Abstract: Aspects of the disclosure are directed to the field of artificial intelligence technologies and provides a method and an apparatus for obtaining a feature of duct tissue based on computer vision, an intelligent microscope, a storage medium, and a computer device. The method can include the steps of obtaining an image including duct tissue, determining, in an image region corresponding to the duct tissue in the image, at least two feature obtaining regions adapted to duct morphology of the duct tissue, obtaining cell features of cells of the duct tissue in the feature obtaining regions respectively, and obtaining a feature of the duct tissue based on the cell features of the cells of the duct tissue in the feature obtaining regions respectively.

Abstract: A medical image processing method and apparatus, and an image processing method and apparatus, terminal and storage medium that obtains a to-be-processed medical image; generates a difference image according to the first image data, the second image data, and the third image data included in the to-be-processed medical image; and performs binarization processing on the difference image to obtain a binarized image, a foreground region of the binarized image corresponding to a pathological tissue region of the to-be-processed medical image. A difference image is generated based on color information of different channels before binarization processing is performed on an image, thereby effectively using the color information in the image. The pathological tissue region extracted based on the difference image is more accurate and facilitates subsequent image analysis.

Abstract: Described are a system, method, and computer program product for dynamic node classification in temporal-based machine learning classification models. The method includes receiving graph data of a discrete time dynamic graph including graph snapshots, and node classifications associated with all nodes in the discrete time dynamic graph. The method includes converting the discrete time dynamic graph to a time-augmented spatio-temporal graph and generating an adjacency matrix based on a temporal walk of the time-augmented spatio-temporal graph. The method includes generating an adaptive information transition matrix based on the adjacency matrix and determining feature vectors based on the nodes and the node attribute matrix of each graph snapshot.

Abstract: Methods, systems, and computer program products are provided for spatial-temporal prediction using trained spatial-temporal masked autoencoders. An example system includes a processor configured to determine a structural dependency graph associated with a networked system. The processor is also configured to receive multivariate time-series data from a first time period associated with the networked system. The processor is further configured to mask the plurality of edges of the structural dependency graph and mask the multivariate time-series data. The processor is further configured to train a spatial-temporal autoencoder based on the masked structural representation and the masked temporal representation. The processor is further configured to generate a prediction using a spatial-temporal machine learning model including the trained spatial-temporal autoencoder, the prediction associated with an attribute of the networked system in a second time period subsequent to the first time period.

Abstract: Described are a system, method, and computer program product for dynamic node classification in temporal-based machine learning classification models. The method includes receiving graph data of a discrete time dynamic graph including graph snapshots, and node classifications associated with all nodes in the discrete time dynamic graph. The method includes converting the discrete time dynamic graph to a time-augmented spatio-temporal graph and generating an adjacency matrix based on a temporal walk of the time-augmented spatio-temporal graph. The method includes generating an adaptive information transition matrix based on the adjacency matrix and determining feature vectors based on the nodes and the node attribute matrix of each graph snapshot.

Abstract: An AI-based object classification method and apparatus, a computer-readable storage medium, and a computer device. The method includes: obtaining a target image to be processed, the target image including a target detection object; separating a target detection object image of the target detection object from the target image; inputting the target detection object image into a feature object prediction model to obtain a feature object segmentation image of a feature object in the target detection object image; obtaining quantitative feature information of the target detection object according to the target detection object image and the feature object segmentation image; and classifying the target detection object image according to the quantitative feature information to obtain category information of the target detection object in the target image.

Abstract: Described are a system, method, and computer program product for dynamic node classification in temporal-based machine learning classification models. The method includes receiving graph data of a discrete time dynamic graph including graph snapshots, and node classifications associated with all nodes in the discrete time dynamic graph. The method includes converting the discrete time dynamic graph to a time-augmented spatio-temporal graph and generating an adjacency matrix based on a temporal walk of the time-augmented spatio-temporal graph. The method includes generating an adaptive information transition matrix based on the adjacency matrix and determining feature vectors based on the nodes and the node attribute matrix of each graph snapshot.

Abstract: An image generation method, apparatus, device, and storage medium. The method includes: obtaining contour information and target region information; determining at least one target contour according to the contour information and the target region information, the at least one target contour wholly or partly located in a target region; decreasing first coordinates of a plurality of contour points in the at least one target contour to obtain second location information, second location information of the at least one target contour includes second coordinates of a plurality of contour points in the at least one target contour, and the first coordinates of the plurality of contour points in the at least one target contour having a same decreasing extent; and generating a target image corresponding to the target region according to the second location information of the at least one target contour and the target region information.

Abstract: A method includes extracting, by an analysis computer, a plurality of first datasets from a plurality of graph snapshots using a graph structural learning module. The analysis computer can then extract a plurality of second datasets from the plurality of first datasets using a temporal convolution module across the plurality of graph snapshots.

Abstract: Aspects of the disclosure are directed to the field of artificial intelligence technologies and provides a method and an apparatus for obtaining a feature of duct tissue based on computer vision, an intelligent microscope, a storage medium, and a computer device. The method can include the steps of obtaining an image including duct tissue, determining, in an image region corresponding to the duct tissue in the image, at least two feature obtaining regions adapted to duct morphology of the duct tissue, obtaining cell features of cells of the duct tissue in the feature obtaining regions respectively, and obtaining a feature of the duct tissue based on the cell features of the cells of the duct tissue in the feature obtaining regions respectively.

Abstract: A medical image processing method and apparatus, and an image processing method and apparatus, terminal and storage medium that obtains a to-be-processed medical image; generates a difference image according to the first image data, the second image data, and the third image data included in the to-be-processed medical image; and performs binarization processing on the difference image to obtain a binarized image, a foreground region of the binarized image corresponding to a pathological tissue region of the to-be-processed medical image. A difference image is generated based on color information of different channels before binarization processing is performed on an image, thereby effectively using the color information in the image. The pathological tissue region extracted based on the difference image is more accurate and facilitates subsequent image analysis.

Abstract: An image generation method, apparatus, device, and storage medium. The method includes: obtaining contour information and target region information; determining at least one target contour according to the contour information and the target region information, the at least one target contour wholly or partly located in a target region; decreasing first coordinates of a plurality of contour points in the at least one target contour to obtain second location information, second location information of the at least one target contour includes second coordinates of a plurality of contour points in the at least one target contour, and the first coordinates of the plurality of contour points in the at least one target contour having a same decreasing extent; and generating a target image corresponding to the target region according to the second location information of the at least one target contour and the target region information.

Abstract: An AI-based object classification method and apparatus, a computer-readable storage medium, and a computer device. The method includes: obtaining a target image to be processed, the target image including a target detection object; separating a target detection object image of the target detection object from the target image; inputting the target detection object image into a feature object prediction model to obtain a feature object segmentation image of a feature object in the target detection object image; obtaining quantitative feature information of the target detection object according to the target detection object image and the feature object segmentation image; and classifying the target detection object image according to the quantitative feature information to obtain category information of the target detection object in the target image.