CN110992303A - Abnormal cell screening method and device, electronic equipment and storage medium - Google Patents
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Abstract
The application relates to a neural network and discloses an abnormal cell screening method, an abnormal cell screening device, an electronic device and a storage medium, wherein the method comprises the following steps: acquiring a cervical cell image; segmenting the cervical cell image based on gray values of pixel points in the cervical cell image to obtain a plurality of cervical cell sub-images; respectively inputting the plurality of cervical cell subimages into an abnormal cell screening model to obtain a plurality of prediction results corresponding to the plurality of cervical cell subimages; selecting a plurality of prediction results from the plurality of prediction results as a plurality of first prediction results according to a preset selection strategy; obtaining a plurality of first cervical cell sub-images corresponding to the plurality of first prediction results; displaying the plurality of first cervical cell sub-images on a display interface. The embodiment of the invention is beneficial to improving the screening efficiency of abnormal cells.
Description
Technical Field
The invention relates to the technical field of computers, in particular to a method and a device for screening abnormal cells, electronic equipment and a storage medium.
Background
Cervical cancer is the most common gynecological malignancy and has a low-age trend in recent years with 50 million cases and 27.4 million deaths worldwide each year, with 85% of cervical cancer deaths occurring in low and moderate income areas with low rates of screening. Cervical cancer is the only cancer which can be found and cured in the early stage at present, so that early screening and diagnosis are key links for preventing and treating cervical cancer.
At present, in general inspection centers and hospitals, doctors are generally required to find abnormal cells from thousands of cells under a microscope and to make a diagnosis based on the abnormal cells. This abnormal cell screening method is inefficient.
Disclosure of Invention
The embodiment of the invention provides a method and a device for screening abnormal cells, electronic equipment and a storage medium, and is beneficial to improving the efficiency of screening the abnormal cells.
The invention provides a method for screening abnormal cells in a first aspect, which comprises the following steps:
acquiring a cervical cell image;
segmenting the cervical cell image based on gray values of pixel points in the cervical cell image to obtain a plurality of cervical cell sub-images;
respectively inputting the plurality of cervical cell subimages into an abnormal cell screening model to obtain a plurality of prediction results corresponding to the plurality of cervical cell subimages, wherein each cervical cell subimage corresponds to one prediction result, each prediction result is used for indicating abnormal cells included in each cervical cell subimage, and the abnormal cells are cells which are diseased or cancerated on the basis of the cervical cells;
selecting a plurality of prediction results from the plurality of prediction results as a plurality of first prediction results according to a preset selection strategy;
obtaining a plurality of first cervical cell sub-images corresponding to the plurality of first prediction results;
displaying the plurality of first cervical cell sub-images on a display interface;
the abnormal cell screening model comprises a plurality of neural networks, the number of the neural networks is equal to the number of the types of the abnormal cells, and the neural networks are used for screening different abnormal cells.
The second aspect of the present invention provides an abnormal cell screening apparatus, comprising:
the first acquisition module is used for acquiring a cervical cell image;
the segmentation module is used for segmenting the cervical cell image based on the gray value of the pixel point in the cervical cell image to obtain a plurality of cervical cell sub-images;
the input module is used for respectively inputting the plurality of cervical cell sub-images into the abnormal cell screening model so as to obtain a plurality of prediction results corresponding to the plurality of cervical cell sub-images, wherein each cervical cell sub-image corresponds to one prediction result, each prediction result is used for indicating abnormal cells included in each cervical cell sub-image, and the abnormal cells are cells which are diseased or cancerized on the basis of the cervical cells;
the selection module is used for selecting a plurality of prediction results from the plurality of prediction results as a plurality of first prediction results according to a preset selection strategy;
a second obtaining module, configured to obtain a plurality of first cervical cell sub-images corresponding to the plurality of first prediction results;
a display module for displaying the plurality of first cervical cell sub-images on a display interface;
the abnormal cell screening model comprises a plurality of neural networks, the number of the neural networks is equal to the number of the types of the abnormal cells, and the neural networks are used for screening different abnormal cells.
A third aspect of the invention provides an electronic device for abnormal cell screening, comprising a processor, a memory, a communication interface, and one or more programs, wherein the one or more programs are stored in the memory and generate instructions for execution by the processor to perform the steps of any of the methods of a method for abnormal cell screening.
A fourth aspect of the present invention provides a computer-readable storage medium for storing a computer program for execution by the processor to perform the method of any one of the abnormal cell screening methods.
It can be seen that, in the above technical scheme, an image of cervical cells is obtained; segmenting the cervical cell image based on gray values of pixel points in the cervical cell image to obtain a plurality of cervical cell sub-images; respectively inputting the plurality of cervical cell subimages into an abnormal cell screening model to obtain a plurality of prediction results corresponding to the plurality of cervical cell subimages, wherein each cervical cell subimage corresponds to one prediction result, each prediction result is used for indicating abnormal cells included in each cervical cell subimage, and the abnormal cells are cells which are diseased or cancerated on the basis of the cervical cells; selecting a plurality of prediction results from the plurality of prediction results as a plurality of first prediction results according to a preset selection strategy; obtaining a plurality of first cervical cell sub-images corresponding to the plurality of first prediction results; displaying the plurality of first cervical cell sub-images on a display interface. By dividing the cervical cell image into a plurality of cervical cell sub-images and respectively inputting the plurality of cervical cell sub-images into the abnormal cell screening model, the condition that the screening process consumes much time due to the fact that one cervical cell image is too large is avoided, and the efficiency of screening the abnormal cells by the abnormal cell screening model is improved. Meanwhile, the abnormal cell screening model comprises a plurality of neural networks, the number of the neural networks is equal to the number of the types of the abnormal cells, the neural networks are used for screening different abnormal cells, various abnormal cells can be screened out, and the practicability is higher. Furthermore, a plurality of first prediction results with the highest prediction probability are screened out, a plurality of first cervical cell sub-images corresponding to the first prediction results are displayed on an interface, so that the doctor can check the images more conveniently, and the workload of the doctor is reduced by displaying the cervical cell sub-images with the highest abnormal cell probability to the doctor.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
Wherein:
FIG. 1A is a schematic flow chart of a method for screening abnormal cells according to an embodiment of the present invention;
fig. 1B is a schematic diagram of a display interface according to an embodiment of the present invention;
FIG. 2A is a schematic flow chart of another abnormal cell screening method according to an embodiment of the present invention;
FIG. 2B is a schematic diagram of a coordinate system according to an embodiment of the present invention;
FIG. 2C is a schematic diagram of a dicing shape according to an embodiment of the present invention;
FIG. 2D is a schematic diagram of a pixel diffusion according to an embodiment of the present invention;
FIG. 3 is a schematic flow chart of another abnormal cell screening method according to an embodiment of the present invention;
FIG. 4 is a schematic view of an abnormal cell screening apparatus according to an embodiment of the present invention;
fig. 5 is a schematic diagram of a server structure of a hardware operating environment according to an embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The following are detailed below.
The terms "first" and "second" in the description and claims of the present invention and the above-described drawings are used for distinguishing between different objects and not for describing a particular order. Furthermore, the terms "include" and "have," as well as any variations thereof, are intended to cover non-exclusive inclusions. For example, a process, method, system, article, or apparatus that comprises a list of steps or elements is not limited to only those steps or elements listed, but may alternatively include other steps or elements not listed, or inherent to such process, method, article, or apparatus.
First, an execution subject of the embodiment of the present application may be, for example, a server, or may be a local data processing device. The server may be, for example, a tablet computer, a notebook computer, a palm top computer, an MID, a desktop computer, or other server devices. And are not limiting in this application.
101. Acquiring a cervical cell image;
optionally, the acquiring an image of a cervical cell includes: acquiring the cervical cell image through a scanning device.
The scanning device may be, for example, a scanner. It is understood that the cervical cell layer detected by the liquid-based thin layer cells is scanned with a scanner to obtain an image of the cervical cells.
Wherein the cervical cell image is an image of a cervical cell layer.
102. Segmenting the cervical cell image based on gray values of pixel points in the cervical cell image to obtain a plurality of cervical cell sub-images;
103. respectively inputting the plurality of cervical cell subimages into an abnormal cell screening model to obtain a plurality of prediction results corresponding to the plurality of cervical cell subimages, wherein each cervical cell subimage corresponds to one prediction result, each prediction result is used for indicating abnormal cells included in each cervical cell subimage, and the abnormal cells are cells which are diseased or cancerated on the basis of the cervical cells;
wherein, the cervical cell is a cell in a normal growth state.
The abnormal cell screening model comprises a plurality of neural networks, the number of the neural networks is equal to the number of the types of the abnormal cells, and the neural networks are used for screening different abnormal cells.
Further, a first neural network is one of the plurality of neural networks, the first neural network is used for screening each cervical cell sub-image in the plurality of cervical cell sub-images for a first abnormal cell, a second neural network is another one of the plurality of neural networks different from the first neural network, and the second neural network is used for screening each cervical cell sub-image in the plurality of cervical cell sub-images for a second abnormal cell.
Wherein the first abnormal cell is different from the second abnormal cell, the first abnormal cell is one of the abnormal cells, and the second abnormal cell is another one of the abnormal cells.
Optionally, the abnormal cells include at least one of: squamous carcinoma (SCC), high-grade squamous epithelial lesions (LSIL), Atypical squamous cells that cannot exclude high-grade squamous intraepithelial lesions (carcinoma cells, canot high-grade squamous intraepithelial lesions, ascil), low-grade squamous epithelial lesions (LSIL), Atypical squamous cells of indefinite significance (ascil), Atypical squamous cells of indefinite significance (ASC-US), Adenocarcinoma (AC), Atypical Adenocarcinoma (AGC), and the like.
It is understood that a plurality of neural networks are respectively used for screening each cervical cell sub-image of the plurality of cervical cell sub-images for an abnormal cell. Specifically, a neural network of the plurality of neural networks is used to screen each cervical cell sub-image of the plurality of cervical cell sub-images for squamous carcinoma, and another neural network of the plurality of neural networks is used to screen each cervical cell sub-image of the plurality of cervical cell sub-images for adenocarcinoma.
104. Selecting a plurality of prediction results from the plurality of prediction results as a plurality of first prediction results according to a preset selection strategy;
the preset selection strategy is determined according to a prediction selection operation, and the prediction selection operation comprises the following steps: obtaining a prediction probability corresponding to each prediction result in the plurality of prediction results; numbering the prediction probabilities corresponding to each prediction result in the plurality of prediction results according to the sequence of the prediction probabilities from large to small to obtain a plurality of numbers; selecting a part number from the plurality of numbers, wherein the part number is at least one number which is larger than a preset number from the plurality of numbers; and taking the part prediction probabilities corresponding to the part numbers one by one as the preset selection strategy.
Wherein the preset number is set by an administrator. For example, if the plurality of numbers are 10 numbers from 10-1, the predetermined number is 6, and the number of the part is 3 numbers from 7-10.
Further, the selecting a plurality of prediction results from the plurality of prediction results as a plurality of first prediction results according to a preset selection strategy includes: and selecting a plurality of predicted results from the plurality of predicted results as a plurality of first predicted results according to the partial prediction probability.
105. Obtaining a plurality of first cervical cell sub-images corresponding to the plurality of first prediction results;
106. displaying the plurality of first cervical cell sub-images on a display interface.
Referring to fig. 1B, fig. 1B is a schematic diagram of a display interface provided by an embodiment of the present invention, in which each rectangle with oblique lines represents a first cervical cell subimage. It can be seen that a plurality of first cervical cell subimages are displayed on the display interface.
It can be seen that, in the above technical scheme, an image of cervical cells is obtained; segmenting the cervical cell image based on gray values of pixel points in the cervical cell image to obtain a plurality of cervical cell sub-images; respectively inputting the plurality of cervical cell subimages into an abnormal cell screening model to obtain a plurality of prediction results corresponding to the plurality of cervical cell subimages, wherein each cervical cell subimage corresponds to one prediction result, each prediction result is used for indicating abnormal cells included in each cervical cell subimage, and the abnormal cells are cells which are diseased or cancerated on the basis of the cervical cells; selecting a plurality of prediction results from the plurality of prediction results as a plurality of first prediction results according to a preset selection strategy; obtaining a plurality of first cervical cell sub-images corresponding to the plurality of first prediction results; displaying the plurality of first cervical cell sub-images on a display interface. By dividing the cervical cell image into a plurality of cervical cell sub-images and respectively inputting the plurality of cervical cell sub-images into the abnormal cell screening model, the condition that the screening process consumes much time due to the fact that one cervical cell image is too large is avoided, and the efficiency of screening the abnormal cells by the abnormal cell screening model is improved. Meanwhile, the abnormal cell screening model comprises a plurality of neural networks, the number of the neural networks is equal to the number of the types of the abnormal cells, the neural networks are used for screening different abnormal cells, various abnormal cells can be screened out, and the practicability is higher. Furthermore, a plurality of first prediction results with the highest prediction probability are screened out, a plurality of first cervical cell sub-images corresponding to the first prediction results are displayed on an interface, so that the doctor can check the images more conveniently, and the workload of the doctor is reduced by displaying the cervical cell sub-images with the highest abnormal cell probability to the doctor.
The following specifically exemplifies the process of segmenting the cervical cell image based on the gray-scale values of the pixel points in the cervical cell image to obtain a plurality of cervical cell sub-images.
Referring to fig. 2A, fig. 2A is a schematic flow chart of a method for screening abnormal cells according to an embodiment of the present invention. As shown in fig. 2A, an abnormal cell screening method according to an embodiment of the present invention may include:
201. acquiring a gray value corresponding to each pixel point in the cervical cell image;
wherein the gray scale value ranges from 0 to 255.
When abnormal cells are included in the cervical cell image, the abnormal cells are different, and the corresponding gray values are also different. For example, when the cervical cell image includes a squamous carcinoma and a high-grade squamous epithelial lesion, the grayscale value corresponding to the squamous carcinoma is different from the grayscale value corresponding to the high-grade squamous epithelial lesion.
202. Determining a pixel point with the minimum gray value according to the gray value corresponding to each pixel point in the cervical cell image;
203. selecting any one pixel point from the pixel points as a coordinate origin for segmenting the cervical cell image;
204. establishing a coordinate system on the cervical cell image based on the origin of coordinates, wherein the coordinate system takes the positive transverse direction of the cervical cell image as an x-axis and the positive longitudinal direction of the cervical cell image as a y-axis;
referring to fig. 2B, fig. 2B is a schematic diagram of a coordinate system according to an embodiment of the present invention, it can be seen that the positive direction of the x-axis of the coordinate system is the rightward direction, i.e. the positive lateral direction of the cervical cell image; the positive direction of the y axis of the coordinate system is an upward direction, namely the positive longitudinal direction of the cervical cell image; the coordinate system is established on the cervical cell image based on the origin of coordinates.
205. Segmenting the cervical cell image starting from the origin of coordinates to obtain the plurality of cervical cell sub-images.
Optionally, in a possible embodiment, the segmenting the cervical cell image from the origin of coordinates to obtain the plurality of cervical cell sub-images includes: determining a plurality of gray level difference values according to the gray level value corresponding to each pixel point in the cervical cell image, wherein each gray level difference value is the difference value of each pixel point and the corresponding adjacent pixel point in the gray level value; dividing the gray difference values falling into the same gray value interval into a group to obtain a plurality of gray groups; determining a plurality of segmentation shapes corresponding to the cervical cell image from the origin of coordinates according to the gray groups; normalizing irregular shapes in the plurality of segmentation shapes to obtain a plurality of regular first segmentation shapes; setting the plurality of first tangent shapes as the plurality of cervical cell subimages.
Optionally, in a possible implementation manner, the determining, by the first pixel point, a plurality of gray level differences according to a gray level corresponding to each pixel point in the cervical cell image includes: acquiring at least one gray value corresponding to at least one pixel point adjacent to the first pixel point; and respectively determining a gray difference value between the gray value corresponding to the first pixel point and each gray value in the at least one gray value to obtain at least one gray difference value.
For example, the cervical cell image includes 9 pixel points, the 9 pixel points are arranged in a square, and when the pixel point located at the center of the square is the first pixel point, at least one pixel point adjacent to the first pixel point includes: adjacent pixel points above the first pixel point, adjacent pixel points below the first pixel point, adjacent pixel points to the left of the first pixel point and adjacent pixel points to the right of the first pixel point; when the pixel point at the top right corner vertex of the square is the first pixel point, at least one pixel point adjacent to the first pixel point comprises: the adjacent pixel point below the first pixel point, the adjacent pixel point at the left of the first pixel point and the adjacent pixel point at the right of the first pixel point.
Further, at least one pixel point adjacent to the first pixel point at least includes one of the following: the pixel structure comprises adjacent pixel points above the first pixel point, adjacent pixel points below the first pixel point, adjacent pixel points at the left of the first pixel point and adjacent pixel points at the right of the first pixel point.
It can be seen that, in the above technical solution, at least one gray value corresponding to at least one pixel point adjacent to the first pixel point is obtained; and respectively determining a gray difference value between the gray value corresponding to the first pixel point and each gray value in the at least one gray value to obtain at least one gray difference value, so as to determine the difference value of the first pixel point and the adjacent pixel point corresponding to the first pixel point on the gray value, and prepare for grouping according to the same gray value interval in which the gray difference value falls.
It will be appreciated that when determining a plurality of segmentation shapes corresponding to the cervical cell image based on the gray sets from the origin of coordinates, it is highly likely that the segmentation shapes are not regular shapes. Wherein the regular shape includes, for example: rectangular, square, etc. Therefore, it is necessary to perform normalization processing on the irregular shapes in the plurality of segmentation shapes to obtain a plurality of regular first segmentation shapes, so that the abnormal cell screening model can screen the abnormal cells more quickly.
Wherein the irregular shape is a shape excluding a rectangle and a square, and the normalization process includes the operations of: determining a plurality of second pixel points corresponding to each of the plurality of segmentation shapes, wherein the gray value corresponding to each second pixel point is a first gray value; and presetting a plurality of second pixel points corresponding to each of the plurality of segmentation shapes according to a first sequence.
Wherein the first order comprises one of: an order of a center point of each of the plurality of sliced shapes from near to far from the origin of coordinates and an order of a center point of each of the plurality of sliced shapes from far to near from the origin of coordinates.
Further, the preset processing comprises at least one of the following processing: removing redundant second pixel points in each segmentation shape; collecting redundant second pixel points in the plurality of segmentation shapes to obtain a plurality of third pixel points, and filling the segmentation shapes needing to be filled in the plurality of segmentation shapes by adopting the plurality of third pixel points; and rearranging a plurality of second pixel points corresponding to each segmentation shape. The redundant second pixel points are determined according to the shape attribute corresponding to each segmentation shape, and the shape attribute corresponding to each segmentation shape is determined according to the process that each segmentation shape tends to be a regular shape.
For example, a certain segmentation shape of the plurality of segmentation shapes is a triangle, see fig. 2C, where fig. 2C is a schematic diagram of a segmentation shape provided in the embodiment of the present invention, where the uppermost legend is to directly remove redundant second pixel points in the triangle, so as to obtain a rectangle, that is, a regular first segmentation shape; the middle legend is that redundant second pixel points in the triangle are rearranged, redundant second pixel points in other segmentation shapes are adopted to fill the arranged triangle, and finally, a formed rectangle is the regular first segmentation shape; the last legend is a rectangle obtained by rearranging redundant second pixel points in the triangle, namely a regular first segmentation shape.
Wherein the first gray value is 0 or 255.
Optionally, in the above technical scheme, a plurality of gray level difference values are determined according to a gray level value corresponding to each pixel point in the cervical cell image, and each gray level difference value is a difference value of a gray level value between each pixel point and an adjacent pixel point corresponding to each pixel point; dividing the gray difference values falling into the same gray value interval into a group to obtain a plurality of gray groups; determining a plurality of segmentation shapes corresponding to the cervical cell image from the origin of coordinates according to the gray groups; normalizing irregular shapes in the plurality of segmentation shapes to obtain a plurality of regular first segmentation shapes; the plurality of first segmentation shapes are set as the plurality of cervical cell subimages, so that a plurality of segmentation shapes corresponding to the cervical cell image are determined from the origin of coordinates according to the gray level group, each segmentation shape is formed by pixel points falling into the same gray level interval in the gray level difference value, the abnormal cells are screened out by the abnormal cell screening model more quickly, and the efficiency of segmenting the image is improved. Meanwhile, irregular shapes in the plurality of segmentation shapes are subjected to normalized processing, input data which are more suitable for an abnormal cell screening model are constructed, and screening efficiency is improved.
It can be seen that, in one possible embodiment, the segmenting the cervical cell image based on the gray-scale values of the pixel points in the cervical cell image to obtain the plurality of cervical cell sub-images includes: acquiring a gray value corresponding to each pixel point in the cervical cell image; determining a plurality of groups of pixel points with the same gray value in the cervical cell image according to the gray value corresponding to each pixel point in the cervical cell image, wherein each group of pixel points comprises at least one pixel point, and the gray values corresponding to each pixel point in the at least one pixel point are the same; segmenting the cervical cell image according to the multiple groups of pixel points to obtain the multiple cervical cell sub-images, wherein the multiple groups of pixel points correspond to the multiple cervical cell sub-images.
It can be seen that, in the above technical solution, a gray value corresponding to each pixel point in the cervical cell image is obtained; determining a plurality of groups of pixel points with the same gray value in the cervical cell image according to the gray value corresponding to each pixel point in the cervical cell image, wherein each group of pixel points comprises at least one pixel point, and the gray values corresponding to each pixel point in the at least one pixel point are the same; and segmenting the cervical cell image according to the multiple groups of pixel points to obtain multiple cervical cell sub-images, wherein the multiple groups of pixel points correspond to the multiple cervical cell sub-images, so that the cervical cell image is segmented according to the same gray value, and preparation is made for screening abnormal cells more quickly by a subsequent abnormal cell screening model.
Optionally, in a possible implementation, the segmenting the cervical cell image according to the plurality of groups of pixel points to obtain the plurality of cervical cell sub-images includes: segmenting the cervical cell image according to the multiple groups of pixel points to obtain a plurality of second cervical cell sub-images, wherein the multiple groups of pixel points correspond to the plurality of second cervical cell sub-images; determining at least one third cervical cell sub-image with irregular outer contour among the plurality of second cervical cell sub-images; performing the following operations for each of the at least one third cervical cell sub-image to obtain the plurality of cervical cell sub-images, including: determining the outer contour size corresponding to the currently processed third cervical cell subimage; acquiring a template image according to the outer contour size corresponding to the currently processed third cervical cell subimage so as to obtain the outer contour size corresponding to the template image; and performing pixel diffusion on the currently processed third cervical cell subimage according to the outer contour size corresponding to the template image, and stopping the pixel diffusion until the outer contour size corresponding to the currently processed third cervical cell subimage is the same as the outer contour size corresponding to the template image, wherein the pixel diffusion is performed by adopting a first gray value.
Wherein the outer contour is irregular to a contour excluding the outer contour being rectangular and square. Wherein the template image comprises one of: rectangular and square.
Wherein the first gray value is 0 or 255.
For example, if a second cervical cell sub-image of the plurality of second cervical cell sub-images is a triangle, the outer contour of the second cervical cell sub-image is a triangle, and further, the outer contour of the second cervical cell sub-image is irregular. It can be understood that, referring to fig. 2D, fig. 2D is a schematic diagram of pixel diffusion according to an embodiment of the present invention, when the template image corresponding to the second cervical cell subimage is a rectangle, the triangle is pixel diffused according to the outline size corresponding to the rectangle, and finally, the cervical cell subimage corresponding to the second cervical cell subimage is a rectangle.
It can be seen that, in the above technical solution, the cervical cell image is segmented according to the plurality of groups of pixel points to obtain a plurality of second cervical cell sub-images, and the plurality of groups of pixel points correspond to the plurality of second cervical cell sub-images; determining at least one third cervical cell sub-image with irregular outer contour among the plurality of second cervical cell sub-images; performing the following operations for each of the at least one third cervical cell sub-image to obtain the plurality of cervical cell sub-images, including: determining the outer contour size corresponding to the currently processed third cervical cell subimage; acquiring a template image according to the outer contour size corresponding to the currently processed third cervical cell subimage so as to obtain the outer contour size corresponding to the template image; and performing pixel diffusion on the currently processed third cervical cell subimage according to the outer contour dimension corresponding to the template image, stopping the pixel diffusion until the outer contour dimension corresponding to the currently processed third cervical cell subimage is the same as the outer contour dimension corresponding to the template image, performing pixel diffusion by adopting a first gray value, and performing pixel diffusion on at least one third cervical cell subimage with irregular outer contour to enable all cervical cell subimages to be rectangular or square images so as to prepare for quickly screening abnormal cells in a subsequent abnormal cell screening model.
Referring to fig. 3, fig. 3 is a schematic flow chart of a method for screening abnormal cells according to another embodiment of the present invention. Before the plurality of cervical cell sub-images are respectively input into the abnormal cell screening model to obtain a plurality of prediction results corresponding to the plurality of cervical cell sub-images, as shown in fig. 3, the method further includes:
301. acquiring a training set, wherein the training set comprises a plurality of training subsets, the plurality of training subsets correspond to the plurality of neural networks, each training subset comprises a plurality of cervical cell sub-image sets with different brightness levels, each cervical cell sub-image set comprises a plurality of second cervical cell sub-images with one brightness level, and each second cervical cell sub-image in the plurality of second cervical cell sub-images comprises different abnormal cells;
optionally, in a possible implementation, the acquiring a training set includes: displaying the plurality of third cervical cell sub-images on a labeling interface; when a marking operation for a plurality of positions on the marking interface is detected, marking the plurality of third cervical cell sub-images corresponding to the plurality of positions to obtain a plurality of fourth cervical cell sub-images corresponding to the plurality of third cervical cell sub-images, wherein each fourth cervical cell sub-image is an image obtained after marking each third cervical cell sub-image; processing the fourth cervical cell subimages respectively by using a plurality of preset brightnesses to obtain a plurality of cervical cell subimages sets included in each of a plurality of training subsets, wherein the preset brightnesses correspond to the plurality of cervical cell subimages sets; setting the plurality of cervical cell subimages included in each of the plurality of training subsets as the training set.
Wherein the marker interface includes a plurality of marker display regions corresponding to the plurality of third cervical cell subimages. The displaying a plurality of third cervical cell subimages on the labeling interface includes: displaying the plurality of third cervical cell sub-images in the plurality of marker display areas on a marker interface. Further, a person with medical knowledge may view and mark a plurality of third cervical cell subimages in a plurality of mark display areas.
Optionally, in a possible embodiment, when a marking operation for a plurality of positions on the marking interface is detected, marking the plurality of third cervical cell sub-images corresponding to the plurality of positions to obtain a plurality of fourth cervical cell sub-images corresponding to the plurality of third cervical cell sub-images includes: tracking a plurality of marking trajectories on the plurality of third cervical cell sub-images corresponding to a plurality of positions when a marking operation for the plurality of positions on the marking interface is detected; acquiring a plurality of marking areas formed by the plurality of marking tracks; generating a plurality of tag labels from the plurality of tag regions; generating the plurality of fourth cervical cell sub-images including the plurality of labeling labels from the plurality of third cervical cell sub-images corresponding to the plurality of positions.
When the marking operation of the first position on the marking interface is detected, tracking a marking track on a third cervical cell subimage corresponding to the first position; acquiring a mark area formed by the mark track; generating a marking label according to the marking area; and generating a fourth cervical cell subimage comprising the marking label according to the third cervical cell subimage corresponding to the first position.
Further, the plurality of third cervical cell sub-images correspond to the plurality of labeling tracks corresponding to the plurality of labeling regions corresponding to the plurality of labeling labels.
It can be seen that, in the above technical solution, when a marking operation is detected for a plurality of positions on the marking interface, a plurality of marking trajectories on the plurality of third cervical cell sub-images corresponding to the plurality of positions are tracked; acquiring a plurality of marking areas formed by the plurality of marking tracks; generating a plurality of tag labels from the plurality of tag regions; and generating a plurality of fourth cervical cell subimages comprising a plurality of marking labels according to the plurality of third cervical cell subimages corresponding to the plurality of positions, so that when the marking tracks are different, the marking areas are different, the marking labels are different, the uniqueness of the marking labels of the fourth cervical cell subimages is improved, and the model training efficiency is improved.
It can be seen that, in the above technical solution, a plurality of third cervical cell subimages are displayed on the marking interface; when a marking operation for a plurality of positions on the marking interface is detected, marking the plurality of third cervical cell sub-images corresponding to the plurality of positions to obtain a plurality of fourth cervical cell sub-images corresponding to the plurality of third cervical cell sub-images, wherein each fourth cervical cell sub-image is an image obtained after marking each third cervical cell sub-image; processing the fourth cervical cell subimages respectively by using a plurality of preset brightnesses to obtain a plurality of cervical cell subimages sets included in each of a plurality of training subsets, wherein the preset brightnesses correspond to the plurality of cervical cell subimages sets; and setting the plurality of cervical cell sub-image sets included by each training subset in the plurality of training subsets as the training set, so that after a plurality of third cervical cell sub-images are marked on a marking interface, brightness adjustment is performed on the marked third cervical cell sub-images by adopting a plurality of preset brightnesses to obtain the training set, and preparation is made for a subsequent abnormal cell screening model to identify cervical cell images shot under different ambient lights.
302. Constructing a plurality of neural networks to be trained, wherein the plurality of neural networks to be trained correspond to the plurality of neural networks;
the neural networks to be trained run on a plurality of processes respectively, and the plurality of processes correspond to the plurality of neural networks to be trained.
303. And training the training set based on the plurality of neural networks to be trained to obtain the abnormal cell screening model.
It can be seen that in the above technical solution, by obtaining a training set, the training set includes a plurality of training subsets, the plurality of training subsets correspond to the plurality of neural networks, each training subset includes a plurality of cervical cell sub-image sets with different light and shade levels, each cervical cell sub-image set includes a plurality of second cervical cell sub-images with one light and shade level, and each second cervical cell sub-image in the plurality of second cervical cell sub-images includes different abnormal cells; constructing a plurality of neural networks to be trained, wherein the plurality of neural networks to be trained correspond to the plurality of neural networks; training the training set based on the plurality of neural networks to be trained to obtain the abnormal cell screening model, so that the abnormal cell screening model can identify cervical cell images shot under different ambient light, the abnormal cell screening model is optimized, and the practicability of the abnormal cell screening model is enhanced.
Referring to fig. 4, fig. 4 is a schematic view of an abnormal cell screening apparatus according to an embodiment of the present invention. As shown in fig. 4, an abnormal cell screening apparatus 400 according to an embodiment of the present invention may include:
a first acquisition module 401 for acquiring an image of cervical cells;
optionally, a first acquiring module 401 is configured to acquire the cervical cell image through a scanning device.
The scanning device may be, for example, a scanner. It is understood that the cervical cell layer detected by the liquid-based thin layer cells is scanned with a scanner to obtain an image of the cervical cells.
Wherein the cervical cell image is an image of a cervical cell layer.
A segmentation module 402, configured to segment the cervical cell image based on gray values of pixel points in the cervical cell image to obtain a plurality of cervical cell sub-images;
optionally, the segmentation module 402 is specifically configured to obtain a gray value corresponding to each pixel point in the cervical cell image; determining a pixel point with the minimum gray value according to the gray value corresponding to each pixel point in the cervical cell image; selecting any one pixel point from the pixel points as a coordinate origin for segmenting the cervical cell image; establishing a coordinate system on the cervical cell image based on the origin of coordinates, wherein the coordinate system takes the positive transverse direction of the cervical cell image as an x-axis and the positive longitudinal direction of the cervical cell image as a y-axis; segmenting the cervical cell image starting from the origin of coordinates to obtain the plurality of cervical cell sub-images.
Optionally, the segmentation module 402 is specifically configured to determine a plurality of gray scale difference values according to a gray scale value corresponding to each pixel point in the cervical cell image, where each gray scale difference value is a difference value of each pixel point and a corresponding adjacent pixel point in the gray scale value; dividing the gray difference values falling into the same gray value interval into a group to obtain a plurality of gray groups; determining a plurality of segmentation shapes corresponding to the cervical cell image from the origin of coordinates according to the gray groups; normalizing irregular shapes in the plurality of segmentation shapes to obtain a plurality of regular first segmentation shapes; setting the plurality of first tangent shapes as the plurality of cervical cell subimages.
Optionally, the segmentation module 402 is specifically configured to obtain a gray value corresponding to each pixel point in the cervical cell image; determining a plurality of groups of pixel points with the same gray value in the cervical cell image according to the gray value corresponding to each pixel point in the cervical cell image, wherein each group of pixel points comprises at least one pixel point, and the gray values corresponding to each pixel point in the at least one pixel point are the same; segmenting the cervical cell image according to the multiple groups of pixel points to obtain the multiple cervical cell sub-images, wherein the multiple groups of pixel points correspond to the multiple cervical cell sub-images.
Optionally, the segmenting module 402 is specifically configured to segment the cervical cell image according to the plurality of groups of pixel points to obtain a plurality of second cervical cell sub-images, where the plurality of groups of pixel points correspond to the plurality of second cervical cell sub-images; determining at least one third cervical cell sub-image with irregular outer contour among the plurality of second cervical cell sub-images; performing the following operations for each of the at least one third cervical cell sub-image to obtain the plurality of cervical cell sub-images, including: determining the outer contour size corresponding to the currently processed third cervical cell subimage; acquiring a template image according to the outer contour size corresponding to the currently processed third cervical cell subimage so as to obtain the outer contour size corresponding to the template image; and performing pixel diffusion on the currently processed third cervical cell subimage according to the outer contour size corresponding to the template image, and stopping the pixel diffusion until the outer contour size corresponding to the currently processed third cervical cell subimage is the same as the outer contour size corresponding to the template image, wherein the pixel diffusion is performed by adopting a first gray value.
An input module 403, configured to input the multiple cervical cell sub-images into an abnormal cell screening model respectively, so as to obtain multiple prediction results corresponding to the multiple cervical cell sub-images, where each cervical cell sub-image corresponds to one prediction result, and each prediction result is used to indicate an abnormal cell included in each cervical cell sub-image, where the abnormal cell is a cell that is diseased or cancerated on the basis of a cervical cell;
wherein, the cervical cell is a cell in a normal growth state.
The abnormal cell screening model comprises a plurality of neural networks, the number of the neural networks is equal to the number of the types of the abnormal cells, and the neural networks are used for screening different abnormal cells.
Further, a first neural network is one of the plurality of neural networks, the first neural network is used for screening each cervical cell sub-image in the plurality of cervical cell sub-images for a first abnormal cell, a second neural network is another one of the plurality of neural networks different from the first neural network, and the second neural network is used for screening each cervical cell sub-image in the plurality of cervical cell sub-images for a second abnormal cell.
Wherein the first abnormal cell is different from the second abnormal cell, the first abnormal cell is one of the abnormal cells, and the second abnormal cell is another one of the abnormal cells.
Optionally, the abnormal cells include at least one of: squamous carcinoma (SCC), high-grade squamous epithelial lesions (LSIL), Atypical squamous cells that cannot exclude high-grade squamous intraepithelial lesions (carcinoma cells, canot high-grade squamous intraepithelial lesions, ascil), low-grade squamous epithelial lesions (LSIL), Atypical squamous cells of indefinite significance (ascil), Atypical squamous cells of indefinite significance (ASC-US), Adenocarcinoma (AC), Atypical Adenocarcinoma (AGC), and the like.
It is understood that a plurality of neural networks are respectively used for screening each cervical cell sub-image of the plurality of cervical cell sub-images for an abnormal cell. Specifically, a neural network of the plurality of neural networks is used to screen each cervical cell sub-image of the plurality of cervical cell sub-images for squamous carcinoma, and another neural network of the plurality of neural networks is used to screen each cervical cell sub-image of the plurality of cervical cell sub-images for adenocarcinoma.
Optionally, before the plurality of cervical cell sub-images are respectively input into the abnormal cell screening model to obtain a plurality of prediction results corresponding to the plurality of cervical cell sub-images, the abnormal cell screening apparatus further includes a processing module, where the processing module is configured to acquire a training set, the training set includes a plurality of training subsets, the plurality of training subsets correspond to the plurality of neural networks, each training subset includes a plurality of cervical cell sub-image sets with different light and shade degrees, each cervical cell sub-image set includes a plurality of second cervical cell sub-images with one light and shade degree, and each second cervical cell sub-image in the plurality of second cervical cell sub-images includes different abnormal cells; constructing a plurality of neural networks to be trained, wherein the plurality of neural networks to be trained correspond to the plurality of neural networks; and training the training set based on the plurality of neural networks to be trained to obtain the abnormal cell screening model.
Optionally, the processing module is specifically configured to display the plurality of third cervical cell subimages on a labeling interface; when a marking operation for a plurality of positions on the marking interface is detected, marking the plurality of third cervical cell sub-images corresponding to the plurality of positions to obtain a plurality of fourth cervical cell sub-images corresponding to the plurality of third cervical cell sub-images, wherein each fourth cervical cell sub-image is an image obtained after marking each third cervical cell sub-image; processing the fourth cervical cell subimages respectively by using a plurality of preset brightnesses to obtain a plurality of cervical cell subimages sets included in each of a plurality of training subsets, wherein the preset brightnesses correspond to the plurality of cervical cell subimages sets; setting the plurality of cervical cell subimages included in each of the plurality of training subsets as the training set.
A selecting module 404, configured to select multiple prediction results from the multiple prediction results according to a preset selecting strategy as multiple first prediction results;
the preset selection strategy is determined according to a prediction selection operation, and the prediction selection operation comprises the following steps: obtaining a prediction probability corresponding to each prediction result in the plurality of prediction results; numbering the prediction probabilities corresponding to each prediction result in the plurality of prediction results according to the sequence of the prediction probabilities from large to small to obtain a plurality of numbers; selecting a part number from the plurality of numbers, wherein the part number is at least one number which is larger than a preset number from the plurality of numbers; and taking the part prediction probabilities corresponding to the part numbers one by one as the preset selection strategy.
Wherein the preset number is set by an administrator. For example, if the plurality of numbers are 10 numbers from 10-1, the predetermined number is 6, and the number of the part is 3 numbers from 7-10.
Further, the selecting a plurality of prediction results from the plurality of prediction results as a plurality of first prediction results according to a preset selection strategy includes: and selecting a plurality of predicted results from the plurality of predicted results as a plurality of first predicted results according to the partial prediction probability.
A second obtaining module 405, configured to obtain a plurality of first cervical cell sub-images corresponding to the plurality of first prediction results;
a display module 406 for displaying the plurality of first cervical cell sub-images on a display interface;
referring to fig. 5, fig. 5 is a schematic diagram of a server structure of a hardware operating environment according to an embodiment of the present application.
The embodiment of the invention provides an information push electronic device, which comprises a processor, a memory, a communication interface and one or more programs, wherein the one or more programs are stored in the memory and are configured to be executed by the processor to execute instructions comprising the steps of any information push method. As shown in fig. 5, a server of a hardware operating environment according to an embodiment of the present application may include:
a processor 501, such as a CPU.
The memory 502 may alternatively be a high speed RAM memory or a stable memory such as a disk memory.
A communication interface 503 for implementing connection communication between the processor 501 and the memory 502.
Those skilled in the art will appreciate that the configuration of the server shown in fig. 5 is not intended to be limiting and may include more or fewer components than those shown, or some components may be combined, or a different arrangement of components.
As shown in fig. 5, the memory 502 may include an operating system, a network communication module, and a program for data processing. The operating system is a program that manages and controls server hardware and software resources, a program that supports personnel management, and the execution of other software or programs. The network communication module is used to implement communication between the components in the memory 502 and with other hardware and software in the server.
In the server shown in fig. 5, a processor 501 is used to execute a program for personnel management stored in a memory 502, and implements the following steps:
acquiring a cervical cell image;
segmenting the cervical cell image based on gray values of pixel points in the cervical cell image to obtain a plurality of cervical cell sub-images;
respectively inputting the plurality of cervical cell subimages into an abnormal cell screening model to obtain a plurality of prediction results corresponding to the plurality of cervical cell subimages, wherein each cervical cell subimage corresponds to one prediction result, each prediction result is used for indicating abnormal cells included in each cervical cell subimage, and the abnormal cells are cells which are diseased or cancerated on the basis of the cervical cells;
selecting a plurality of prediction results from the plurality of prediction results as a plurality of first prediction results according to a preset selection strategy;
obtaining a plurality of first cervical cell sub-images corresponding to the plurality of first prediction results;
displaying the plurality of first cervical cell sub-images on a display interface;
the abnormal cell screening model comprises a plurality of neural networks, the number of the neural networks is equal to the number of the types of the abnormal cells, and the neural networks are used for screening different abnormal cells. For specific implementation of the server according to the present application, reference may be made to the above embodiments of the abnormal cell screening method, which are not described herein again.
The present application further provides a computer readable storage medium for storing a computer program, the stored computer program being executable by the processor to perform the steps of:
acquiring a cervical cell image;
segmenting the cervical cell image based on gray values of pixel points in the cervical cell image to obtain a plurality of cervical cell sub-images;
respectively inputting the plurality of cervical cell subimages into an abnormal cell screening model to obtain a plurality of prediction results corresponding to the plurality of cervical cell subimages, wherein each cervical cell subimage corresponds to one prediction result, each prediction result is used for indicating abnormal cells included in each cervical cell subimage, and the abnormal cells are cells which are diseased or cancerated on the basis of the cervical cells;
selecting a plurality of prediction results from the plurality of prediction results as a plurality of first prediction results according to a preset selection strategy;
obtaining a plurality of first cervical cell sub-images corresponding to the plurality of first prediction results;
displaying the plurality of first cervical cell sub-images on a display interface;
the abnormal cell screening model comprises a plurality of neural networks, the number of the neural networks is equal to the number of the types of the abnormal cells, and the neural networks are used for screening different abnormal cells. For specific implementation of the computer-readable storage medium related to the present application, reference may be made to the above embodiments of the abnormal cell screening method, which are not described herein again.
It should be noted that, for simplicity of description, the above-mentioned method embodiments are described as a series of acts or combination of acts, but those skilled in the art will recognize that the present invention is not limited by the order of acts, as some steps may occur in other orders or concurrently in accordance with the invention. Further, those skilled in the art should also appreciate that the embodiments described in the specification are preferred embodiments and that the acts and modules referred to are not necessarily required by the invention.
The above-mentioned embodiments are only used for illustrating the technical solutions of the present invention, and not for limiting the same; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and the modifications or the substitutions do not make the essence of the corresponding technical solutions depart from the scope of the technical solutions of the embodiments of the present invention.
Claims (10)
1. A method for screening abnormal cells, comprising:
acquiring a cervical cell image;
segmenting the cervical cell image based on gray values of pixel points in the cervical cell image to obtain a plurality of cervical cell sub-images;
respectively inputting the plurality of cervical cell subimages into an abnormal cell screening model to obtain a plurality of prediction results corresponding to the plurality of cervical cell subimages, wherein each cervical cell subimage corresponds to one prediction result, each prediction result is used for indicating abnormal cells included in each cervical cell subimage, and the abnormal cells are cells which are diseased or cancerated on the basis of the cervical cells;
selecting a plurality of prediction results from the plurality of prediction results as a plurality of first prediction results according to a preset selection strategy;
obtaining a plurality of first cervical cell sub-images corresponding to the plurality of first prediction results;
displaying the plurality of first cervical cell sub-images on a display interface;
the abnormal cell screening model comprises a plurality of neural networks, the number of the neural networks is equal to the number of the types of the abnormal cells, and the neural networks are used for screening different abnormal cells.
2. The method of claim 1, wherein said segmenting the cervical cell image based on gray values of pixel points in the cervical cell image to obtain a plurality of cervical cell sub-images comprises:
acquiring a gray value corresponding to each pixel point in the cervical cell image;
determining a pixel point with the minimum gray value according to the gray value corresponding to each pixel point in the cervical cell image;
selecting any one pixel point from the pixel points as a coordinate origin for segmenting the cervical cell image;
establishing a coordinate system on the cervical cell image based on the origin of coordinates, wherein the coordinate system takes the positive transverse direction of the cervical cell image as an x-axis and the positive longitudinal direction of the cervical cell image as a y-axis;
segmenting the cervical cell image starting from the origin of coordinates to obtain the plurality of cervical cell sub-images.
3. The method of claim 2, wherein said segmenting said cervical cell image from said origin of coordinates to obtain a plurality of cervical cell sub-images comprises:
determining a plurality of gray level difference values according to the gray level value corresponding to each pixel point in the cervical cell image, wherein each gray level difference value is the difference value of each pixel point and the corresponding adjacent pixel point in the gray level value;
dividing the gray difference values falling into the same gray value interval into a group to obtain a plurality of gray groups;
determining a plurality of segmentation shapes corresponding to the cervical cell image from the origin of coordinates according to the gray groups;
normalizing irregular shapes in the plurality of segmentation shapes to obtain a plurality of regular first segmentation shapes;
setting the plurality of first tangent shapes as the plurality of cervical cell subimages.
4. The method of claim 1, wherein said segmenting the cervical cell image based on gray values of pixel points in the cervical cell image to obtain a plurality of cervical cell sub-images comprises:
acquiring a gray value corresponding to each pixel point in the cervical cell image;
determining a plurality of groups of pixel points with the same gray value in the cervical cell image according to the gray value corresponding to each pixel point in the cervical cell image, wherein each group of pixel points comprises at least one pixel point, and the gray values corresponding to each pixel point in the at least one pixel point are the same;
segmenting the cervical cell image according to the multiple groups of pixel points to obtain the multiple cervical cell sub-images, wherein the multiple groups of pixel points correspond to the multiple cervical cell sub-images.
5. The method of claim 4, wherein said segmenting said cervical cell image according to said plurality of groups of pixel points to obtain said plurality of cervical cell sub-images comprises:
segmenting the cervical cell image according to the multiple groups of pixel points to obtain a plurality of second cervical cell sub-images, wherein the multiple groups of pixel points correspond to the plurality of second cervical cell sub-images;
determining at least one third cervical cell sub-image with irregular outer contour among the plurality of second cervical cell sub-images;
performing the following operations for each of the at least one third cervical cell sub-image to obtain the plurality of cervical cell sub-images, including:
determining the outer contour size corresponding to the currently processed third cervical cell subimage; acquiring a template image according to the outer contour size corresponding to the currently processed third cervical cell subimage so as to obtain the outer contour size corresponding to the template image; and performing pixel diffusion on the currently processed third cervical cell subimage according to the outer contour size corresponding to the template image, and stopping the pixel diffusion until the outer contour size corresponding to the currently processed third cervical cell subimage is the same as the outer contour size corresponding to the template image, wherein the pixel diffusion is performed by adopting a first gray value.
6. The method of claim 1, wherein before the inputting the plurality of cervical cell sub-images into the abnormal cell screening model respectively to obtain a plurality of predictions corresponding to the plurality of cervical cell sub-images, the method further comprises:
acquiring a training set, wherein the training set comprises a plurality of training subsets, the plurality of training subsets correspond to the plurality of neural networks, each training subset comprises a plurality of cervical cell sub-image sets with different brightness levels, each cervical cell sub-image set comprises a plurality of second cervical cell sub-images with one brightness level, and each second cervical cell sub-image in the plurality of second cervical cell sub-images comprises different abnormal cells;
constructing a plurality of neural networks to be trained, wherein the plurality of neural networks to be trained correspond to the plurality of neural networks;
and training the training set based on the plurality of neural networks to be trained to obtain the abnormal cell screening model.
7. The method of claim 6, wherein the obtaining the training set comprises:
displaying the plurality of third cervical cell sub-images on a labeling interface;
when a marking operation for a plurality of positions on the marking interface is detected, marking the plurality of third cervical cell sub-images corresponding to the plurality of positions to obtain a plurality of fourth cervical cell sub-images corresponding to the plurality of third cervical cell sub-images, wherein each fourth cervical cell sub-image is an image obtained after marking each third cervical cell sub-image;
processing the fourth cervical cell subimages respectively by using a plurality of preset brightnesses to obtain a plurality of cervical cell subimages sets included in each of a plurality of training subsets, wherein the preset brightnesses correspond to the plurality of cervical cell subimages sets;
setting the plurality of cervical cell subimages included in each of the plurality of training subsets as the training set.
8. An abnormal cell screening apparatus, comprising:
the first acquisition module is used for acquiring a cervical cell image;
the segmentation module is used for segmenting the cervical cell image based on the gray value of the pixel point in the cervical cell image to obtain a plurality of cervical cell sub-images;
the input module is used for respectively inputting the plurality of cervical cell sub-images into the abnormal cell screening model so as to obtain a plurality of prediction results corresponding to the plurality of cervical cell sub-images, wherein each cervical cell sub-image corresponds to one prediction result, each prediction result is used for indicating abnormal cells included in each cervical cell sub-image, and the abnormal cells are cells which are diseased or cancerized on the basis of the cervical cells;
the selection module is used for selecting a plurality of prediction results from the plurality of prediction results as a plurality of first prediction results according to a preset selection strategy;
a second obtaining module, configured to obtain a plurality of first cervical cell sub-images corresponding to the plurality of first prediction results;
a display module for displaying the plurality of first cervical cell sub-images on a display interface;
the abnormal cell screening model comprises a plurality of neural networks, the number of the neural networks is equal to the number of the types of the abnormal cells, and the neural networks are used for screening different abnormal cells.
9. An electronic device for abnormal cell screening, comprising a processor, a memory, a communication interface, and one or more programs, wherein the one or more programs are stored in the memory and are generated as instructions to be executed by the processor to perform the steps of the method of any of claims 1-7.
10. A computer-readable storage medium, characterized in that the computer-readable storage medium is used to store a computer program, which is executed by the processor, to implement the method of any of claims 1-7.
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| CN110992303B (en) | 2023-12-22 |
| WO2021082434A1 (en) | 2021-05-06 |
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