WO2020134533A1 - 深度模型训练方法及装置、电子设备及存储介质 - Google Patents

深度模型训练方法及装置、电子设备及存储介质 Download PDF

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WO2020134533A1
WO2020134533A1 PCT/CN2019/114497 CN2019114497W WO2020134533A1 WO 2020134533 A1 WO2020134533 A1 WO 2020134533A1 CN 2019114497 W CN2019114497 W CN 2019114497W WO 2020134533 A1 WO2020134533 A1 WO 2020134533A1
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model
training
training set
labeling information
segmentation
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French (fr)
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李嘉辉
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Beijing Sensetime Technology Development Co Ltd
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Beijing Sensetime Technology Development Co Ltd
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Priority to KR1020217007097A priority patent/KR20210042364A/ko
Priority to JP2021537466A priority patent/JP7110493B2/ja
Publication of WO2020134533A1 publication Critical patent/WO2020134533A1/zh
Priority to US17/225,368 priority patent/US20210224598A1/en
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Definitions

  • This application relates to the field of information technology but is not limited to the field of information technology, and in particular to a method and device for training a deep model, electronic equipment, and a storage medium.
  • the training set usually includes training data and labeled data of the training data.
  • labeling data requires manual labeling manually.
  • all the training data is labeled manually, which has a large workload, low efficiency, and manual errors in the labeling process;
  • high-precision labeling is required, such as the labeling in the image field, it is necessary to achieve pixel-level Segmentation, pure manual labeling must achieve pixel-level segmentation, which is very difficult and the labeling accuracy is difficult to guarantee.
  • the training of deep learning models based on purely manually labeled training data may result in low training efficiency and the resulting model's accuracy because of the low accuracy of the training data, resulting in the model's classification or recognition ability being less accurate than expected.
  • the embodiments of the present application are expected to provide a deep model training method and device, electronic equipment, and storage medium.
  • a first aspect of an embodiment of the present application provides a deep learning model training method, including:
  • n is an integer greater than 1;
  • the method includes:
  • N is the maximum number of training rounds
  • n is less than N, obtain the n+1th first labeling information output by the first model, and obtain the n+1th second labeling information output by the second model.
  • the acquiring the training data and the initial annotation information of the training data includes:
  • the generating the first training set of the first model and the first training set of the second model based on the initial annotation information includes:
  • a first training set of the first model and a first training set of the second model are generated.
  • the generating the first training set of the first model and the first training set of the second model based on the initial labeling information further includes:
  • a first training set of the first model and a first training set of the second model are generated.
  • the drawing outlines consistent with the shape of the segmentation target in the circumscribed frame based on the circumscribed frame includes:
  • an inscribed ellipse of the circumscribed frame consistent with the cell shape is drawn in the circumscribed frame.
  • a second aspect of an embodiment of the present application provides a deep learning model training device, including:
  • the labeling module is configured to obtain the n+1th first labeling information output by the first model, the first model undergoes n rounds of training; and, obtain the n+1th second labeling information output by the second model, the first The second model has been trained for n rounds; n is an integer greater than 1;
  • the first generating module is configured to generate an n+1th training set of the second model based on the training data and the n+1th first labeling information, and based on the training data and the n+1th first 2. Annotate information to generate the n+1th training set of the first model;
  • the training module is configured to input the n+1th training set of the second model to the second model, and perform the n+1th round of training on the second model; the n+th training of the first model 1
  • the training set is input to the first model, and the n+1th round of training is performed on the first model.
  • the device includes:
  • the determination module is configured to determine whether n is less than N, and N is the maximum number of training rounds;
  • the labeling module is configured to obtain n+1th first labeling information output by the first model if n is less than N, and obtain n+1th second labeling information output by the second model.
  • the device includes:
  • An acquisition module configured to acquire the training data and the initial annotation information of the training data
  • the second generation module is configured to generate the first training set of the first model and the first training set of the second model based on the initial annotation information.
  • the acquisition module is configured to acquire a training image including multiple segmentation targets and an external frame of the segmentation targets;
  • the second generation module is configured to draw a labeled contour in the circumscribed frame consistent with the shape of the segmentation target based on the circumscribed frame; generate the first model based on the training data and the labeled contour And the first training set of the second model.
  • the first generating module is configured to generate a segmentation boundary of two segmentation targets with overlapping portions based on the circumscribed frame; and generate the first segmentation based on the training data and the segmentation boundary A first training set of the model and a first training set of the second model.
  • the second generation module is configured to draw an inscribed ellipse of the circumscribed frame that is consistent with the cell shape in the circumscribed frame based on the circumscribed frame.
  • a third aspect of embodiments of the present application provides a computer storage medium that stores computer-executable instructions; the computer-executable instructions; after the computer-executable instructions are executed, any of the foregoing technical solutions can be implemented Provided deep learning model training methods.
  • a fourth aspect of the embodiments of the present application provides an electronic device, including:
  • a processor connected to the memory, is configured to implement the deep learning model training method provided by any one of the foregoing technical solutions by executing computer-executable instructions stored on the memory.
  • a fifth aspect of the embodiments of the present application provides a computer program product, the program product including computer-executable instructions; after the computer-executable instructions are executed, the deep learning model training method provided by any one of the foregoing technical solutions can be provided.
  • the technical solution provided by the embodiments of the present application will use the deep learning model to mark the training data after the previous round of training is completed to obtain labeling information, which is used as a training sample for the next round of training of another model, which can be used very little
  • the initial manually labeled training data is used for model training, and then the labeled data output by the first and second models that gradually converge are used as the training samples for the next round of another model.
  • the model parameters of the deep learning model will be generated based on most of the correctly labeled data, and a small amount of incorrectly labeled or lowly labeled data will have little effect on the model parameters of the deep learning model.
  • the annotation information of the in-depth model will become more and more accurate.
  • the model uses its own labeling information to build training samples, it reduces the amount of data manually annotated, reduces the efficiency and artificial errors caused by manual manual annotation, and has the characteristics of fast model training and good training effect.
  • the deep learning model trained in this way has the characteristics of high classification or recognition accuracy.
  • at least two models are trained at the same time, which reduces the learning abnormality of the final deep learning model caused by repeated iteration after a single model learns a wrong feature.
  • the result of labeling the training data after the previous round of training of one model will be used for the next round of learning of another model. In this way, the two models can be used to prepare the next round of training data for each other to reduce A single model repeatedly iterates to strengthen certain errors, which can reduce the phenomenon of model learning errors and improve the training effect of deep learning models.
  • FIG. 1 is a schematic flowchart of a first deep learning model training method provided by an embodiment of this application;
  • FIG. 2 is a schematic flowchart of a second deep learning model training method provided by an embodiment of this application.
  • FIG. 3 is a schematic flowchart of a third deep learning model training method provided by an embodiment of this application.
  • FIG. 4 is a schematic structural diagram of a deep learning model training device provided by an embodiment of this application.
  • FIG. 5 is a schematic diagram of a change of a training set provided by an embodiment of this application.
  • FIG. 6 is a schematic structural diagram of an electronic device according to an embodiment of the present application.
  • this embodiment provides a deep learning model training method, including:
  • Step S110 Obtain the n+1th first labeling information output by the first model, the first model has undergone n rounds of training; and, obtain the n+1th second labeling information output by the second model, the second The model has been trained for n rounds; n is an integer greater than 1;
  • Step S120 generate an n+1th training set of the second model based on the training data and the n+1th first labeling information, and based on the training data and the n+1th second labeling information, Generating an n+1th training set of the first model;
  • Step S130 input the n+1th training set of the second model to the second model, perform the n+1th round of training on the second model; train the n+1th training of the first model The set is input to the first model, and the n+1th round of training is performed on the first model.
  • the deep learning model training method provided in this embodiment can be used in various electronic devices, for example, in various large data model training servers.
  • all the first labeling information and the second labeling information may include but are not limited to labeling information on the image.
  • the image may include medical images and the like.
  • the medical image may be a planar (2D) medical image or a stereoscopic (3D) medical image composed of an image sequence formed by a plurality of 2D images.
  • Each of the first labeling information and the second labeling information may be a label for an organ and/or tissue in a medical image, or may be a label for different cell structures in a cell, such as a label for a cell nucleus.
  • step S110 in this embodiment the training data will be processed using the first model that has completed n rounds of training.
  • the first model will obtain an output, which is the n+1th first labeling data .
  • the n+1th first labeling data corresponds to the training data to form the n+1th training set of the second model.
  • the step S110 will also use the second model that has completed n rounds of training to process the training data.
  • the second model will obtain an output, which is the n+1th second labeling data.
  • the n+1 training set of the first model is formed.
  • the first labeling data are the labeling information obtained by the first model identifying or classifying the training data; the second labeling information is the labeling obtained by the second model identifying or identifying the training data information.
  • the n+1th first label data is used for the n+1 round of training of the second model, and the n+1 second label data is used for the n+1 round of training of the first model.
  • the training samples of the first model and the second model in the n+1th round of this embodiment are automatically generated, and the user does not need to manually mark the training set of the n+1th round of training, which reduces the consumption of manually manually labeling the samples Time, improves the training rate of deep learning models, and reduces the phenomenon of inaccurate or inaccurate manual labeling of deep learning models.
  • the classification or recognition results of the model after training are not accurate enough, which improves the classification of deep learning models after training. Or the accuracy of the recognition results.
  • the first label data of the first model is used to train the second model
  • the second label data of the second model is used to train the first model
  • the label data of the first model itself is suppressed It is used for the phenomenon of erroneous enhancement in model training caused by the next round of self training. In this way, the training effect of the first model and the second model can be improved.
  • the first model and the second model refer to two independent models, but the two models may be the same or different.
  • the first model and the second model may be the same type of deep learning model or different types of deep learning models.
  • the first model and the second model may be deep learning models of different network structures, for example, the first model is a fully connected convolutional network (FNN), and the second model may be an ordinary convolution Product Neural Network (CNN).
  • the first model may be a recurrent neural network, and the second model may be FNN or CNN.
  • the first model may be V-NET, and the second model may be U-NET or the like.
  • the probability of the same error generated by the first model and the second model based on the same first training set during training is greatly reduced, which can further suppress repeated iterations During the process, the first model and the second model are strengthened because of the same error, and the training results can be improved again.
  • the completion of a round of training in this embodiment includes: the first model and the second model have completed at least one learning for each training sample in their respective training sets.
  • the first training sample may be the S images and the manual labeling results of the S images. If one of the S images is not accurate enough to label the image, but the first During the first round of training for the first model and the second model, since the accuracy of the annotation structure of the remaining S-1 images reaches the expected threshold, the S-1 images and their corresponding annotation data
  • the image of the model parameters of the model is larger.
  • the deep learning model includes but is not limited to a neural network; the model parameters include but are not limited to: weights and/or thresholds of network nodes in the neural network.
  • the neural network may be various types of neural networks, for example, U-net or V-net.
  • the neural network may include an encoding part that performs feature extraction on the training data and a decoding part that acquires semantic information based on the extracted features.
  • the encoding part can perform feature extraction on the area where the segmentation target is located in the image to obtain a mask image that distinguishes the segmentation target from the background.
  • the decoder can obtain some semantic information based on the mask image, for example, the target's Omics features, etc.
  • the omics features may include: morphological features such as area, volume, shape of the target, and/or, gray value features formed based on the gray value.
  • the characteristics of the gray value may include: statistical characteristics of the histogram and the like.
  • the first model and the second model after the first round of training recognize S images, they will automatically mark which image is not accurate enough, using the other S-1 images. Learn to obtain network parameters for labeling, and the labeling accuracy at this time is the same as the labeling accuracy of other S-1 images, so the second labeling information corresponding to this image will be better than the original first labeling information. Increased accuracy.
  • the second training set of the first model composed includes the training data composed of the S images and the first annotation information generated by the second model.
  • the second training set of the second model includes: training data and the first annotation information of the first model.
  • the training data and the second label information output by the second model are used. If the second model does not have the error A, then the first 2 The labeling information will not be affected by the error A.
  • using the second labeling information of the second model to train the first model for the second round of training can always enhance the error A in the first model. Therefore, in this embodiment, the first model and the second model can be used to learn based on most correct or high-precision labeling information during the training process to gradually suppress the negative effects of training samples with insufficient or incorrect initial labeling accuracy.
  • the training data takes an image as an example.
  • the training data may also be a voice segment other than the image, text information other than the image, etc.
  • the training data has many forms It is not limited to any of the above.
  • the method includes:
  • Step S100 Determine whether n is less than N, where N is the maximum number of training rounds;
  • the step S110 may include:
  • n is less than N
  • the first model that completes the nth round of training is used to label the training data to obtain n+1 first labeling information
  • the second model that completes the nth round of training is used to label the training data, Obtain the n+1 second label information.
  • the n+1th training set before constructing the n+1th training set, it is first determined whether the current number of training rounds has reached the predetermined maximum number of training rounds N, and if it is not reached, the n+1th labeling information is generated to construct the first The n+1th training set of the model and the second model, otherwise, it is determined that the model training is completed to stop the training of the deep learning model.
  • the value of N may be 4, 5, 6, 7 or 8 empirical values or statistical values.
  • the value of N may range from 3 to 10, and the value of N may be a user input value received by the training device from the human-computer interaction interface.
  • determining whether to stop training may further include:
  • test set Use the test set to test the first model and the second model. If the test result indicates that the accuracy of the first model and the second model's labeling result of the test data in the test set reaches a specific value, stop the first The training of one model and the second model, otherwise enter the step S110 to enter the next round of training.
  • the test set may be an accurately labeled data set, so it can be used to measure the training results of each round of a first model and a second model to determine whether to stop the training of the first model and the second model.
  • the method includes:
  • Step S210 Obtain the training data and the initial annotation information of the training data
  • Step S220 Based on the initial annotation information, generate a first training set of the first model and a first training set of the second model.
  • the initial labeling information may be original labeling information of the training data, and the original labeling information may be information manually labeled manually, or may be information labeled by other devices. For example, information marked by other devices with certain marking capabilities.
  • the first first labeling information and the first second identification information are generated based on the initial labeling information.
  • the first first labeling information and the first first identification information may directly include: the initial labeling information and/or the refined labeling information generated according to the initial standard information.
  • the initial labeling information may be labeling information that roughly labels the location of the cell imaging
  • the refined labeling information may be a location that accurately indicates the location of the cell Labeling
  • the precision of the refined labeling information on the segmentation object may be higher than the accuracy of the initial labeling information
  • the initial labeling information may be a circumscribed frame of cells drawn manually by a doctor.
  • the refined labeling information may be: an inscribed ellipse generated by the training device based on a manually labeled outer frame. Compared with the circumscribed frame, the calculation of the inscribed ellipse reduces the number of pixels that do not belong to the cell imaging in the cell imaging, so the accuracy of the first labeling information is higher than the accuracy of the initial labeling information.
  • the step S210 may include: obtaining a training image including a plurality of segmentation targets and an external frame of the segmentation targets;
  • the step S220 may include: based on the circumscribed frame, drawing a labeled contour in the circumscribed frame consistent with the shape of the segmentation target; based on the training data and the labeled contour, generating a first model of the first model A training set and the first training set of the second model.
  • the annotated contour that is consistent with the segmentation target shape may be the aforementioned ellipse, or may be a circle, or, a triangle or other contralateral shape is equal to the segmentation target shape, and is not limited to an ellipse.
  • the marked outline is inscribed in the outer frame.
  • the external frame may be a rectangular frame.
  • the step S220 further includes:
  • a first training set of the first model and a first training set of the second model are generated.
  • the drawing an outline corresponding to the shape of the segmentation target in the circumscribed frame based on the circumscribed frame includes: drawing the cell shape in the circumscribed frame based on the circumscribed frame The ellipse inside the outer frame is consistent.
  • the first labeling information further includes: a segmentation boundary between the two overlapping segmentation targets.
  • cell imaging A is superimposed on cell imaging B, then after cell imaging A is drawn out of the cell boundary and after cell B imaging is drawn out of the cell boundary, the two cell boundaries intersect to form part of the two Intersection between cell imaging.
  • the portion of the cell boundary of the cell imaging B located inside the cell imaging A may be erased, and the part of the cell imaging A located in the cell imaging B may be As the division boundary.
  • the step S220 may include: drawing the division boundary on the overlapping part of the two using the positional relationship of the two division targets.
  • the segmentation boundary when drawing the segmentation boundary, it can be achieved by modifying the boundary of one of the two segmentation targets with overlapping boundaries.
  • the pixel expansion can be used to thicken the boundary.
  • the cell boundary of the cell imaging A is expanded by a predetermined number of pixels in the direction of the overlapping portion toward the cell imaging B, for example, 1 or more pixels, and the cell of the overlapping portion is thickened to the boundary of the imaging A, thereby making the bolding
  • the boundary is recognized as a dividing boundary.
  • the drawing an outline corresponding to the shape of the segmentation target in the circumscribed frame based on the circumscribed frame includes: drawing the cell shape in the circumscribed frame based on the circumscribed frame The ellipse inside the outer frame is consistent.
  • the segmentation target is cell imaging
  • the marked outline includes an inscribed ellipse of a circumscribed frame of the cell shape.
  • the first labeling information includes at least one of the following:
  • the cell boundary of the cell imaging (corresponding to the inscribed ellipse);
  • the segmentation target is not a cell but other targets, for example, the segmentation target is a face in a collective phase, the outer frame of the face may still be a rectangular frame, but at this time the boundary of the face may be marked It is the border of an oval-shaped face, the border of a round face, etc. In this case, the shape is not limited to the inscribed ellipse.
  • the first model and the second model use the training results of the previous round of the other model to output the labeled information of the training data to construct the training set of the next round. Iterate multiple times to complete model training without manually labeling a large number of training samples. It has a fast training rate and can improve training accuracy through repeated iterations.
  • an embodiment of the present application provides a deep learning model training device, including:
  • the labeling module 110 is configured to obtain the n+1th first labeling information output by the first model, the first model undergoes n rounds of training; and, to obtain the n+1th second labeling information output by the second model, the The second model has been trained for n rounds; n is an integer greater than 1;
  • the first generation module 120 is configured to generate an n+1th training set of the second model based on the training data and the n+1th first labeling information, and based on the training data and the n+1th training set Second annotation information to generate the n+1th training set of the first model;
  • the training module 130 is configured to input the n+1th training set of the second model to the second model, and perform the n+1th round of training on the second model; the nth training set of the first model The +1 training set is input to the first model, and the n+1th round of training is performed on the first model.
  • the labeling module 110, the first generating module 120, and the training module 130 may be program modules, which can be implemented by the processor after being executed by the processor.
  • the labeling module 110, the first generation module 120, and the training module 130 may be soft-hard combination models; the soft-hard combination modules may be various programmable arrays, for example, field programmable arrays Or complex programmable array.
  • the labeling module 110, the first generation module 120, and the training module 130 may be pure hardware modules, and the pure hardware modules may be application specific integrated circuits.
  • the device includes:
  • the determination module is configured to determine whether n is less than N, where N is the maximum number of training rounds;
  • the labeling module is configured to obtain n+1th first labeling information output by the first model if n is less than N; and obtain n+1th second labeling information output by the second model.
  • the device includes:
  • An acquisition module configured to acquire the training data and the initial annotation information of the training data
  • the second generation module is configured to generate the first training set of the first model and the first training set of the second model based on the initial annotation information.
  • the acquisition module is configured to acquire a training image including multiple segmentation targets and an external frame of the segmentation targets;
  • the second generation module is configured to draw a labeled contour in the circumscribed frame consistent with the shape of the segmentation target based on the circumscribed frame; generate the first model based on the training data and the labeled contour And the first training set of the second model.
  • the first generating module is configured to generate a segmentation boundary of two segmentation targets with overlapping portions based on the circumscribed frame; and generate the first segment based on the training data and the segmentation boundary A first training set of a model and a first training set of the second model.
  • the second generation module is configured to draw an inscribed ellipse of the circumscribed frame that is consistent with the cell shape in the circumscribed frame based on the circumscribed frame.
  • the first two models predict a result, and then use each other's prediction to repeat the above process.
  • the original image is annotated, and the second model obtains a mask image to construct the first training set of the first model and the first training set of the second model.
  • the first training set is used to perform the first model and The second model performs the first round of training.
  • the first model is used for image recognition to obtain annotation information, and a second training set of the second model is generated based on the annotation information.
  • the second model is used for image recognition to obtain annotation information, which is used to generate the second training set of the first model. Perform the second round of training for the first model and the second model separately; after repeatedly forming the training set in this way, stop training after iterative training for multiple rounds.
  • the deep learning model training method does not perform any calculation on the output segmentation probability map, and directly takes it as a union with the annotation map, and then continues to train the model. This process is simple to implement.
  • an electronic device including:
  • Memory used to store information
  • a processor connected to the memory, is configured to execute the deep learning model training method provided by the foregoing one or more technical solutions by executing computer-executable instructions stored on the memory, for example, as shown in FIGS. 1 to 3 One or more of the methods shown.
  • the memory may be various types of memory, such as random access memory, read-only memory, flash memory, etc.
  • the memory can be used for information storage, for example, storing computer-executable instructions.
  • the computer executable instructions may be various program instructions, for example, target program instructions and/or source program instructions.
  • the processor may be various types of processors, for example, a central processor, a microprocessor, a digital signal processor, a programmable array, a digital signal processor, an application specific integrated circuit, or an image processor.
  • the processor may be connected to the memory through a bus.
  • the bus may be an integrated circuit bus or the like.
  • the terminal device may further include: a communication interface, and the communication interface may include: a network interface, for example, a local area network interface, a transceiver antenna, and the like.
  • the communication interface is also connected to the processor and can be used for information transmission and reception.
  • the electronic device further includes a camera, which can collect various images, such as medical images.
  • the terminal device further includes a human-machine interaction interface.
  • the human-machine interaction interface may include various input and output devices, such as a keyboard, a touch screen, and so on.
  • An embodiment of the present application provides a computer storage medium that stores computer executable code; after the computer executable code is executed, the deep learning model training method provided by one or more of the foregoing technical solutions can be implemented For example, one or more of the methods shown in FIGS. 1-3.
  • the storage medium includes: mobile storage devices, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), magnetic disks or optical disks, and other media that can store program codes.
  • the storage medium may be a non-transitory storage medium.
  • An embodiment of the present application provides a computer program product, the program product including computer-executable instructions; after the computer-executable instructions are executed, the deep learning model training method provided by any of the foregoing implementations can be implemented, for example, as shown in FIGS. 1 to One or more of the methods shown in FIG. 3.
  • the disclosed device and method may be implemented in other ways.
  • the device embodiments described above are only schematic.
  • the division of the units is only a division of logical functions.
  • the displayed or discussed components are coupled to each other, or directly coupled, or the communication connection may be through some interfaces, and the indirect coupling or communication connection of the device or unit may be electrical, mechanical, or other forms of.
  • the above-mentioned units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place or distributed to multiple network units; Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
  • the functional units in the embodiments of the present application may all be integrated into one processing module, or each unit may be separately used as a unit, or two or more units may be integrated into one unit; the above integration
  • the unit can be implemented in the form of hardware, or in the form of hardware plus software functional units.
  • the foregoing program may be stored in a computer-readable storage medium, and when the program is executed, Including the steps of the above method embodiments; and the foregoing storage media include: mobile storage devices, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), magnetic disks or optical disks, etc.
  • ROM read-only memory
  • RAM random access memory
  • magnetic disks or optical disks etc.

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Abstract

公开了一种深度模型训练方法及装置、电子设备及存储介质。所述深度学习模型训练方法，包括：获取第一模型输出的第n+1第一标注信息，所述第一模型已经过n轮训练；以及，获取第二模型输出的第n+1第二标注信息，所述第二模型已经过n轮训练；n为大于1的整数；基于训练数据及所述第n+1第一标注信息，生成第二模型的第n+1训练集，并基于训练数据及所述第n+1第二标注信息，生成所述第一模型的第n+1训练集；将所述第二模型的第n+1训练集输入至所述第二模型，对所述第二模型进行第n+1轮训练；将所述第一模型的第n+1训练集输入至所述第一模型，对所述第一模型进行第n+1轮训练。

Description

深度模型训练方法及装置、电子设备及存储介质
相关申请的交叉引用
本申请基于申请号为201811646736.0、申请日为2018年12月29日的中国专利申请提出，并要求该中国专利申请的优先权，该中国专利申请的全部内容在此引入本申请作为参考。
技术领域
本申请涉及信息技术领域但不限于信息技术领域，尤其涉及一种深度模型训练方法及装置、电子设备及存储介质。
背景技术
深度学习模型可以通过训练集的训练之后，具有一定的分类或识别能力。所述训练集通常包括：训练数据及训练数据的标注数据。但是一般情况下，标注数据都需要人工进行手动标注。一方面纯手动标注所有的训练数据，工作量大、效率低，且标注过程中存在人工错误；另一方面，若需要实现高精度的标注，例如以图像领域的标注为例，需要实现像素级分割，纯人工标注要达到像素级分割，难度非常大且标注精度也难以保证。
故基于纯人工标注的训练数据进行深度学习模型的训练，会存在训练效率低、训练得到的模型因为训练数据自身精度低导致模型的分类或识别能力精度达不到预期。
发明内容
有鉴于此，本申请实施例期望提供一种深度模型训练方法及装置、电子设备及存储介质。
本申请的技术方案是这样实现的：
本申请实施例第一方面提供一种深度学习模型训练方法，包括：
获取第一模型输出的第n+1第一标注信息，所述第一模型经过n轮训练；以及，获取第二模型输出的第n+1第二标注信息，所述第二模型已经过n轮训练；n为大于1的整数；
基于所述训练数据及所述第n+1第一标注信息，生成第二模型的第n+1训练集，并基于所述训练数据及所述第n+1第二标注信息，生成所述第一模型的第n+1训练集；
将所述第一模型的第n+1训练集输入至所述第二模型，对所述第二模型进行第n+1轮训练；将所述第二模型的第n+1训练集输入至所述第一模型，对所述第一模型进行第n+1轮训练。
基于上述方案，所述方法包括：
确定n是否小于N，N为最大训练轮数；
所述获取第一模型输出的第n+1第一标注信息，以及，获取第二模型输出的第n+1第二标注信息；包括：
若n小于N，获取第一模型输出的第n+1第一标注信息，以及，获取第二模型输出的第n+1第二标注信息。
基于上述方案，所述获取所述训练数据及所述训练数据的初始标注信息，包括：
获取包含有多个分割目标的训练图像及所述分割目标的外接框；
所述基于所述初始标注信息，生成所述第一模型的第一训练集和所述第二模型的第一训练集，包括：
基于所述外接框，在所述外接框内绘制与所述分割目标形状一致的标注轮廓；
基于所述训练数据及所述标注轮廓，生成所述第一模型的第一训练集及所述第二模型的第一训练集。
基于上述方案，所述基于所述初始标注信息，生成所述第一模型的第一训练集和所述第二模型的第一训练集，还包括：
基于所述外接框，生成具有重叠部分的两个所述分割目标的分割边界；
基于所述训练数据及所述分割边界，生成所述第一模型的第一训练集和所述第二模型的第一训练集。
基于上述方案，所述基于所述外接框，在所述外接框内绘制与所述分割目标形状一致的标注轮廓，包括：
基于所述外接框，在所述外接框内绘制与细胞形状一致的所述外接框的内接椭圆。
本申请实施例第二方面提供一种深度学习模型训练装置，包括：
标注模块，配置为获取第一模型输出的第n+1第一标注信息，所述第一模型经过n轮训练；以及，获取第二模型输出的第n+1第二标注信息，所述第二模型已经过n轮训练；n为大于1的整数；
第一生成模块，配置为基于所述训练数据及所述第n+1第一标注信息，生成第二模型的第n+1训练集，并基于所述训练数据及所述第n+1第二标注信息，生成所述第一模型的第n+1训练集；
训练模块，配置为将所述第二模型的第n+1训练集输入至所述第二模型，对所述第二模型进行第n+1轮训练；将所述第一模型的第n+1训练集输入至所述第一模型，对所述第一模型进行第n+1轮训练。
基于上述方案，所述装置包括：
确定模块，配置为确定n是否小于N，N为最大训练轮数；
所述标注模块，配置为若n小于N，获取第一模型输出的第n+1第一标注信息，以及，获取第二模型输出的第n+1第二标注信息。
基于上述方案，所述装置包括：
获取模块，配置为获取所述训练数据及所述训练数据的初始标注信息；
第二生成模块，配置为基于所述初始标注信息，生成所述第一模型的第一训练集和所述第二模型的第一训练集。
基于上述方案，所述获取模块，配置为获取包含有多个分割目标的训练图像及所述分割目标的外接框；
所述第二生成模块，配置为基于所述外接框，在所述外接框内绘制与所述分割目标形状一致的标注轮廓；基于所述训练数据及所述标注轮廓，生成所述第一模型的第一训练集及所述第二模型的第一训练集。
基于上述方案，所述第一生成模块，配置为基于所述外接框，生成具有重叠部分的两个所述分割目标的分割边界；基于所述训练数据及所述分割边界，生成所述第一模型的第一训练集和所述第二模型的第一训练集。
基于上述方案，所述第二生成模块，配置为基于所述外接框，在所述外接框内绘制与细胞形状一致的所述外接框的内接椭圆。
本申请实施例第三方面提供一种计算机存储介质，所述计算机存储介质存储有计算机可执行指令；所述计算机可执行指令；所述计算机可执行指令被执行后，能够实现前述任意一个技术方案提供的深度学习模型训练方法。
本申请实施例第四方面提供一种电子设备，包括：
存储器；
处理器，与所述存储器连接，用于通过执行存储在所述存储器上的计算机可执行指令实现前述任意一个技术方案提供的深度学习模型训练方法。
本申请实施例第五方面提供一种计算机程序产品，所述程序产品包括计算机可执行指令；所述计算机可执行指令被执行后，能够前述任意一个技术方案提供的深度学习模型训练方法。
本申请实施例提供的技术方案，会利用深度学习模型前一轮训练完成之后对训练数据进行标注获得标注信息，该标注信息用作另外一个模型的下一轮训练的训练样本，可以利用非常少的初始人工标注的训练数据进行模型训练，然后利用逐步收敛的第一模型和第二模型识别输出的标注数据作为另一个模型下一轮的训练样本。由于深度学习模型在前一轮训练过程中模型参数会依据大部分标注正确的数据生成，而少量标注不正确或者标注精度低的数据对深度学模型的模型参数影响小，如此反复迭代多次，深度学模型的标注信息会越来越精确。利用越来越精确的标注信息作为训练数据，则会使得深度学习模型的训练结果也越来越好。由于模型利用自身的标注信息构建训练样本，如此，减少了人工手动标注的数据量，减少了人工手动标注所导致的效率低及人工错误，具有模型训练速度快及训练效果好的特点，且采用这种方式训练的深度学习模型，具有分类或识别精确 度高的特点。此外，在本实施例中同时训练至少两个模型，减少了单一模型在学习了一个错误的特征之后通过反复迭代导致最终深度学习模型的学习异常现象。在本实施例中会将一个模型的前一轮训练之后对训练数据进行标注的结果，用于另一个模型的下一轮学习，如此，可以利用两个模型为彼此准备下一轮训练数据减少单一模型反复迭代加强某些错误，从而能够减少模型学习出错的现象，提升深度学习模型的训练效果。
附图说明
图1为本申请实施例提供的第一种深度学习模型训练方法的流程示意图；
图2为本申请实施例提供的第二种深度学习模型训练方法的流程示意图；
图3为本申请实施例提供的第三种深度学习模型训练方法的流程示意图；
图4为本申请实施例提供的一种深度学习模型训练装置的结构示意图；
图5为本申请实施例提供的一种训练集的变化示意图；
图6为本申请实施例提供的一种电子设备的结构示意图。
具体实施方式
以下结合说明书附图及具体实施例对本申请的技术方案做进一步的详细阐述。
如图1所示，本实施例提供一种深度学习模型训练方法，包括：
步骤S110：获取第一模型输出的第n+1第一标注信息，所述第一模型已经过n轮训练；以及，获取第二模型输出的第n+1第二标注信息，所述第二模型已经过n轮训练；n为大于1的整数；
步骤S120：基于所述训练数据及所述第n+1第一标注信息，生成第二模型的第n+1训练集，并基于所述训练数据及所述第n+1第二标注信息，生成所述第一模型的第n+1训练集；
步骤S130：将所述第二模型的第n+1训练集输入至所述第二模型，对所述第二模型进行第n+1轮训练；将所述第一模型的第n+1训练集输入至所述第一模型，对所述第一模型进行第n+1轮训练。
本实施例提供的深度学习模型训练方法可以用于各种电子设备中，例如，各种大数据模型训练的服务器中。
在本申请实施例中所有的第一标注信息和第二标注信息，可包括但不限于对图像的标注信息。该图像可包括医疗图像等。该医疗图像可为平面(2D)医疗图像或者由多个2D图像形成的图像序列构成的立体(3D)医疗图像。
各所述第一标注信息和所述第二标注信息，可为对医疗图像中器官和/会组织的标注，也可以是对细胞内不同细胞结构的标注，如，细胞核的标注。
在本实施例中的步骤S110中，会利用已经完成n轮训练的第一模型对训练数据进行处理，此时第一模型会获得输出，该输出即为所述第n+1第一标注数据，该第n+1第一标注数据与训练数据对应起来，就形成了第二模型的第n+1训练集。
同样地，所述步骤S110还会利用已经完成n轮训练的第二模型对训练数据进行处理，此时第二模型会获得输出，该输出即为所述第n+1第二标注数据，该第n+1第二标注数据与训练数据对应起来，就形成了第一模型的第n+1训练集。
在本申请实施例中，所述第一标注数据均为第一模型对训练数据进行识别或分类得到的标注信息；所述第二标注信息为第二模型对训练数据进行识别或标识得到的标注信息。在本实施中，所述第n+1第一标注数据用于第二模型的第n+1轮训练，而第n+1第二标注数据用于第一模型的第n+1轮训练。
如此，本实施例中第n+1轮对第一模型和第二模型的训练样本就自动生成了，无需用户手动标注第n+1轮训练的训练集，减少了人工手动标注样本所消耗的时间，提升了深度学习模型的训练速率，且减少深度学习模型因为手动标注的不准确或不精确导致的模型训练后的分类或识别结果的 不够精确的现象，提升了深度学习模型训练后的分类或识别结果的精确度。
此外，在本实施例中，第一模型的第一标注数据用于训练第二模型，而第二模型的第二标注数据用于训练第一模型，如此，抑制了第一模型自身的标注数据用于自身下一轮训练导致的模型训练中错误加强的现象，如此，可以提升所述第一模型和第二模型训练效果。
在一些实施例中，所述第一模型和第二模型指代的是两个独立的模型，但是这两个模型可以相同也可以不同。例如，所述第一模型和第二模型可以为同一类深度学习模型，或者为不同类的深度学习模型。
在一些实施例中，所述第一模型和第二模型可为不同网络结构的深度学习模型，例如，所述第一模型为全连接卷积网络(FNN)、第二模型可为普通的卷积神经网络(CNN)。再例如，所述第一模型可为循环神经网络，第二模型可为FNN或CNN。再例如，所述第一模型可为V-NET，所述第二模型可为U-NET等。
若所述第一模型和第二模型不同，则所述第一模型和第二模型在进行训练时基于相同的第一训练集产生的相同错误的概率就大大降低了，可以进一步抑制在反复迭代过程中第一模型和第二模型因为相同的错误进行加强的现象，可以再一次提升训练结果。
在本实施例中完成一轮训练包括：第一模型和第二模型均对各自训练集中的每一个训练样本都完成了至少一次学习。
例如，以所述训练数据为S张图像为例，则第1训练样本可为S张图像及这S张图像的人工标注结果，若S张图像中有一张图像标注图像精确度不够，但是第一模型和第二模型在第一轮训练过程中，由于剩余S-1张图像的标注结构精确度达到预期阈值，则这S-1张图像及其对应的标注数据对第一模型和第二模型的模型参数影像更大。在本实施例中，所述深度学习模型包括但不限于神经网络；所述模型参数包括但不限于：神经网络中网络节点的权值和/或阈值。所述神经网络可为各种类型的神经网络，例如，U-net或V-net。所述神经网络可包括：对训练数据进行特征提取的编码部分和基于提取的特征获取语义信息的解码部分。例如，编码部分可以对图像中分割目标所在区域等进行特征提取，得到区分分割目标和背景的掩码 图像，解码器基于掩码图像可以得到一些语义信息，例如，通过像素统计等方式获得目标的组学特征等，该组学特征可包括：目标的面积、体积、形状等形态特征，和/或，基于灰度值形成的灰度值特征等。所述灰度值特征可包括：直方图的统计特征等。
总之，在本实施例中，经过第一轮训练后的第一模型和第二模型在识别S张图像时，会自动度标注精度不够的哪一张图像，利用从其他S-1张图像上学习获得网络参数来进行标注，而此时标注精度是向其他S-1张图像的标注精度靠齐的，故这一张图像所对应的第2标注信息是会比原始的第1标注信息的精度提升的。如此，构成的第一模型的第2训练集包括：S张图像和第二模型生成的第1标注信息构成的训练数据。如此，第二模型的第2训练集包括：训练数据及第一模型的第1标注信息。若第一模型在第一轮训练时出现了错误A，但是第2轮训练时，使用的是训练数据及第二模型输出的第2标注信息，若第二模型未出现该错误A，则第2标注信息不会受到该错误A的影响，如此，利用第二模型的第2标注信息对第一模型训练进行第二轮训练就能够一直错误A在第一模型中的加强。故在本实施例中，可以利用第一模型和第二模型在训练过程中会基于大多数正确或高精度的标注信息进行学习，逐步抑制初始标注精度不够或不正确的训练样本的负面影响，且因为两个模型的标注数据的交叉用于下一轮训练，不仅能够实现训练样本的人工标注大大的减少，而且还会通过自身迭代的特性逐步提升训练精度，使得训练后的第一模型和第二模型的精确度达到预期效果。
在上述举例中所述训练数据以图像为例，在一些实施例中，所述训练数据还可以图像以外的语音片段、所述图像以外的文本信息等；总之，所述训练数据的形式有多种，不局限于上述任意一种。
在一些实施例中，如图2所示，所述方法包括：
步骤S100：确定n是否小于N，其中，N为最大训练轮数；
所述步骤S110可包括：
若n小于N，利用完成第n轮训练的第一模型对训练数据进行标注，获得第n+1第一标注信息，并利用完成第n轮训练的第二模型对所述训练 数据进行标注，获得第n+1第二标注信息。
在本实施例中在构建第n+1训练集之前，首先会确定目前已训练轮数是否达到预定的最大训练轮数N，若未大达到才生成第n+1标注信息，以构建第一模型和第二模型的第n+1训练集，否则，则确定模型训练完成停止所述深度学习模型的训练。
在一些实施例中，所述N的取值可为4、5、6、7或8等经验值或者统计值。
在一些实施例中，所述N的取值范围可为3到10之间，所述N的取值可以是训练设备从人机交互接口接收的用户输入值。
在还有一些实施例中，确定是否停止训练还可包括：
利用测试集进行所述第一模型和第二模型的测试，若测试结果表明所述第一模型和第二模型的对测试集中测试数据的标注结果的精确度达到特定值，则停止所述第一模型和第二模型的训练，否则进入到所述步骤S110以进入下一轮训练。此时，所述测试集可为精确标注的数据集，故可以用于衡量一个第一模型和第二模型的每一轮的训练结果，以判定是否停止第一模型和第二模型的训练。
在一些实施例中，如图3所示，所述方法包括：
步骤S210：获取所述训练数据及所述训练数据的初始标注信息；
步骤S220：基于所述初始标注信息，生成所述第一模型的第一训练集和所述第二模型的第一训练集。
在本实施例中，所述初始标注信息可为所述训练数据的原始标注信息，该原始标注信息可为人工手动标注的信息，也可以是其他设备标注的信息。例如，具有一定标注能力的其他设备标注的信息。
本实施例中，获取到训练数据及初始标注信息之后，会基于初始标注信息生成第1第一标注信息及第1第二标识信息。此处的第1第一标注信息及第1第一标识信息可直接包括：所述初始标注信息和/或根据所述初始标准信息生成的精细化的标注信息。
例如，若训练数据为图像，图像包含有细胞成像，所述初始标注信息可为大致标注所述细胞成像所在位置的标注信息，而精细化的标注信息可 为精确指示所述细胞所在位置的位置标注，总之，在本实施例中，所述精细化的标注信息对分割对象的标注精确度可高于所述初始标注信息的精确度。
如此，即便由人工进行所述初始标注信息的标注，也降低了人工标注的难度，简化了人工标注。
例如，以细胞成像为例，细胞由于其椭圆球状态的形态，一般在二维平面图像内细胞的外轮廓都呈现为椭圆形。所述初始标注信息可为医生手动绘制的细胞的外接框。所述精细化的标注信息可为：训练设备基于手动标注的外接框生成的内接椭圆。在计算内接椭圆相对于外接框，减少细胞成像中不属于细胞成像的像素个数，故第一标注信息的精确度是高于所述初始标注信息的精确度的。
在一些实施例中，所述步骤S210可包括：获取包含有多个分割目标的训练图像及所述分割目标的外接框；
所述步骤S220可包括：基于所述外接框，在所述外接框内绘制与所述分割目标形状一致的标注轮廓；基于所述训练数据及所述标注轮廓，生成所述第一模型的第一训练集及所述第二模型的第一训练集。
在一些实施例中，所述与分割目标形状一致的标注轮廓可为前述椭圆形，还可以为圆形，或者，三角形或者其他对边形等于分割目标形状一致的形状，不局限于椭圆形。
在一些实施例中，所述标注轮廓为内接于所述外接框的。所述外接框可为矩形框。
在一些实施例中，所述步骤S220还包括：
基于所述外接框，生成具有重叠部分的两个所述分割目标的分割边界；
基于所述训练数据及所述分割边界，生成所述第一模型的第一训练集和所述第二模型的第一训练集。
在一些实施例中，所述基于所述外接框，在所述外接框内绘制与所述分割目标形状一致的标注轮廓，包括：基于所述外接框，在所述外接框内绘制与细胞形状一致的所述外接框的内接椭圆。
在一些图像中，两个分割目标之间会有重叠，在本实施例中所述第一 标注信息还包括：两个重叠分割目标之间的分割边界。
例如，两个细胞成像，细胞成像A叠在细胞成像B上，则细胞成像A被绘制出细胞边界之后和细胞B成像被绘制出细胞边界之后，两个细胞边界交叉形成一部分框出了两个细胞成像之间的交集。在本实施例中，可以根据细胞成像A和细胞成像B之间的位置关系，擦除细胞成像B的细胞边界位于细胞成像A内部的部分，并以细胞成像A的位于细胞成像B中的部分作为所述分割边界。
总之，在本实施例中，所述步骤S220可包括：利用两个分割目标的位置关系，在两者的重叠部分绘制分割边界。
在一些实施例中，在绘制分割边界时，可以通过修正两个具有重叠边界的分割目标其中一个的边界来实现。为了突出边界，可以通过像素膨胀的方式，可以加粗边界。例如，通过细胞成像A的细胞边界在所述重叠部分向细胞成像B方向上扩展预定个像素，例如，1个或多个像素，加粗重叠部分的细胞成像A的边界，从而使得该加粗边界被识别为分割边界。
在一些实施例中，所述基于所述外接框，在所述外接框内绘制与所述分割目标形状一致的标注轮廓，包括：基于所述外接框，在所述外接框内绘制与细胞形状一致的所述外接框的内接椭圆。
在本本实施例中分割目标为细胞成像，所述标注轮廓包括所述细胞形状这一张的外接框的内接椭圆。
在本实施例中，所述第一标注信息包括以下至少之一：
所述细胞成像的细胞边界(对应于所述内接椭圆)；
重叠细胞成像之间的分割边界。
若在一些实施例中，所述分割目标不是细胞而是其他目标，例如，分割目标为集体相中的人脸，人脸的外接框依然可以是矩形框，但是此时人脸的标注边界可能是鹅蛋形脸的边界，圆形脸的边界等，此时，所述形状不局限于所述内接椭圆。
当然以上仅是举例，总之在本实施例中，所述第一模型及第二模型利用另外一个模型前一轮的训练结果输出训练数据的标注信息，以构建下一轮的训练集，通过反复迭代多次完成模型训练，无需手动标注大量的训练 样本，具有训练速率快及通过反复迭代可以提升训练精确度。
如图4所示，本申请实施例提供一种深度学习模型训练装置，包括：
标注模块110，配置为获取第一模型输出的第n+1第一标注信息，所述第一模型经过n轮训练；以及，获取第二模型输出的第n+1第二标注信息，所述第二模型已经过n轮训练；n为大于1的整数；
第一生成模块120，配置为基于所述训练数据及所述第n+1第一标注信息，生成第二模型的第n+1训练集，并基于所述训练数据及所述第n+1第二标注信息，生成所述第一模型的第n+1训练集；
训练模块130，配置为将所述第二模型的第n+1训练集输入至所述第二模型，对所述第二模型进行第n+1轮训练；将第一模型的所述第n+1训练集输入至所述第一模型，对所述第一模型进行第n+1轮训练。
在一些实施例中，所述标注模块110，第一生成模块120及训练模块130可为程序模块，所述程序模块被处理器执行后，能够实现上述操作。
在还有一些实施例中，所述标注模块110，第一生成模块120及训练模块130可为软硬结合模型；所述软硬结合模块可为各种可编程阵列，例如，现场可编程阵列或复杂可编程阵列。
在另外一些实施例中，所述标注模块110，第一生成模块120及训练模块130可纯硬件模块，所述纯硬件模块可为专用集成电路。
在一些实施例中，所述装置包括：
确定模块，配置为确定n是否小于N，其中，N为最大训练轮数；
所述标注模块，配置为若n小于N，获取第一模型输出的第n+1第一标注信息；以及，获取第二模型输出的第n+1第二标注信息。
在一些实施例中，所述装置包括：
获取模块，配置为获取所述训练数据及所述训练数据的初始标注信息；
第二生成模块，配置为基于所述初始标注信息，生成所述第一模型的第一训练集和所述第二模型的第一训练集。
在一些实施例中所述获取模块，配置为获取包含有多个分割目标的训练图像及所述分割目标的外接框；
所述第二生成模块，配置为基于所述外接框，在所述外接框内绘制与 所述分割目标形状一致的标注轮廓；基于所述训练数据及所述标注轮廓，生成所述第一模型的第一训练集及所述第二模型的第一训练集。
在一些实施例中所述第一生成模块，配置为基于所述外接框，生成具有重叠部分的两个所述分割目标的分割边界；基于所述训练数据及所述分割边界，生成所述第一模型的第一训练集和所述第二模型的第一训练集。
在一些实施例中所述第二生成模块，配置为基于所述外接框，在所述外接框内绘制与细胞形状一致的所述外接框的内接椭圆。
以下结合上述实施例提供一个具体示例：
示例1：
互相学习弱监督算法，以图中部分物体的包围矩形框作为输入，进行两个模型互相学习，能够输出其他未知图片中该物体的像素分割结果。
以细胞分割为例子，一开始有图中部分细胞的包围矩形标注。观察发现细胞大部分是椭圆，于是在矩形中画个最大内接椭圆，不同椭圆之间画好分割线，椭圆边缘也画上分割线。作为初始监督信号。训练两个分割模型。然后此分割模型在此图上预测，得到的预测图和初始标注图作并集，作为新的监督信号，两个模型使用彼此的整合结果，再重复训练该分割模型，于是发现图中的分割结果变得越来越好。
同样的使用该方法，对于未知的无标注新图片，第一次两个模型预测一份结果，然后使用彼此的预测重复上述过程。
如图5所示，对原始图像进行标注，第二模型得到一个掩膜图像构建第一模型的第一训练集和第二模型的第一训练集，利用第一训练集分别进行第一模型及第二模型进行第一轮训练。第一轮训练完之后，利用第一模型进行图像识别得到标注信息，基于该标注信息生成第二模型的第二训练集。并在第一轮训练之后，利用第二模型进行图像识别得到标注信息，该标注信息用于生成第一模型的第二训练集。分别进行第一模型和第二模型的第二轮训练；如此反复交叉形成训练集之后，进行迭代训练多轮之后停止训练。
在相关技术中，总是复杂的考虑第一次分割结果的概率图，做峰值、平缓区域等等的分析，然后做区域生长等，对于阅读者而言，复现工作量 大，实现困难。本示例提供的深度学习模型训练方法，不对输出分割概率图做任何计算，直接拿来和标注图做并集，再继续训练模型，这个过程实现简单。
如图6示，本申请实施例提供了一种电子设备，包括：
存储器，用于存储信息；
处理器，与所述存储器连接，用于通过执行存储在所述存储器上的计算机可执行指令，能够实现前述一个或多个技术方案提供的深度学习模型训练方法，例如，如图1至图3所示的方法中的一个或多个。
该存储器可为各种类型的存储器，可为随机存储器、只读存储器、闪存等。所述存储器可用于信息存储，例如，存储计算机可执行指令等。所述计算机可执行指令可为各种程序指令，例如，目标程序指令和/或源程序指令等。
所述处理器可为各种类型的处理器，例如，中央处理器、微处理器、数字信号处理器、可编程阵列、数字信号处理器、专用集成电路或图像处理器等。
所述处理器可以通过总线与所述存储器连接。所述总线可为集成电路总线等。
在一些实施例中，所述终端设备还可包括：通信接口，该通信接口可包括：网络接口、例如，局域网接口、收发天线等。所述通信接口同样与所述处理器连接，能够用于信息收发。
在一些实施例中，所述电子设备还包括摄像头，该摄像头可采集各种图像，例如，医疗影像等。
在一些实施例中，所述终端设备还包括人机交互接口，例如，所述人机交互接口可包括各种输入输出设备，例如，键盘、触摸屏等。
本申请实施例提供了一种计算机存储介质，所述计算机存储介质存储有计算机可执行代码；所述计算机可执行代码被执行后，能够实现前述一个或多个技术方案提供的深度学习模型训练方法，例如，如图1至图3所示的方法中的一个或多个。
所述存储介质包括：移动存储设备、只读存储器(ROM，Read-Only  Memory)、随机存取存储器(RAM，Random Access Memory)、磁碟或者光盘等各种可以存储程序代码的介质。所述存储介质可为非瞬间存储介质。
本申请实施例提供一种计算机程序产品，所述程序产品包括计算机可执行指令；所述计算机可执行指令被执行后，能够实现前述任意实施提供的深度学习模型训练方法，例如，如图1至图3所示的方法中的一个或多个。
在本申请所提供的几个实施例中，应该理解到，所揭露的设备和方法，可以通过其它的方式实现。以上所描述的设备实施例仅仅是示意性的，例如，所述单元的划分，仅仅为一种逻辑功能划分，实际实现时可以有另外的划分方式，如：多个单元或组件可以结合，或可以集成到另一个系统，或一些特征可以忽略，或不执行。另外，所显示或讨论的各组成部分相互之间的耦合、或直接耦合、或通信连接可以是通过一些接口，设备或单元的间接耦合或通信连接，可以是电性的、机械的或其它形式的。
上述作为分离部件说明的单元可以是、或也可以不是物理上分开的，作为单元显示的部件可以是、或也可以不是物理单元，即可以位于一个地方，也可以分布到多个网络单元上；可以根据实际的需要选择其中的部分或全部单元来实现本实施例方案的目的。
另外，在本申请各实施例中的各功能单元可以全部集成在一个处理模块中，也可以是各单元分别单独作为一个单元，也可以两个或两个以上单元集成在一个单元中；上述集成的单元既可以采用硬件的形式实现，也可以采用硬件加软件功能单元的形式实现。
本领域普通技术人员可以理解：实现上述方法实施例的全部或部分步骤可以通过程序指令相关的硬件来完成，前述的程序可以存储于一计算机可读取存储介质中，该程序在执行时，执行包括上述方法实施例的步骤；而前述的存储介质包括：移动存储设备、只读存储器(ROM，Read-Only Memory)、随机存取存储器(RAM，Random Access Memory)、磁碟或者光盘等各种可以存储程序代码的介质。
以上所述，仅为本申请的具体实施方式，但本申请的保护范围并不局限于此，任何熟悉本技术领域的技术人员在本申请揭露的技术范围内，可轻易想到变化或替换，都应涵盖在本申请的保护范围之内。因此，本申请的保护范围应以所述权利要求的保护范围为准。

Claims (15)

  1. 一种深度学习模型训练方法，包括：
    获取第一模型输出的第n+1第一标注信息，所述第一模型经过n轮训练；以及，获取第二模型输出的第n+1第二标注信息，所述第二模型已经过n轮训练；n为大于1的整数；
    基于所述训练数据及所述第n+1第一标注信息，生成第二模型的第n+1训练集，并基于所述训练数据及所述第n+1第二标注信息，生成所述第一模型的第n+1训练集；
    将所述第二模型的第n+1训练集输入至所述第二模型，对所述第二模型进行第n+1轮训练；将所述第一模型的第n+1训练集输入至所述第一模型，对所述第一模型进行第n+1轮训练。
  2. 根据权利要求1所述的方法，其中，所述方法包括：
    确定n是否小于N，N为最大训练轮数；
    所述获取第一模型输出的第n+1第一标注信息，以及，获取第二模型输出的第n+1第二标注信息，包括：
    若n小于N，获取第一模型输出的第n+1第一标注信息，以及，获取第二模型输出的第n+1第二标注信息。
  3. 根据权利要求1或2所述的方法，其中，所述方法包括：
    获取所述训练数据及所述训练数据的初始标注信息；
    基于所述初始标注信息，生成所述第一模型的第一训练集和所述第二模型的第一训练集。
  4. 根据权利要求3所述的方法，其中，
    所述获取所述训练数据及所述训练数据的初始标注信息，包括：
    获取包含有多个分割目标的训练图像及所述分割目标的外接框；
    所述基于所述初始标注信息，生成所述第一模型的第一训练集和所述第二模型的第一训练集，包括：
    基于所述外接框，在所述外接框内绘制与所述分割目标形状一致的标 注轮廓；
    基于所述训练数据及所述标注轮廓，生成所述第一模型的第一训练集及所述第二模型的第一训练集。
  5. 根据权利要求4所述的方法，其中，所述基于所述初始标注信息，生成所述第一模型的第一训练集和所述第二模型的第一训练集，还包括：
    基于所述外接框，生成具有重叠部分的两个所述分割目标的分割边界；
    基于所述训练数据及所述分割边界，生成所述第一模型的第一训练集和所述第二模型的第一训练集。
  6. 根据权利要求4所述的方法，其中，
    所述基于所述外接框，在所述外接框内绘制与所述分割目标形状一致的标注轮廓，包括：
    基于所述外接框，在所述外接框内绘制与细胞形状一致的所述外接框的内接椭圆。
  7. 一种深度学习模型训练装置，其中，包括：
    标注模块，配置为获取第一模型输出的第n+1第一标注信息，所述第一模型经过n轮训练；以及，获取第二模型输出的第n+1第二标注信息，所述第二模型已经过n轮训练；n为大于1的整数；
    第一生成模块，配置为基于所述训练数据及所述第n+1第一标注信息，生成第二模型的第n+1训练集，并基于所述训练数据及所述第n+1第二标注信息，生成所述第一模型的第n+1训练集；
    训练模块，配置为将所述第二模型的第n+1训练集输入至所述第二模型，对所述第二模型进行第n+1轮训练；将所述第一模型的第n+1训练集输入至所述第一模型，对所述第一模型进行第n+1轮训练。
  8. 根据权利要求7所述的装置，其中，所述装置包括：
    确定模块，配置为确定n是否小于N，N为最大训练轮数；
    所述标注模块，配置为若n小于N，获取第一模型输出的第n+1第一标注信息，以及，获取第二模型输出的第n+1第二标注信息。
  9. 根据权利要求7或8所述的装置，其中，所述装置包括：
    获取模块，配置为获取所述训练数据及所述训练数据的初始标注信息；
    第二生成模块，配置为基于所述初始标注信息，生成所述第一模型的第一训练集和所述第二模型的第一训练集。
  10. 根据权利要求9所述的装置，其中，
    所述获取模块，配置为获取包含有多个分割目标的训练图像及所述分割目标的外接框；
    所述第二生成模块，配置为基于所述外接框，在所述外接框内绘制与所述分割目标形状一致的标注轮廓；基于所述训练数据及所述标注轮廓，生成所述第一模型的第一训练集及所述第二模型的第一训练集。
  11. 根据权利要求10所述的装置，其中，所述第一生成模块，配置为基于所述外接框，生成具有重叠部分的两个所述分割目标的分割边界；基于所述训练数据及所述分割边界，生成所述第一模型的第一训练集和所述第二模型的第一训练集。
  12. 根据权利要求10所述的装置，其中，
    所述第二生成模块，配置为基于所述外接框，在所述外接框内绘制与细胞形状一致的所述外接框的内接椭圆。
  13. 一种计算机存储介质，所述计算机存储介质存储有计算机可执行指令；所述计算机可执行指令；所述计算机可执行指令被执行后，能够实现权利要求1至6任一项所述的方法。
  14. 一种电子设备，其中，包括：
    存储器；
    处理器，与所述存储器连接，用于通过执行存储在所述存储器上的计算机可执行指令实现前述权利要求1至6任一项所述的方法。
  15. 一种计算机程序产品，所述程序产品包括计算机可执行指令；所述计算机可执行指令被执行后，能够实现权利要求1至6任一项所述的方法。
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