Abstract: Described herein are systems, methods, and programming for analyzing and classifying digital pathology images. Some embodiments include receiving whole slide images (WSIs) and dividing each of the WSIs into tiles. For each WSI, a random subset of the tiles may be selected and augmented views of each of the selected tiles may be generated. For each of the selected tiles, a first convolutional neural network (CNN) may be trained to: generate, using a first one of the augmented views corresponding to the selected tile, a first representation of the selected tile, and predict a second representation of the selected tile to be generated by a second CNN, wherein the second representation is generated based on a second one of the augmented views of the selected tile.

Abstract: For breast cancer detection with an x-ray scanner, a cascade of multiple classifiers is trained or used. One or more of the classifiers uses a deep-learnt network trained on non-x-ray data, at least initially, to extract features. Alternatively or additionally, one or more of the classifiers is trained using classification of patches rather than pixels and/or classification with regression to create additional cancer-positive partial samples.

Abstract: For breast cancer detection with an x-ray scanner, a cascade of multiple classifiers is trained or used. One or more of the classifiers uses a deep-learnt network trained on non-x-ray data, at least initially, to extract features. Alternatively or additionally, one or more of the classifiers is trained using classification of patches rather than pixels and/or classification with regression to create additional cancer-positive partial samples.