training according to the training sample, and the recognition analysis network obtained after training is obtained after adjusting the parameters of the network in the training process in order to meet the preset target, so that the target requirement is better met, and the reordering precision is improved.
In a possible implementation manner, the obtaining a plurality of target objects arranged in a first order includes:
acquiring a plurality of target objects arranged in a first order in a process of performing image processing on the plurality of target objects; or,
after performing image processing on the plurality of target objects, a plurality of target objects arranged in a first order are acquired.
By adopting the method and the device, the acquisition mode of the target object can be realized in the image processing process and also can be realized after the image processing, thereby supporting various application scenes of image processing.
In a possible implementation manner, the reordering, according to the recognition analysis network, the plurality of target objects arranged in the first order to obtain the plurality of target objects arranged in the second order includes:
according to the identification and analysis network, carrying out classification and identification on the target objects arranged in the first sequence to obtain a plurality of identification results which are classified and evaluated according to expected values;
in response to setting the target output of the recognition analysis network to be sorted based on the expected value, sorting the plurality of recognition results according to the recognition analysis network to obtain the plurality of target objects arranged in the second order.
By adopting the method and the device, classification and identification can be carried out through the identification and analysis network to obtain a plurality of identification results of expected value classification and evaluation, and the identification results can be sequenced through the identification and analysis network to obtain a plurality of target objects in second sequence arrangement. The classification recognition and the sequencing can be obtained through the recognition analysis network, and the recognition analysis network is obtained by adjusting network parameters in the training process in order to meet the preset target, so that the classification recognition and the sequencing can better meet the target requirement, and the reordering precision is improved.
In a possible implementation manner, the reordering, according to the recognition analysis network, the plurality of target objects arranged in the first order to obtain the plurality of target objects arranged in the second order includes:
according to the identification and analysis network, carrying out classification and identification on the target objects arranged in the first sequence to obtain a plurality of identification results which are classified and evaluated according to expected values;
and sorting the plurality of recognition results according to sorting logic obtained from the expected values to obtain the plurality of target objects arranged in the second order.
By adopting the method and the device, classification and identification can be carried out through the identification and analysis network to obtain a plurality of identification results of expected value classification evaluation, and a plurality of target objects in the second sequence can also be obtained through the sequencing logic obtained from the expected values instead of the identification and analysis network. Because the recognition and analysis network is obtained after the network parameters are adjusted in the training process in order to meet the preset target, the classification and recognition are more in line with the target requirements, the sorting is realized by adopting the sorting logic instead of the recognition and analysis network, the processing speed is higher, and the requirements of the reordering precision and the processing speed are considered.
In a possible implementation manner, after obtaining the plurality of target objects arranged in the second order, the method further includes:
and performing sub-sorting processing on the plurality of target objects arranged in the second sequence according to at least two preset categories to obtain different sub-sorting results respectively corresponding to different categories, and feeding back the results to a user for viewing.
By adopting the method and the device, in the post-processing process of the plurality of target objects which are arranged in the second sequence after the reordering, the plurality of target objects can be subjected to sub-ordering processing according to at least two preset categories, so that different sub-ordering results corresponding to different categories respectively are obtained, and the sub-ordering results are fed back to the user for checking, therefore, the user can be assisted to check various sub-ordering results in a more convenient and more intuitive checking mode, and the user can obtain an accurate processing conclusion according to the sub-ordering results.
According to an aspect of the present disclosure, there is provided an image reordering method applied to a digitized image device, the method including:
acquiring a plurality of target images, the plurality of target images being arranged in a first order;
according to any one of the above methods, the plurality of target images are subjected to image reordering to obtain the plurality of target images arranged in a second order;
the first order is different from the second order.
By adopting the method and the device, a plurality of target images can be collected and arranged in a first sequence, and through the identification and analysis strategy, the plurality of target objects arranged in the first sequence can be reordered to obtain a plurality of target objects arranged in a second sequence different from the first sequence, so that reordering is realized, and a user directly faces the reordering processing result without manual reordering during looking up.
In a possible implementation, the digital image device includes: an intelligent scanner or an intelligent microscope with a built-in chip or assembly, wherein the chip or assembly is provided with processing logic for the acquisition and the reordering; or,
the digital image device comprises: a scanner or microscope for performing the acquisition, and a separate device connected to the scanner or microscope for performing the reordered processing logic.
With the present disclosure, including but not limited to the two digital imaging devices, such as the intelligent scanner or the intelligent microscope, or the independent device, it is within the scope of the present disclosure that the device for acquiring and reordering can be implemented.
According to an aspect of the present disclosure, there is provided a reordering processing apparatus, the apparatus including:
an acquisition unit configured to acquire a plurality of target objects arranged in a first order;
a reordering unit, configured to perform reordering processing on the multiple target objects arranged in the first order according to an identification analysis policy, so as to obtain the multiple target objects arranged in a second order;
the first order is different from the second order.
In a possible implementation manner, the reordering unit is configured to perform reordering processing on the plurality of target objects arranged in the first order according to an identification analysis network, so as to obtain the plurality of target objects arranged in a second order;
the recognition analysis network is a trained recognition analysis network obtained by training according to a training sample and used for realizing the recognition analysis strategy.
In a possible implementation manner, the obtaining unit is configured to:
acquiring a plurality of target objects arranged in a first order in a process of performing image processing on the plurality of target objects; or,
after performing image processing on the plurality of target objects, a plurality of target objects arranged in a first order are acquired.
In a possible implementation manner, the reordering unit is configured to:
according to the identification and analysis network, carrying out classification and identification on the target objects arranged in the first sequence to obtain a plurality of identification results which are classified and evaluated according to expected values;
in response to setting the target output of the recognition analysis network to be sorted based on the expected value, sorting the plurality of recognition results according to the recognition analysis network to obtain the plurality of target objects arranged in the second order.
In a possible implementation manner, the reordering unit is configured to:
according to the identification and analysis network, carrying out classification and identification on the target objects arranged in the first sequence to obtain a plurality of identification results which are classified and evaluated according to expected values;
and sorting the plurality of recognition results according to sorting logic obtained from the expected values to obtain the plurality of target objects arranged in the second order.
In a possible implementation manner, the reordering unit is further configured to:
and performing sub-sorting processing on the plurality of target objects arranged in the second sequence according to at least two preset categories to obtain different sub-sorting results respectively corresponding to different categories, and feeding back the results to a user for viewing.
According to an aspect of the present disclosure, there is provided an image reordering apparatus, the apparatus including:
an acquisition unit for acquiring a plurality of target images, the plurality of target images being arranged in a first order;
a reordering unit, configured to reorder the images of the plurality of target images according to any one of claims 1-6, to obtain the plurality of target images arranged in a second order;
the first order is different from the second order.
In a possible implementation, the apparatus includes: the intelligent scanner or the intelligent microscope is embedded with a chip or a component, and the acquisition unit and the reordering processing unit are arranged in the chip or the component; or,
the device comprises: the device comprises a scanner or a microscope provided with the acquisition unit, and an independent device provided with the reordering processing unit and connected with the scanner or the microscope.
According to an aspect of the present disclosure, there is provided an electronic device including: a processor; a memory for storing processor-executable instructions; wherein the processor is configured to perform any of the methods described above.
According to an aspect of the present disclosure, there is provided a computer readable storage medium having computer program instructions stored thereon, wherein the computer program instructions, when executed by a processor, implement the method of any of the above.
In the embodiment of the present disclosure, a plurality of target objects arranged in a first order are obtained; according to an identification analysis strategy, carrying out reordering processing on the plurality of target objects arranged in the first sequence to obtain the plurality of target objects arranged in a second sequence; the first order is different from the second order. By adopting the method and the device, through the identification and analysis strategy, the plurality of target objects arranged in the first sequence can be reordered to obtain the plurality of target objects arranged in the second sequence different from the first sequence, so that the reordering is realized, and a user directly faces the reordering processing result without manual reordering during looking up.
It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the disclosure.
Other features and aspects of the present disclosure will become apparent from the following detailed description of exemplary embodiments, which proceeds with reference to the accompanying drawings.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate exemplary embodiments, features, and aspects of the disclosure and, together with the description, serve to explain the principles of the disclosure.
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present disclosure and, together with the description, serve to explain the principles of the disclosure.
Fig. 1 shows a flow chart of a reordering processing method according to an embodiment of the present disclosure.
Fig. 2 is a schematic diagram illustrating an operation mode of a reordering processing method according to an embodiment of the present disclosure.
Fig. 3 shows a block diagram of a reordering processing apparatus according to an embodiment of the present disclosure.
Fig. 4 illustrates a block diagram of an electronic device in accordance with an embodiment of the disclosure.
Fig. 5 shows a block diagram of an electronic device according to an embodiment of the disclosure.
Detailed Description
Various exemplary embodiments, features and aspects of the present disclosure will be described in detail below with reference to the accompanying drawings. In the drawings, like reference numbers can indicate functionally identical or similar elements. While the various aspects of the embodiments are presented in drawings, the drawings are not necessarily drawn to scale unless specifically indicated.
The word "exemplary" is used exclusively herein to mean "serving as an example, embodiment, or illustration. Any embodiment described herein as "exemplary" is not necessarily to be construed as preferred or advantageous over other embodiments.
The term "and/or" herein is merely an association describing an associated object, meaning that three relationships may exist, e.g., a and/or B, may mean: a exists alone, A and B exist simultaneously, and B exists alone. In addition, the term "at least one" herein means any one of a plurality or any combination of at least two of a plurality, for example, including at least one of A, B, C, and may mean including any one or more elements selected from the group consisting of A, B and C.
Furthermore, in the following detailed description, numerous specific details are set forth in order to provide a better understanding of the present disclosure. It will be understood by those skilled in the art that the present disclosure may be practiced without some of these specific details. In some instances, methods, means, elements and circuits that are well known to those skilled in the art have not been described in detail so as not to obscure the present disclosure.
Fig. 1 is a flowchart illustrating a reordering processing method according to an embodiment of the present disclosure, which is applied to a reordering processing apparatus, for example, when the processing apparatus is deployed in a terminal device or a server or other processing device, the processing apparatus may perform target object classification recognition, target object ordering, and the like. The terminal device may be a terminal device such as a scanner capable of scanning images, or a terminal device such as a microscope capable of acquiring images. In some possible implementations, the processing method may be implemented by a processor calling computer readable instructions stored in a memory. As shown in fig. 1, the process includes:
step S101, a plurality of target objects arranged in a first order are acquired.
In one example, the target object may be an image containing a pathological section, including but not limited to: scanning (e.g., scanning by a scanner) the resulting image, or acquiring (e.g., capturing) the resulting image.
In an example, in order to better identify whether a pathological section is positive or negative, so that a user can better process the pathological nature of different pathological sections when consulting the image, a reordering process is needed, and the disclosure is not limited to pathological section reordering in the medical scene, and can also be other target objects to be classified, identified and reordered.
In one example, the obtaining a plurality of target objects arranged in a first order includes: acquiring a plurality of target objects arranged in a first order in a process of performing image processing on the plurality of target objects; alternatively, after performing image processing on the plurality of target objects, a plurality of target objects arranged in a first order are acquired. For example, the image processing may be a digital processing in a scanned scene, and may be implemented by a scanner. In the case of a scanner, as a basic technology of digital pathology, the scanner is used for digital processing, and through an optical technology and a digital technology, an image containing a pathological section is preprocessed (such as staining, flaking and the like) so that the pathological section (such as a cell section, a nerve ending section, an immunohistochemical section and the like) can be imaged to obtain a digital pathological section image. The pathological section image obtained by imaging is provided for a pathologist or a clinician to browse images on a computer, and a plurality of application scenes such as consultation, grading diagnosis and the like in remote medical treatment can be supported. Therefore, the image containing the pathological section is based on the digital processing of the scanner, the image reading and analysis can be conveniently carried out on the computer, the image storage and the image transmission are easy, and the processing efficiency of the user for the image reading and analysis is improved.
The present disclosure includes at least two application scenarios, and is not limited to the two specific application scenarios, that the target object is obtained and digitized during the scanning process based on the scanner, or the target object is obtained and digitized after the scanning process.
Step S102, according to the identification and analysis strategy, the plurality of target objects arranged in the first sequence are subjected to reordering processing, and the plurality of target objects arranged in the second sequence are obtained. Wherein the first order is different from the second order.
This step can be implemented according to an identification analysis network such as an Artificial Intelligence (AI) algorithm, or according to a non-AI algorithm. The recognition analysis network may be a trained recognition analysis network obtained by training according to a training sample and used for implementing the recognition analysis strategy.
It should be noted that the recognition analysis network may be implemented based on an AI algorithm, and may also be other neural network learning models designed according to specific application scenarios, and the disclosure does not limit the specific algorithm models. According to the recognition analysis network, a plurality of target objects arranged in a first order are reordered, and one case may be: for the classification identification processing, the identification analysis network is adopted; for reordering (e.g., reordering based on expected values), a non-AI ordering approach may be used, i.e., for reordering this may be implemented without the use of the identified analysis network. Another case may be: both classification identification and reordering (e.g., reordering based on expected values) can be implemented using the identification analysis network. Wherein the reordering is based on expected values, including but not limited to: reordering according to the probability parameter; the categories may also be sorted according to "0" or "1", for example, after "0" or "1" is sorted, the categories shown by "1" are sorted before the categories shown by "0", and so on; the reordering may also be performed in physical identification, which may include, in terms of the first order and the second order: the manner in which the Identification (ID) information is physically ordered. Wherein the ID information may include: a numeric ID, a barcode ID, or a two-dimensional code ID.
Wherein, the physical ordering refers to: compared with the logic ordering for realizing the reordering processing, the logic ordering is processing logic, and through the processing logic, the ordering which is finally displayed for a user to look up in a more visual physical sense can be obtained.
With the present disclosure, taking image processing as an example, the reordering processing may be performed on the acquired plurality of target objects arranged in the first order, either during or after the image processing on the plurality of target objects. Since this reordering process can be performed on a plurality of target objects arranged in a first order by identifying an analysis policy, thereby obtaining a plurality of target objects arranged in a second order different from the first order, not only is the reordering process achieved, but also the user is directly faced with the result of the reordering process without manual reordering upon review.
In a possible implementation manner, according to the recognition analysis network, the reordering processing is performed on the plurality of target objects arranged in the first order to obtain the plurality of target objects arranged in the second order, and the processing method at least includes the following two processing manners:
firstly, according to a recognition analysis network, a plurality of target objects arranged in a first sequence are classified and recognized to obtain a plurality of recognition results which are classified and evaluated by expected values such as probability parameters. In response to setting the target output of the recognition and analysis network to be sorted based on the probability parameter (e.g., sorting logic with probability parameters from large to small), the recognition results are sorted according to the recognition and analysis network, and the target objects arranged in the second order are obtained. For example, the content analyzed by the recognition analysis network implemented by the AI algorithm is set as the above recognition results sorted by the probability parameter (e.g., the probability parameter that the analysis slice in the medical scene contains cancer tissue). Thereafter, the above-mentioned ordered processing logic is still performed according to the recognition analysis network implemented by the AI algorithm, thereby obtaining a plurality of target objects arranged in a second order, and the second order is different from the first order.
Secondly, according to the recognition analysis network, reordering the plurality of target objects arranged in the first order to obtain the plurality of target objects arranged in the second order, comprising: according to the identification and analysis network, carrying out classification and identification on the target objects arranged in the first sequence to obtain a plurality of identification results which are classified and evaluated by expected values such as probability parameters; and sequencing the plurality of recognition results according to sequencing logic obtained by the probability parameters to obtain the plurality of target objects arranged in the second sequence. For example, the content analyzed by the recognition analysis network implemented by the AI algorithm is set as the above recognition results sorted by the probability parameter (e.g., the probability parameter that the analysis slice in the medical scene contains cancer tissue). Thereafter, a non-AI algorithm may be employed, i.e., the network may be analyzed without employing the recognition, but directly according to the ordered processing logic, thereby resulting in a plurality of target objects arranged in a second order, the second order being different from the first order.
In this way, not only reordering is achieved, but also reordering is performed by performing all functions (such as obtaining the plurality of recognition results based on classification recognition and executing the processing logic for ordering to obtain the plurality of target objects arranged in the second order) or partial functions (such as obtaining the plurality of recognition results based on classification recognition) of the recognition analysis network, such as an AI algorithm, and the accuracy and processing efficiency of reordering can be improved.
In a possible implementation manner, the plurality of target objects are arranged in the first order, and a target object sequence arranged in the first order may be obtained. In the process of reordering the target objects arranged in the first order according to the identification analysis network, the target object sequence may be reordered to obtain an intermediate ordering result, and the intermediate ordering result is stored in a transfer memory, and the intermediate ordering result is updated to finally obtain a target object sequence arranged in the second order; the first order is different from the second order.
In one example, the intermediate memory may be an internal memory device different from the external memory, and the intermediate memory may be a plurality of sets of chip slots, such as a newly added dedicated auxiliary chip slot.
In addition, the transfer memory can be a multiplexing slice slot under the condition that the transfer memory is a plurality of groups of slice slots, such as a slice slot in an idle state in the existing slice slots stored externally; or a cooperating slice slot, which may be a slice slot based on allocation of free resources and combining the dedicated auxiliary slice slot with the multiplexed slice slot.
In a possible implementation manner, after obtaining the plurality of target objects arranged in the second order, the method further includes: and performing sub-sorting processing on the plurality of target objects arranged in the second sequence according to at least two preset categories to obtain different sub-sorting results respectively corresponding to different categories. The preset at least two categories can be customized into a positive category and a negative category by taking a medical scene as an example, and the sequencing results finally displayed to the user can be respectively sequenced for the positive category and the negative category, such as a sub-sequencing result of strong positive, weak positive and the like; strong negative, weak negative, and the like. And can also be classified and displayed according to the forms of strong positive, weak positive, … …, negative, weak negative and the like.
In a possible implementation, a plurality of target objects arranged in a second order different from the first order, such as a reordered plurality of images containing pathological sections, may be observed under a microscope. For the section with the probability parameter lower than a certain threshold value, the section can be observed by a low-age physician through a microscope or directly through a digital image obtained by a scanner, so that better division of labor is realized, and the processing efficiency is improved.
For example, the correspondence between the images including the pathological sections before and after reordering can be obtained through two-dimensional code scanning or OCR analysis, for example, the initial digitized image and the physical section image obtained after reordering can be referred to as corresponding to the associated user according to the correspondence between the digitized image and the section image, so as to identify which physical section image the digitized image corresponding to the user is associated with. Thus, the user can know the identification result of the physical section image, such as the probability of positive or negative presence, and the like of the contained cancer tissue.
It should be noted that the reordering device implementing the above-mentioned embodiment and possible implementations thereof may be a digitized image device, such as writing the above-mentioned reordering logic into a chip or module, and setting the chip or module in a smart scanner or a smart microscope, etc. The reordering logic may also be provided in an imaging device separate from the smart scanner or the smart microscope and which may be connected to a conventional scanner or microscope.
The disclosed image reordering method is applied to a digital image device, and comprises the following steps: a plurality of target images are acquired, the plurality of target images being arranged in a first order. The above-described embodiments and possible implementations thereof may be adopted to perform image reordering on the plurality of target images to obtain the plurality of target images arranged in the second order. The first order is different from the second order.
In one example, the digital image device may include: an intelligent scanner or an intelligent microscope with a built-in chip or assembly, wherein the chip or assembly is provided with processing logic for the acquisition and the reordering; in another aspect, the digital imaging apparatus includes: a scanner or microscope for performing the acquisition, and a separate device connected to the scanner or microscope for performing the reordered processing logic.
Application example:
taking a scanning scene as an example, a conventional scanner is used, and reordering of a plurality of target objects during or after scanning is not supported, for example, an order obtained after scanning the plurality of target objects by using the conventional scanner is consistent with an arrangement order placed by a user before scanning. If the target object is an image containing pathological sections, the slice types which are positive or negative can be identified through the identification analysis network realized by the AI algorithm, however, only classification marks of the slice types are carried out, and the sequence finally presented to the user is still consistent with the arrangement sequence placed by the user before scanning, but the classification marks are added to facilitate the user to carry out classification reference according to the marks. However, the user is still required to manually perform physical sorting according to the classification mark, i.e., manually select a desired type of section from a large number of images containing pathological sections, sort different types of sections respectively, and the like, and then observe the section under a microscope. This manual screening and sorting approach is time consuming and labor intensive due to the large amount of data.
In order to better classify, screen, label and finally realize physical sequencing on the image containing pathological sections, classification, identification and reordering can be carried out according to an identification and analysis network realized by an AI algorithm without manual work in the scanning process or after scanning so as to obtain the physical sequencing of the required type of sections, and the automatic sequencing workflow is full-intelligent. The arrangement sequence obtained after reordering is inconsistent with the arrangement sequence before scanning, so that not only classification is realized, but also physical sequencing based on classification is realized.
Reordering can be accomplished by incorporating an embedded chip or component (containing the above-described ordering logic in the chip or component) into a conventional scanner. It may also be a stand-alone device that implements the reordering process and is independent of the conventional scanner. However, the present invention is not limited to the scanner, and may also be a microscope, and any digital image device that can image a batch of slices to obtain digitized images and reorder the digitized images to obtain a physical sequence presented to a user is within the protection scope of the present example.
Fig. 2 is a schematic diagram illustrating an operation mode of the reordering processing method according to an embodiment of the present disclosure, and fig. 2 includes a server cluster 21 (which may be connected to a conventional scanner and is not shown in fig. 2) independent from the scanner, an intelligent scanner 22 (embedded chip or component), and reordering processing logic 23 that may be run in the scanner 22 or optionally in the server cluster 21 and respectively execute workflow 1 and workflow 2. Wherein the reordering processing logic comprises: acquiring N images arranged in a first order; and reordering the N images arranged in the first sequence according to the recognition analysis network to obtain N target objects arranged in the second sequence. In the working mode shown in fig. 2, taking workflow 1 as an example, the following contents are included:
first, a user prepares a plurality of target objects (e.g., a plurality of pathological sections) and places them into an intelligent scanner in a first order.
And secondly, sequentially scanning the plurality of pathological sections in an intelligent scanner to obtain a digitized image corresponding to each pathological section.
And thirdly, during or after the scanning is performed by the intelligent scanner, performing identification analysis on all the obtained digitized images by using an identification analysis network realized by an AI algorithm, for example, analyzing the probability that each pathological section contains cancer tissues and using the probability as a probability parameter, performing classification identification and sequencing on a plurality of pathological sections by using the probability parameter, and sequencing according to the probability that each pathological section contains cancer tissues from high to low to obtain a plurality of pathological sections arranged in a second sequence different from the first sequence, thereby assisting a user to preferentially pay attention to the pathological sections containing cancer tissues with higher probability (such as positive appearance, namely malignant lesion). That is, the user only needs to observe the reordered pathological sections under the microscope in the order of the probability from high to low.
In addition to the above scanning scenarios, the present disclosure may perform a customized configuration of reordering for different scenarios, and it is within the scope of the present disclosure as long as the present disclosure relates to screening, classification, identification, reordering, and finally obtaining the scenario that is shown to the user for physical ordering. The physical ordering obtained after reordering is not consistent with the ordering before reordering.
It will be understood by those skilled in the art that in the method of the present invention, the order of writing the steps does not imply a strict order of execution and any limitations on the implementation, and the specific order of execution of the steps should be determined by their function and possible inherent logic.
The above-mentioned method embodiments can be combined with each other to form a combined embodiment without departing from the principle logic, which is limited by the space and will not be repeated in this disclosure.
In addition, the present disclosure also provides a reordering processing apparatus, an electronic device, a computer-readable storage medium, and a program, which can be used to implement any reordering processing method provided by the present disclosure, and the corresponding technical solutions and descriptions and corresponding descriptions in the method sections are not repeated.
Fig. 3 shows a block diagram of a reordering processing apparatus according to an embodiment of the present disclosure, and as shown in fig. 3, the processing apparatus includes: an acquisition unit 31 for acquiring a plurality of target objects arranged in a first order; a reordering unit 32, configured to perform reordering processing on the multiple target objects arranged in the first order according to an identification analysis policy, so as to obtain the multiple target objects arranged in a second order; the first order is different from the second order.
In a possible implementation manner, the reordering unit is configured to perform reordering processing on the plurality of target objects arranged in the first order according to an identification analysis network, so as to obtain the plurality of target objects arranged in a second order; the recognition analysis network is a trained recognition analysis network obtained by training according to a training sample and used for realizing the recognition analysis strategy.
In a possible implementation manner, the obtaining unit is configured to: acquiring a plurality of target objects arranged in a first order in a process of performing image processing on the plurality of target objects; alternatively, after performing image processing on the plurality of target objects, a plurality of target objects arranged in a first order are acquired.
In a possible implementation manner, the reordering unit is configured to: according to the identification and analysis network, carrying out classification and identification on the target objects arranged in the first sequence to obtain a plurality of identification results which are classified and evaluated by expected values such as probability parameters; in response to setting the target output of the recognition and analysis network to be ranked based on the probability parameter, ranking the plurality of recognition results according to the recognition and analysis network, resulting in the plurality of target objects arranged in the second order.
Reordering is performed based on expected values, except that in the above example, the target output of the recognition and analysis network is set to be ordered based on the probability parameter, and reordering is performed based on the probability parameter; the categories may also be sorted according to "0" or "1", for example, after "0" or "1" is sorted, the categories shown by "1" are sorted before the categories shown by "0", and so on; the reordering may also be performed in physical identification, which may include, in terms of the first order and the second order: the manner in which the Identification (ID) information is physically ordered. Wherein the ID information may include: a numeric ID, a barcode ID, or a two-dimensional code ID.
In a possible implementation manner, the reordering unit is configured to: according to the identification and analysis network, carrying out classification and identification on the target objects arranged in the first sequence to obtain a plurality of identification results which are classified and evaluated by expected values such as probability parameters; and sequencing the plurality of recognition results according to sequencing logic obtained by the probability parameters to obtain the plurality of target objects arranged in the second sequence.
In a possible implementation manner, the reordering unit is further configured to: and performing sub-sorting processing on the plurality of target objects arranged in the second sequence according to at least two preset categories to obtain different sub-sorting results respectively corresponding to different categories, and feeding back the results to a user for viewing.
An image reordering apparatus of the present disclosure, the apparatus comprising: an acquisition unit for acquiring a plurality of target images, the plurality of target images being arranged in a first order; a reordering processing unit, configured to perform image reordering on a plurality of target images according to any one of the above embodiments and possible implementation manners, so as to obtain the plurality of target images arranged in a second order; the first order is different from the second order.
In a possible implementation, the apparatus includes: the intelligent scanner or the intelligent microscope is embedded with a chip or a component, and the acquisition unit and the reordering processing unit are arranged in the chip or the component; alternatively, the apparatus comprises: the device comprises a scanner or a microscope provided with the acquisition unit, and an independent device provided with the reordering processing unit and connected with the scanner or the microscope.
In some embodiments, functions of or modules included in the apparatus provided in the embodiments of the present disclosure may be used to execute the method described in the above method embodiments, and specific implementation thereof may refer to the description of the above method embodiments, and for brevity, will not be described again here.
Embodiments of the present disclosure also provide a computer-readable storage medium having stored thereon computer program instructions, which when executed by a processor, implement the above-mentioned method. The computer readable storage medium may be a volatile computer readable storage medium or a non-volatile computer readable storage medium.
The embodiments of the present disclosure also provide a computer program product, which includes computer readable codes, and when the computer readable codes are run on a device, a processor in the device executes instructions for implementing the reordering processing method provided in any of the above embodiments.
The embodiments of the present disclosure also provide another computer program product for storing computer readable instructions, which when executed cause a computer to execute the operations of the reordering processing method provided in any of the above embodiments.
The computer program product may be embodied in hardware, software or a combination thereof. In an alternative embodiment, the computer program product is embodied in a computer storage medium, and in another alternative embodiment, the computer program product is embodied in a Software product, such as a Software Development Kit (SDK), or the like.
An embodiment of the present disclosure further provides an electronic device, including: a processor; a memory for storing processor-executable instructions; wherein the processor is configured as the above method.
The electronic device may be provided as a terminal, server, or other form of device.
Fig. 4 is a block diagram illustrating an electronic device 800 in accordance with an example embodiment. For example, the electronic device 800 may be a mobile phone, a computer, a digital broadcast terminal, a messaging device, a game console, a tablet device, a medical device, a fitness device, a personal digital assistant, or the like terminal.
Referring to fig. 4, electronic device 800 may include one or more of the following components: processing component 802, memory 804, power component 806, multimedia component 808, audio component 810, input/output (I/O) interface 812, sensor component 814, and communication component 816.
The processing component 802 generally controls overall operation of the electronic device 800, such as operations associated with display, telephone calls, data communications, camera operations, and recording operations. The processing components 802 may include one or more processors 820 to execute instructions to perform all or a portion of the steps of the methods described above. Further, the processing component 802 can include one or more modules that facilitate interaction between the processing component 802 and other components. For example, the processing component 802 can include a multimedia module to facilitate interaction between the multimedia component 808 and the processing component 802.
The memory 804 is configured to store various types of data to support operations at the electronic device 800. Examples of such data include instructions for any application or method operating on the electronic device 800, contact data, phonebook data, messages, pictures, videos, and so forth. The memory 804 may be implemented by any type or combination of volatile or non-volatile memory devices such as Static Random Access Memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic or optical disks.
The power supply component 806 provides power to the various components of the electronic device 800. The power components 806 may include a power management system, one or more power supplies, and other components associated with generating, managing, and distributing power for the electronic device 800.
The multimedia component 808 includes a screen that provides an output interface between the electronic device 800 and a user. In some embodiments, the screen may include a Liquid Crystal Display (LCD) and a Touch Panel (TP). If the screen includes a touch panel, the screen may be implemented as a touch screen to receive an input signal from a user. The touch panel includes one or more touch sensors to sense touch, slide, and gestures on the touch panel. The touch sensor may not only sense the boundary of a touch or slide action, but also detect the duration and pressure associated with the touch or slide operation. In some embodiments, the multimedia component 808 includes a front facing camera and/or a rear facing camera. The front camera and/or the rear camera may receive external multimedia data when the electronic device 800 is in an operation mode, such as a shooting mode or a video mode. Each front camera and rear camera may be a fixed optical lens system or have a focal length and optical zoom capability.
The audio component 810 is configured to output and/or input audio signals. For example, the audio component 810 includes a Microphone (MIC) configured to receive external audio signals when the electronic device 800 is in an operational mode, such as a call mode, a recording mode, and a voice recognition mode. The received audio signals may further be stored in the memory 804 or transmitted via the communication component 816. In some embodiments, audio component 810 also includes a speaker for outputting audio signals.
The I/O interface 812 provides an interface between the processing component 802 and peripheral interface modules, which may be keyboards, click wheels, buttons, etc. These buttons may include, but are not limited to: a home button, a volume button, a start button, and a lock button.
The sensor assembly 814 includes one or more sensors for providing various aspects of state assessment for the electronic device 800. For example, the sensor assembly 814 may detect an open/closed state of the electronic device 800, the relative positioning of components, such as a display and keypad of the electronic device 800, the sensor assembly 814 may also detect a change in the position of the electronic device 800 or a component of the electronic device 800, the presence or absence of user contact with the electronic device 800, orientation or acceleration/deceleration of the electronic device 800, and a change in the temperature of the electronic device 800. Sensor assembly 814 may include a proximity sensor configured to detect the presence of a nearby object without any physical contact. The sensor assembly 814 may also include a light sensor, such as a CMOS or CCD image sensor, for use in imaging applications. In some embodiments, the sensor assembly 814 may also include an acceleration sensor, a gyroscope sensor, a magnetic sensor, a pressure sensor, or a temperature sensor.
The communication component 816 is configured to facilitate wired or wireless communication between the electronic device 800 and other devices. The electronic device 800 may access a wireless network based on a communication standard, such as WiFi, 2G or 3G, or a combination thereof. In an exemplary embodiment, the communication component 816 receives a broadcast signal or broadcast related information from an external broadcast management system via a broadcast channel. In an exemplary embodiment, the communication component 816 further includes a Near Field Communication (NFC) module to facilitate short-range communications. For example, the NFC module may be implemented based on Radio Frequency Identification (RFID) technology, infrared data association (IrDA) technology, Ultra Wideband (UWB) technology, Bluetooth (BT) technology, and other technologies.
In an exemplary embodiment, the electronic device 800 may be implemented by one or more Application Specific Integrated Circuits (ASICs), Digital Signal Processors (DSPs), Digital Signal Processing Devices (DSPDs), Programmable Logic Devices (PLDs), Field Programmable Gate Arrays (FPGAs), controllers, micro-controllers, microprocessors or other electronic components for performing the above-described methods.
In an exemplary embodiment, a non-transitory computer-readable storage medium, such as the memory 804, is also provided that includes computer program instructions executable by the processor 820 of the electronic device 800 to perform the above-described methods.
Fig. 5 is a block diagram illustrating an electronic device 900 in accordance with an example embodiment. For example, the electronic device 900 may be provided as a server. Referring to fig. 5, electronic device 900 includes a processing component 922, which further includes one or more processors, and memory resources, represented by memory 932, for storing instructions, such as applications, that are executable by processing component 922. The application programs stored in memory 932 may include one or more modules that each correspond to a set of instructions. Further, the processing component 922 is configured to execute instructions to perform the above-described methods.
The electronic device 900 may also include a power component 926 configured to perform power management of the electronic device 900, a wired or wireless network interface 950 configured to connect the electronic device 900 to a network, and an input/output (I/O) interface 958. The electronic device 900 may operate based on an operating system stored in memory 932, such as WindowsServerTM, Mac OS XTM, UnixTM, LinuxTM, FreeBSDTM, or the like.
In an exemplary embodiment, a non-transitory computer readable storage medium, such as the memory 932, is also provided that includes computer program instructions executable by the processing component 922 of the electronic device 900 to perform the above-described method.
The present disclosure may be systems, methods, and/or computer program products. The computer program product may include a computer-readable storage medium having computer-readable program instructions embodied thereon for causing a processor to implement various aspects of the present disclosure.
The computer readable storage medium may be a tangible device that can hold and store the instructions for use by the instruction execution device. The computer readable storage medium may be, for example, but not limited to, an electronic memory device, a magnetic memory device, an optical memory device, an electromagnetic memory device, a semiconductor memory device, or any suitable combination of the foregoing. More specific examples (a non-exhaustive list) of the computer readable storage medium would include the following: a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a Static Random Access Memory (SRAM), a portable compact disc read-only memory (CD-ROM), a Digital Versatile Disc (DVD), a memory stick, a floppy disk, a mechanical coding device, such as punch cards or in-groove projection structures having instructions stored thereon, and any suitable combination of the foregoing. Computer-readable storage media as used herein is not to be construed as transitory signals per se, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through a waveguide or other transmission medium (e.g., optical pulses through a fiber optic cable), or electrical signals transmitted through electrical wires.
The computer-readable program instructions described herein may be downloaded from a computer-readable storage medium to a respective computing/processing device, or to an external computer or external storage device via a network, such as the internet, a local area network, a wide area network, and/or a wireless network. The network may include copper transmission cables, fiber optic transmission, wireless transmission, routers, firewalls, switches, gateway computers and/or edge servers. The network adapter card or network interface in each computing/processing device receives computer-readable program instructions from the network and forwards the computer-readable program instructions for storage in a computer-readable storage medium in the respective computing/processing device.
The computer program instructions for carrying out operations of the present disclosure may be assembler instructions, Instruction Set Architecture (ISA) instructions, machine-related instructions, microcode, firmware instructions, state setting data, or source or object code written in any combination of one or more programming languages, including an object oriented programming language such as Smalltalk, C + + or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The computer-readable program instructions may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet service provider). In some embodiments, the electronic circuitry that can execute the computer-readable program instructions implements aspects of the present disclosure by utilizing the state information of the computer-readable program instructions to personalize the electronic circuitry, such as a programmable logic circuit, a Field Programmable Gate Array (FPGA), or a Programmable Logic Array (PLA).
Various aspects of the present disclosure are described herein with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the disclosure. It will be understood that each block of the flowchart illustrations and/or block diagrams, and combinations of blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer-readable program instructions.
These computer-readable program instructions may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions/acts specified in the flowchart and/or block diagram block or blocks. These computer-readable program instructions may also be stored in a computer-readable storage medium that can direct a computer, programmable data processing apparatus, and/or other devices to function in a particular manner, such that the computer-readable medium storing the instructions comprises an article of manufacture including instructions which implement the function/act specified in the flowchart and/or block diagram block or blocks.
The computer readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other devices to cause a series of operational steps to be performed on the computer, other programmable apparatus or other devices to produce a computer implemented process such that the instructions which execute on the computer, other programmable apparatus or other devices implement the functions/acts specified in the flowchart and/or block diagram block or blocks.
The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of instructions, which comprises one or more executable instructions for implementing the specified logical function(s). In some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
Different embodiments of the present application may be combined with each other without departing from the logic, and the descriptions of the different embodiments are focused on, and for the parts focused on the descriptions of the different embodiments, reference may be made to the descriptions of the other embodiments.
Having described embodiments of the present disclosure, the foregoing description is intended to be exemplary, not exhaustive, and not limited to the disclosed embodiments. Many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the described embodiments. The terminology used herein is chosen in order to best explain the principles of the embodiments, the practical application, or technical improvements to the technology in the marketplace, or to enable others of ordinary skill in the art to understand the embodiments disclosed herein.
Claims (18)
1. A method of reordering processing, the method comprising:
acquiring a plurality of target objects arranged in a first order;
according to an identification analysis strategy, carrying out reordering processing on the plurality of target objects arranged in the first sequence to obtain the plurality of target objects arranged in a second sequence;
the first order is different from the second order.
2. The method of claim 1, wherein the reordering the plurality of target objects arranged in the first order according to the recognition analysis strategy to obtain the plurality of target objects arranged in the second order comprises:
according to the identification analysis network, carrying out reordering processing on the plurality of target objects arranged in the first sequence to obtain the plurality of target objects arranged in a second sequence;
the recognition analysis network is a trained recognition analysis network obtained by training according to a training sample and used for realizing the recognition analysis strategy.
3. The method of claim 1 or 2, wherein the obtaining a plurality of target objects arranged in a first order comprises:
acquiring a plurality of target objects arranged in a first order in a process of performing image processing on the plurality of target objects; or,
after performing image processing on the plurality of target objects, a plurality of target objects arranged in a first order are acquired.
4. The method of claim 2, wherein the reordering the plurality of target objects in the first order according to the recognition analysis network to obtain the plurality of target objects in the second order comprises:
according to the identification and analysis network, carrying out classification and identification on the target objects arranged in the first sequence to obtain a plurality of identification results which are classified and evaluated according to expected values;
in response to setting the target output of the recognition analysis network to be sorted based on the expected value, sorting the plurality of recognition results according to the recognition analysis network to obtain the plurality of target objects arranged in the second order.
5. The method of claim 2, wherein the reordering the plurality of target objects in the first order according to the recognition analysis network to obtain the plurality of target objects in the second order comprises:
according to the identification and analysis network, carrying out classification and identification on the target objects arranged in the first sequence to obtain a plurality of identification results which are classified and evaluated according to expected values;
and sorting the plurality of recognition results according to sorting logic obtained from the expected values to obtain the plurality of target objects arranged in the second order.
6. The method of claim 4 or 5, wherein after obtaining the plurality of target objects arranged in the second order, the method further comprises:
and performing sub-sorting processing on the plurality of target objects arranged in the second sequence according to at least two preset categories to obtain different sub-sorting results respectively corresponding to different categories, and feeding back the results to a user for viewing.
7. A method for reordering images, the method being applied to a digital imaging device, the method comprising:
acquiring a plurality of target images, the plurality of target images being arranged in a first order;
the method of any of claims 1-6, wherein the plurality of target images are subjected to image reordering to obtain the plurality of target images arranged in a second order;
the first order is different from the second order.
8. The method of claim 7, wherein the digitized image device comprises: an intelligent scanner or an intelligent microscope with a built-in chip or assembly, wherein the chip or assembly is provided with processing logic for the acquisition and the reordering; or,
the digital image device comprises: a scanner or microscope for performing the acquisition, and a separate device connected to the scanner or microscope for performing the reordered processing logic.
9. A reordering processing apparatus, wherein the apparatus comprises:
an acquisition unit configured to acquire a plurality of target objects arranged in a first order;
a reordering unit, configured to perform reordering processing on the multiple target objects arranged in the first order according to an identification analysis policy, so as to obtain the multiple target objects arranged in a second order;
the first order is different from the second order.
10. The apparatus of claim 9, wherein the reordering unit is configured to perform reordering processing on the plurality of target objects arranged in the first order according to a recognition analysis network to obtain the plurality of target objects arranged in a second order;
the recognition analysis network is a trained recognition analysis network obtained by training according to a training sample and used for realizing the recognition analysis strategy.
11. The apparatus according to claim 9 or 10, wherein the obtaining unit is configured to:
acquiring a plurality of target objects arranged in a first order in a process of performing image processing on the plurality of target objects; or,
after performing image processing on the plurality of target objects, a plurality of target objects arranged in a first order are acquired.
12. The apparatus of claim 10, wherein the reordering unit is configured to:
according to the identification and analysis network, carrying out classification and identification on the target objects arranged in the first sequence to obtain a plurality of identification results which are classified and evaluated according to expected values;
in response to setting the target output of the recognition analysis network to be sorted based on the expected value, sorting the plurality of recognition results according to the recognition analysis network to obtain the plurality of target objects arranged in the second order.
13. The apparatus of claim 10, wherein the reordering unit is configured to:
according to the identification and analysis network, carrying out classification and identification on the target objects arranged in the first sequence to obtain a plurality of identification results which are classified and evaluated according to expected values;
and sorting the plurality of recognition results according to sorting logic obtained from the expected values to obtain the plurality of target objects arranged in the second order.
14. The apparatus according to claim 12 or 13, wherein the reordering unit is further configured to:
and performing sub-sorting processing on the plurality of target objects arranged in the second sequence according to at least two preset categories to obtain different sub-sorting results respectively corresponding to different categories, and feeding back the results to a user for viewing.
15. An apparatus for reordering images, the apparatus comprising:
an acquisition unit for acquiring a plurality of target images, the plurality of target images being arranged in a first order;
a reordering unit, configured to reorder the images of the plurality of target images according to any one of claims 1-6, to obtain the plurality of target images arranged in a second order;
the first order is different from the second order.
16. The apparatus of claim 15, wherein the apparatus comprises: the intelligent scanner or the intelligent microscope is embedded with a chip or a component, and the acquisition unit and the reordering processing unit are arranged in the chip or the component; or,
the device comprises: the device comprises a scanner or a microscope provided with the acquisition unit, and an independent device provided with the reordering processing unit and connected with the scanner or the microscope.
17. An electronic device, comprising:
a processor;
a memory for storing processor-executable instructions;
wherein the processor is configured to: performing the method of any one of claims 1 to 6, claims 7 to 8.
18. A computer readable storage medium having computer program instructions stored thereon, wherein the computer program instructions, when executed by a processor, implement the method of any one of claims 1 to 6, 7 to 8.
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114496073A (en) * | 2022-01-21 | 2022-05-13 | 至本医疗科技（上海）有限公司 | Method, computing device, and computer storage medium for identifying positive rearrangements |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107391608A (en) * | 2017-06-30 | 2017-11-24 | 广东欧珀移动通信有限公司 | image display method, device, storage medium and electronic equipment |
| US20180373953A1 (en) * | 2017-06-26 | 2018-12-27 | Verizon Patent And Licensing Inc. | Object recognition based on hierarchical domain-based models |
| US20190147054A1 (en) * | 2017-11-14 | 2019-05-16 | International Business Machines Corporation | Sorting images based on learned actions |
| CN109886185A (en) * | 2019-02-18 | 2019-06-14 | 深圳市商汤科技有限公司 | A kind of target identification method, device, electronic equipment and computer storage medium |
| CN110070046A (en) * | 2019-04-23 | 2019-07-30 | 北京市商汤科技开发有限公司 | Facial image recognition method and device, electronic equipment and storage medium |
| CN110399334A (en) * | 2018-04-20 | 2019-11-01 | 北京太章机电设备有限公司 | The method and system that paper document is ranked up |
-
2020
- 2020-01-21 CN CN202010070980.8A patent/CN111326237A/en not_active Withdrawn
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20180373953A1 (en) * | 2017-06-26 | 2018-12-27 | Verizon Patent And Licensing Inc. | Object recognition based on hierarchical domain-based models |
| CN107391608A (en) * | 2017-06-30 | 2017-11-24 | 广东欧珀移动通信有限公司 | image display method, device, storage medium and electronic equipment |
| US20190147054A1 (en) * | 2017-11-14 | 2019-05-16 | International Business Machines Corporation | Sorting images based on learned actions |
| CN110399334A (en) * | 2018-04-20 | 2019-11-01 | 北京太章机电设备有限公司 | The method and system that paper document is ranked up |
| CN109886185A (en) * | 2019-02-18 | 2019-06-14 | 深圳市商汤科技有限公司 | A kind of target identification method, device, electronic equipment and computer storage medium |
| CN110070046A (en) * | 2019-04-23 | 2019-07-30 | 北京市商汤科技开发有限公司 | Facial image recognition method and device, electronic equipment and storage medium |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114496073A (en) * | 2022-01-21 | 2022-05-13 | 至本医疗科技（上海）有限公司 | Method, computing device, and computer storage medium for identifying positive rearrangements |
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Application publication date: 20200623 |