Abstract: A machine learning framework for breast cancer histologic grading is described herein. In an example, a method involves accessing a whole slide image of a specimen. The image is processed using a first, second, third, and fourth machine learning process. A first output of the first machine learning process indicates portions of the image predicted to depict tumor cells. A second output of the second machine learning process corresponds to a mitotic count predicted score for a mitotic count depicted in the image, a third output of the third machine learning process corresponds to a nuclear pleomorphism predicted score for nuclear pleomorphism depicted in the image, and a fourth output of the fourth machine learning process corresponds to a tubule formation predicted score for tubule formation depicted in the image. A combined score of a predicted histologic grade of a disease in the image is generated based on the outputs.

Abstract: One example method includes receiving a digital image of a needle core prostate biopsy, displaying, using a display device, a magnified portion of the digital image, obtaining, from a deep learning model, Gleason scores corresponding to patches of the magnified portion of the digital image, and displaying, using the display device, a superimposed overlay on the magnified portion of the digital image based on the Gleason scores and corresponding confidence values of the Gleason scores, the superimposed overlay comprising one or more outlines corresponding one or more Gleason scores associated with the magnified portion of the digital image and comprising image patches having colors based on a Gleason score of the Gleason scores corresponding to an underlying portion of the magnified portion of the digital image and a confidence value of the corresponding Gleason score.

Abstract: One example method includes receiving a digital image of a needle core prostate biopsy, displaying, using a display device, a magnified portion of the digital image, obtaining, from a deep learning model, Gleason scores corresponding to patches of the magnified portion of the digital image, and displaying, using the display device, a superimposed overlay on the magnified portion of the digital image based on the Gleason scores and corresponding confidence values of the Gleason scores, the superimposed overlay comprising one or more outlines corresponding one or more Gleason scores associated with the magnified portion of the digital image and comprising image patches having colors based on a Gleason score of the Gleason scores corresponding to an underlying portion of the magnified portion of the digital image and a confidence value of the corresponding Gleason score.