CPC G06T 1/20 (2013.01) [G06F 18/21 (2023.01); G06F 18/2431 (2023.01); G06T 7/0012 (2013.01); G06T 7/11 (2017.01); G06T 11/00 (2013.01); G06V 10/44 (2022.01); G06V 10/764 (2022.01); G06V 10/82 (2022.01); G06T 2207/20081 (2013.01); G06T 2207/30024 (2013.01); G06T 2207/30096 (2013.01)] | 19 Claims |
1. A computing system for identifying biomarkers in a digital image of a Hematoxylin and Eosin-stained slide of a target tissue, comprising:
one or more processors;
an electronic network; and
one or more memories having stored thereon computer-executable instructions that, when executed by the one or more processors, cause the computing system 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 trained biomarker classification models; 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;
transmit, via the electronic network, the predicted presence of the one or more biomarkers;
receive a molecular training dataset for a plurality of training tissue samples, the molecular training dataset comprising molecular data based on sequencing of a substantially similar sample associated with each training tissue sample; and
identify one or more molecular data subsets in the molecular training dataset, each corresponding to a different respective biomarker, by processing the molecular training dataset using a clustering algorithm.
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