Self-supervised attention-based deep learning for pan-cancer mutation prediction from histopathology
Oliver Lester Saldanha, Chiara Maria Lavinia Loeffler, Jan Niehues, Marko van Treeck, Tobias Paul Seraphin, Katherine Hewitt, Didem Çifçi, Gregory Patrick Veldhuizen, Siddhi Ramesh, Alexander T. Pearson, Jakob Nikolas Kather
Abstract
The histopathological phenotype of tumors reflects the underlying genetic makeup. Deep learning can predict genetic alterations from pathology slides, but it is unclear how well these predictions generalize to external datasets. We performed a systematic study on Deep-Learning-based prediction of genetic alterations from histology, using two large datasets of multiple tumor types. We show that an analysis pipeline that integrates self-supervised feature extraction and attention-based multiple instance learning achieves a robust predictability and generalizability.