Determining Biomarkers from Histopathology Slide Images

A computing device comprising:

• one or more memories;
• and
• one or more processors configured to,
• receive a digital image of a Hematoxylin and eosin (H&E) stained slide of target tissue;
• perform an image tiling process on the digital image by separating the digital image into a plurality of tile images, where each of the plurality of tile images contains a different portion of the digital image;
• apply the plurality of tile images to a multiscale deep learning framework comprising one or more trained deep learning multiscale classifier models, each trained deep learning multiscale classifier models being trained to classify a different tissue classification for each tile image and determining a tissue classification for each of the plurality of tile images, using the multiscale deep learning framework;
• identify cells within each of the plurality of tile images using a trained cell segmentation model;
• and
• identify a predicted presence of one or more biomarkers associated with the digital image based at least in part on a comparison of the tissue classification of identified cells within each of a subset of adjacent tile images.

A system comprising:

• a network interface to connect the system a remote computing device over a network;
• one or more memories;
• and
• one or more processors configured to,
• receive a digital image of a Hematoxylin and eosin (H&E) stained slide of target tissue;
• perform an image tiling process on the digital image by separating the digital image into a plurality of tile images, where each of the plurality of tile images contains a different portion of the digital image;
• apply the plurality of tile images to a multiscale deep learning framework comprising one or more trained deep learning multiscale classifier models, each trained deep learning multiscale classifier models being trained to classify a different tissue classification for each tile image and determining a tissue classification for each of the plurality of tile images, using the multiscale deep learning framework;
• identify cells within each of the plurality of tile images using a trained cell segmentation model;
• identify a predicted presence of one or more biomarkers associated with the digital image based at least in part on a comparison of the tissue classification of identified cells within each of a subset of adjacent tile images;
• and
• generate a viewer application (App), accessible by the remote computing device over the network, the viewer App allowing a user at the remote computing device to interact, through a graphical user interface, with a report containing the predicted presence of the one or more biomarkers.