  We propose a Deep learning-based weak label learning method for analyzing
whole slide images (WSIs) of Hematoxylin and Eosin (H&E) stained tumor tissue
not requiring pixel-level or tile-level annotations using Self-supervised
pre-training and heterogeneity-aware deep Multiple Instance LEarning
(DeepSMILE). We apply DeepSMILE to the task of Homologous recombination
deficiency (HRD) and microsatellite instability (MSI) prediction. We utilize
contrastive self-supervised learning to pre-train a feature extractor on
histopathology tiles of cancer tissue. Additionally, we use variability-aware
deep multiple instance learning to learn the tile feature aggregation function
while modeling tumor heterogeneity. For MSI prediction in a tumor-annotated and
color normalized subset of TCGA-CRC (n=360 patients), contrastive
self-supervised learning improves the tile supervision baseline from 0.77 to
0.87 AUROC, on par with our proposed DeepSMILE method. On TCGA-BC (n=1041
patients) without any manual annotations, DeepSMILE improves HRD classification
performance from 0.77 to 0.81 AUROC compared to tile supervision with either a
self-supervised or ImageNet pre-trained feature extractor. Our proposed methods
reach the baseline performance using only 40% of the labeled data on both
datasets. These improvements suggest we can use standard self-supervised
learning techniques combined with multiple instance learning in the
histopathology domain to improve genomic label classification performance with
fewer labeled data.
