| CPC G16H 50/20 (2018.01) [G06T 7/0012 (2013.01); G16H 30/40 (2018.01); G06N 3/02 (2013.01); G06T 2207/20084 (2013.01)] | 30 Claims |
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1. A computer-implemented method for using machine learning to process digital pathology images to predict disease progression, the method comprising:
accessing a digital pathology image that depicts a specimen stained with one or more stains, the specimen having been collected from a subject;
defining a set of patches for the digital pathology image, wherein each patch of the set of patches depicts a portion of the digital pathology image;
generating, for each patch of the set of patches and using an attention-score neural network, an attention score, wherein the attention-score neural network is trained using a loss function, the loss function penalizes attention-score variability across patches in training digital pathology images, the training digital pathology images labeled to indicate subsequent disease progression has occurred;
generating, using a result-prediction neural network and the attention scores, a result representing a prediction of whether or an extent to which a disease of the subject will progress; and
outputting the result.
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