DETECTING INTRATUMOR HETEROGENEITY OF MOLECULAR SUBTYPES IN PATHOLOGY SLIDE IMAGES USING DEEP-LEARNING

摘要

Techniques are provided for determining molecular subtype classifications based on pathology slide images (SIs). A plurality of training SIs is segmented into a plurality of scaled patches. Each scaled patch is converted into a multiscale descriptor using a deep-learning neural network by mapping each of one or more patch representations to a patch-level descriptor and combining the patch-level descriptors. A classifier model is configured and trained to process the multiscale descriptors such that, for each training SI, the classifier model is operable to assign a patch-level molecular subtype classification to each of the scaled patches corresponding to the training SI and determine a Si-level molecular subtype classification based on the patch-level molecular subtype classifications. A molecular subtyping engine is configured to use the trained classifier model to determine a SI-level molecular subtype classification for a test SI.