Abstract: In some embodiments, tissue microarray (TMA) core images are used to train a deep learning network that can then be deployed to computer inferences regarding whole tissue section (WTS) images (WSIs). Preprocessing aligns paired serial core images from differently stained core sections with their associated metadata and H-scores (or other label data obtained from evaluating one of the paired core sections). In some embodiment, a self-supervised learning (SSL) pre-trained encoder is used to generate patch-level embeddings from TMA core images associated with corresponding labels that are then used to train an attention-based deep learning network to generate inferences. These and other aspects of the present disclosure are more fully detailed herein.

Abstract: Computerized systems and methods for digital histopathology analysis are disclosed. In one embodiment, a series of deep learning networks are used that train, in succession, on datasets of successively increasing relevance. In some examples, learned parameters from at least a portion of one deep learning network are transferred to a next deep learning network in a succession of deep learning networks. In some examples, at least one of the deep learning networks includes a self-supervised learning network. In some examples, at least one of the deep learning networks includes an attention-based learning network. These and other examples and details are disclosed herein in various contexts including, for example evaluating genotypes of cancer tissue (e.g., bladder, prostate, or lung cancer) using histopathology images. In some examples, the context is to assist in predicting presence or absence of certain cancer genotypes and/or predicting patient responses to a new treatment.

Abstract: An estimation system automatically estimates a severity of ulcerative colitis (UC) based on an endoscopic video. During a training phase, a training system trains one or more machine-learned models based on a set of training videos each annotated with a single video-level UC severity score representing an aggregate UC severity observed in the whole video. The one or more machine-learned models are capable of estimating UC severity depicted in an individual endoscopic video frame. Applying the one or more machine-learned models to an endoscopic test video of unknown UC severity enables estimation of frame-level UC severity scores for each frame of the test video. The frame-level UC severity scores may be represented on a continuous severity scale or may be mapped to discrete values on a predefined baseline severity scale such as a Mayo Endoscopic Subscore (MES) scale.