Abstract: One example method for biomarker detection in digitized pathology samples includes receiving a plurality of image patches corresponding to an image of a pathology slide having a hematoxylin and eosin-stained (“H&E”) stained sample of tissue, each image patch representing a different portion of the image; for each image patch, determining, using a first trained machine learning (“ML”) model, a patch biomarker status; and determining, using a second trained ML model, a tissue sample biomarker status for the sample of tissue based on the patch biomarker statuses of the image patches.

Abstract: One example method includes obtaining one or more histopathology images of a sample from a cancer patient; selecting a plurality of tissue image patches from the one or more histopathology images; determining, by a deep learning system comprising a plurality of trained machine learning (ML) models, a plurality of image features for the plurality of tissue image patch, wherein each tissue image patch is analyzed by one of the trained ML models; determining, by the deep learning system, probabilities of patient survival based on the determined plurality of image features; and generating, by the deep learning system, a prediction of patient survival based on the determined probabilities.

Abstract: One example method for AI prediction of prostate cancer outcomes involves receiving an image of prostate tissue; assigning Gleason pattern values to one or more regions within the image using an artificial intelligence Gleason grading model, the model trained to identify Gleason patterns on a patch-by-patch basis in a prostate tissue image; determining relative areal proportions of the Gleason patterns within the image; assigning at least one of a risk score or risk group value to the image based on the determined relative areal proportions; and outputting at least one of the risk score or the risk group value.