CPC G06T 7/0012 (2013.01) [G06F 18/214 (2023.01); G06T 7/11 (2017.01); G06V 20/695 (2022.01); G06V 20/698 (2022.01); G16H 10/40 (2018.01); G16H 30/40 (2018.01); G16H 50/20 (2018.01); G06T 2207/10056 (2013.01); G06T 2207/20081 (2013.01); G06T 2207/30024 (2013.01); G06V 2201/03 (2022.01)] | 17 Claims |
1. A computer-implemented method for analyzing a plurality of digital images corresponding to a pathology specimen, comprising:
receiving the plurality of digital images of the pathology specimen;
determining, by a machine learning system, a human epidermal growth factor receptor 2 (HER2) biomarker expression level prediction for the plurality of digital images, the machine learning system having been trained by processing a plurality of training images wherein determining the HER2 biomarker expression level prediction includes:
breaking each of the plurality of digital images into a plurality of tiles;
determining, by machine learning, a HER2 score corresponding to a prediction for each tile to determine a plurality of tile predictions, each HER2 score being based on immunohistochemistry (IHC) on a scale of 0, 1+, 1+ to 2+, 2+, and/or 3+; and
aggregating the plurality of tile predictions into at least one part level HER2 prediction, the HER2 biomarker expression level prediction being based on the at least one part level HER2 prediction; and
outputting, based on the HER2 biomarker expression level prediction, an HER2 score.
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