Abstract: A method comprising receiving images depicting stained target tissue, segmenting the images into cell type and region type segmentations, extracting cell phenotype features from an analysis of the stains for cell type segmentations, clustering the cell type segmentations, computing feature vectors each including the respective cell phenotype features, and an indication of a location of the cell type segmentation relative to region type segmentation(s), creating a cell-graph based on the feature vectors of cell type segmentations and/or clusters, wherein each node denotes respective cell type segmentation and/or respective cluster and includes the feature vector, and edges represent a physical distance between cell type segmentations and/or clusters corresponding to the respective nodes, inputting the cell-graph into a graph neural network, and obtaining an indication of a target therapy likely to be effective for treatment of medical condition in the subject as an outcome of the graph neural network.

Abstract: There is provided a method of computing at least one slide-level tissue type for a tissue image of tissue extracted from a patient, comprising: receiving a tissue image of a slide including tissue extracted from the patient, segmenting tissue objects of the tissue image, creating a tissue image patches from the segmented tissue objects of the tissue image, classifying, by a patch-level classifier, each of the plurality of tissue image patches into at least one patch-level tissue type, wherein each of the classified tissue image patches is associated with a relative location within the tissue image, analyzing, by a slide-level analysis code, the classified at least one patch-level tissue type and associated relative location for each of the plurality of tissue image patches outputted by the patch-level classifier, for computing at least one slide-level tissue type for the tissue image, and providing the at least one slide-level tissue type.

Abstract: A method comprising receiving images depicting stained target tissue, segmenting the images into cell type and region type segmentations, extracting cell phenotype features from an analysis of the stains for cell type segmentations, clustering the cell type segmentations, computing feature vectors each including the respective cell phenotype features, and an indication of a location of the cell type segmentation relative to region type segmentation(s), creating a cell-graph based on the feature vectors of cell type segmentations and/or clusters, wherein each node denotes respective cell type segmentation and/or respective cluster and includes the feature vector, and edges represent a physical distance between cell type segmentations and/or clusters corresponding to the respective nodes, inputting the cell-graph into a graph neural network, and obtaining an indication of a target therapy likely to be effective for treatment of medical condition in the subject as an outcome of the graph neural network.

Abstract: There is provided a method of computing at least one slide-level tissue type for a tissue image of tissue extracted from a patient, comprising: receiving a tissue image of a slide including tissue extracted from the patient, segmenting tissue objects of the tissue image, creating a tissue image patches from the segmented tissue objects of the tissue image, classifying, by a patch-level classifier, each of the plurality of tissue image patches into at least one patch-level tissue type, wherein each of the classified tissue image patches is associated with a relative location within the tissue image, analyzing, by a slide-level analysis code, the classified at least one patch-level tissue type and associated relative location for each of the plurality of tissue image patches outputted by the patch-level classifier, for computing at least one slide-level tissue type for the tissue image, and providing the at least one slide-level tissue type.

Abstract: There is provided a method of computing at least one slide-level tissue type for a tissue image of tissue extracted from a patient, comprising: receiving a tissue image of a slide including tissue extracted from the patient, segmenting tissue objects of the tissue image, creating a tissue image patches from the segmented tissue objects of the tissue image, classifying, by a patch-level classifier, each of the plurality of tissue image patches into at least one patch-level tissue type, wherein each of the classified tissue image patches is associated with a relative location within the tissue image, analyzing, by a slide-level analysis code, the classified at least one patch-level tissue type and associated relative location for each of the plurality of tissue image patches outputted by the patch-level classifier, for computing at least one slide-level tissue type for the tissue image, and providing the at least one slide-level tissue type.

Abstract: There is provided a method of computing at least one slide-level tissue type for a tissue image of tissue extracted from a patient, comprising: receiving a tissue image of a slide including tissue extracted from the patient, segmenting tissue objects of the tissue image, creating a tissue image patches from the segmented tissue objects of the tissue image, classifying, by a patch-level classifier, each of the plurality of tissue image patches into at least one patch-level tissue type, wherein each of the classified tissue image patches is associated with a relative location within the tissue image, analyzing, by a slide-level analysis code, the classified at least one patch-level tissue type and associated relative location for each of the plurality of tissue image patches outputted by the patch-level classifier, for computing at least one slide-level tissue type for the tissue image, and providing the at least one slide-level tissue type.