Abstract: This application relates to a video frame interpolation method performed by a computer device. The method includes: determining first optical flow information between two video frame images; predicting an intermediate frame image between the two video frame images based on the first optical flow information; for each of the two video frame images, determining second optical flow information between the video frame image and the predicted intermediate frame image; selecting image blocks corresponding to the first pixel points from the video frame image according to the second optical flow information; updating pixel values at first pixel points in the predicted intermediate frame image based on image blocks corresponding to the first pixel points in the predicted intermediate frame image; and fusing updated intermediate frame images respectively corresponding to the two video frame images to obtain a fused intermediate frame image.

Abstract: The present application discloses a data processing method and apparatus, device, medium and product, where the method includes: acquiring a target image, a semantic-level feature of the target image, a pixel-level feature of the target image, a semantic-level codebook, and a pixel-level codebook, where a plurality of indices are shared between the semantic-level codebook and the pixel-level codebook, and different indices indicate different entries in the same codebook; summing, for any of the indices, a distance between an entry indicated by the index in the semantic-level codebook and the semantic-level feature and a distance between an entry indicated by the index in the pixel-level codebook and the pixel-level feature to obtain a distance sum corresponding to the index; and comparing the distance sums corresponding to the indices to obtain a minimum value, and determining an indexed result of the target image according to an index corresponding to the minimum value.

Abstract: The present application discloses an image generation method and apparatus, a device, a medium and a product. The method includes a process of predicting a token sequence under a plurality of scales, and the process of predicting a token sequence under each scale is obtained through multiple rounds of sampling prediction, so as to realize a later round of sampling prediction on the basis of a result obtained from an earlier round of sampling prediction under the same scale so that the later round of sampling prediction may acquire an associated relationship between different local areas (for example, different local areas represented by different tokens) from the result.

Abstract: Provided is a method for processing an electroencephalogram signal executed by a computer device. The method includes acquiring an electroencephalogram signal, the electroencephalogram signal comprising an electroencephalogram signal data segment of a specified duration; determining a plurality of time nodes in the electroencephalogram signal data segment according to a preset time interval; predicting an action intention corresponding to each time node based on the electroencephalogram signal data segment to obtain action intention prediction results respectively corresponding to the plurality of time nodes; and determining a target action intention according to the action intention prediction results.

Abstract: Methods, apparatus, device, and storage medium for identifying an abnormal cell in a to-be-detected sample are disclosed. The method includes obtaining, by a device, multi-layer images of a to-be-detected sample, the to-be-detected sample comprising a single cell and a cell cluster; obtaining, by the device, multi-layer image blocks of the single cell and multi-layer image blocks of the cell cluster according to the multi-layer images; obtaining, by the device, a first identification result by a first image identification network according to the multi-layer image blocks of the single cell; obtaining, by the device, a second identification result by a second image identification network according to the multi-layer image blocks of the cell cluster; and determining, by the device, whether an abnormal cell exists in the to-be-detected sample according to the first identification result and the second identification result.

Abstract: A computer device obtains a to-be-annotated image having a first magnification. The device obtains an annotated image from an annotated image set, the annotated image distinct from the to-be-annotated image and having a second magnification that is distinct from with the first magnification. The annotated image set includes at least one annotated image. The device matches the to-be-annotated image with the annotated image to obtain an affine transformation matrix, and generates annotation information of the to-be-annotated image according to the affine transformation matrix and the annotated image. In this way, annotations corresponding to images at different magnifications may be migrated. For example, the annotations may be migrated from the low-magnification images to the high-magnification images, thereby reducing the manual annotation amount and avoiding repeated annotations, and further improving annotation efficiency and reducing labor costs.

Abstract: An image display method includes: processing a first image to obtain a first feature image, the first image being an image of a local area of a smear captured by a microscope, and the local area including multiple objects to be tested; obtaining a second feature image corresponding to the first feature image, the second feature image and the first feature image having a same size; obtaining a third feature image according to an image obtained by overlaying the first feature image and the second feature image, a feature point in the third feature image indicating a possibility that one of the multiple objects is an abnormal object; obtaining a second image according to the third feature image; and displaying the second image superimposed on the first image.

Abstract: This application describes a medical image classification method, a model training method, and a server. The medical image classification method includes: obtaining, by a device, a medical image data set. The device includes a memory storing instructions and a processor in communication with the memory. The method includes performing, by the device, quality analysis on the medical image data set, to extract feature information of a medical image in the medical image data set; and classifying, by the device, the medical image data set based on the feature information and by using a pre-trained deep learning network for performing anomaly detection and classification, to obtain a classification result.

Abstract: An image display method includes: processing a first image to obtain a first feature image, the first image being an image of a local area of a smear captured by a microscope, and the local area including multiple objects to be tested; obtaining a second feature image corresponding to the first feature image, the second feature image and the first feature image having a same size; obtaining a third feature image according to an image obtained by overlaying the first feature image and the second feature image, a feature point in the third feature image indicating a possibility that one of the multiple objects is an abnormal object; obtaining a second image according to the third feature image; and displaying the second image superimposed on the first image.

Abstract: Methods, apparatus, device, and storage medium for identifying an abnormal cell in a to-be-detected sample are disclosed. The method includes obtaining, by a device, multi-layer images of a to-be-detected sample, the to-be-detected sample comprising a single cell and a cell cluster; obtaining, by the device, multi-layer image blocks of the single cell and multi-layer image blocks of the cell cluster according to the multi-layer images; obtaining, by the device, a first identification result by a first image identification network according to the multi-layer image blocks of the single cell; obtaining, by the device, a second identification result by a second image identification network according to the multi-layer image blocks of the cell cluster; and determining, by the device, whether an abnormal cell exists in the to-be-detected sample according to the first identification result and the second identification result.

Abstract: A computer device obtains a to-be-annotated image having a first magnification. The device obtains an annotated image from an annotated image set, the annotated image distinct from the to-be-annotated image and having a second magnification that is distinct from with the first magnification. The annotated image set includes at least one annotated image. The device matches the to-be-annotated image with the annotated image to obtain an affine transformation matrix, and generates annotation information of the to-be-annotated image according to the affine transformation matrix and the annotated image. In this way, annotations corresponding to images at different magnifications may be migrated. For example, the annotations may be migrated from the low-magnification images to the high-magnification images, thereby reducing the manual annotation amount and avoiding repeated annotations, and further improving annotation efficiency and reducing labor costs.

Abstract: This application describes a medical image classification method, a model training method, and a server. The medical image classification method includes: obtaining, by a device, a medical image data set. The device includes a memory storing instructions and a processor in communication with the memory. The method includes performing, by the device, quality analysis on the medical image data set, to extract feature information of a medical image in the medical image data set; and classifying, by the device, the medical image data set based on the feature information and by using a pre-trained deep learning network for performing anomaly detection and classification, to obtain a classification result.