We propose a Deep learning-based weak label learning method for analysing whole slide images (WSIs) of Hematoxylin and Eosin (H&E) stained tumorcells 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. Compared to state-of-the-art genomic label classification methods, DeepSMILE improves classification performance for HRD from $70.43pm4.10%$ to $83.79pm1.25%$ AUC and MSI from $78.56pm6.24%$ to $90.32pm3.58%$ AUC in a multi-center breast and colorectal cancer dataset, respectively. These improvements suggest we can improve genomic label classification performance without collecting larger datasets. In the future, this may reduce the need for expensive genome sequencing techniques, provide personalized therapy recommendations based on widely available WSIs of cancer tissue, and improve patient care with quicker treatment decisions - also in medical centers without access to genome sequencing resources.
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