Determining biomarkers from histopathology slide images

A non-transitory computer-readable medium comprising a set of computer-executable instructions that, when executed by one or more processors, cause a computer to:

• process a plurality of segmented tile images each corresponding to a different respective portion of the digital image using a deep learning framework by:
• (i) predicting a respective biomarker classification for each tile image using one or more biomarker classification models,
• wherein the one or more biomarker classification models are trained using a molecular training dataset that (a) corresponds to a plurality of training tissue samples, (b) includes molecular data based on sequencing of a substantially similar sample associated with each training tissue sample, and (c) includes a plurality of molecular data subsets clustered by biomarker, and
• (ii) predicting a respective tissue classification for each tile image using one or more trained deep learning classifier models;
• determine, based on (i) and (ii), a predicted presence of one or more biomarkers in the target tissue;
• and
• transmit, via the electronic network, the predicted presence of the one or more biomarkers.

A computer-implemented method for identifying biomarkers in a digital image of a Hematoxylin and Eosin-stained slide of a target tissue, comprising:

• processing a plurality of segmented tile images each corresponding to a different respective portion of the digital image using a deep learning framework by:
• (i) predicting a respective biomarker classification for each tile image using one or more biomarker classification models,
• wherein the one or more biomarker classification models are trained using a molecular training dataset that (a) corresponds to a plurality of training tissue samples, (b) includes molecular data based on sequencing of a substantially similar sample associated with each training tissue sample, and (c) includes a plurality of molecular data subsets clustered by biomarker, and
• (ii) predicting a respective tissue classification for each tile image using one or more trained deep learning classifier models;
• determining, based on (i) and (ii), a predicted presence of one or more biomarkers in the target tissue;
• and
• transmitting, via the electronic network, the predicted presence of the one or more biomarkers.