Abstract: In one embodiment, a method includes indexing a whole slide image dataset to generate one or more dataset embeddings corresponding to one or more respective regions of one or more whole slide images. Each dataset embedding includes a feature vector mapping the respective region to a feature embedding space. The method includes accessing a query image and generating an embedding for the query image that includes a feature vector mapping the query image to the feature embedding space. The method includes identifying result tiles by comparing the embedding for the query image to one or more of the dataset embeddings. The comparison is based on one or more distances between the embedding for the query image and the one or more of the dataset embeddings in the feature embedding space. The method includes generating a user interface including a display of the result tiles.

Abstract: Systems and methods relate to predicting disease progression by processing digital pathology images using neural networks. A digital pathology image that depicts a specimen stained with one or more stains is accessed. The specimen may have been collected from a subject. A set of patches are defined for the digital pathology image. Each patch of the set of patches depicts a portion of the digital pathology image. For each patch of the set of patches and using an attention-score neural network, an attention score is generated. The attention-score neural network may have been trained using a loss function that penalized attention-score variability across patches in training digital pathology images labeled to indicate no or low subsequent disease progression. Using a result-prediction neural network and the attention scores, a result is generated that represents a prediction of whether or an extent to which a disease of the subject will progress.