Joint multi-task learning improves weakly-supervised biomarker
  prediction in computational pathology
Omar S. M. El Nahhas Georg Wölflein Marta Ligero Tim Lenz Marko van Treeck Firas Khader Daniel Truhn Jakob Nikolas Kather
Deep Learning (DL) can predict biomarkers directly from digitized cancer
histology in a weakly-supervised setting. Recently, the prediction of
continuous biomarkers through regression-based DL has seen an increasing
interest. Nonetheless, clinical decision making often requires a categorical
outcome. Consequently, we developed a weakly-supervised joint multi-task
Transformer architecture which has been trained and evaluated on four public
patient cohorts for the prediction of two key predictive biomarkers,
microsatellite instability (MSI) and homologous recombination deficiency (HRD),
trained with auxiliary regression tasks related to the tumor microenvironment.
Moreover, we perform a comprehensive benchmark of 16 approaches of task
balancing for weakly-supervised joint multi-task learning in computational
pathology. Using our novel approach, we improve over the state-of-the-art area
under the receiver operating characteristic by +7.7% and +4.1%, as well as
yielding better clustering of latent embeddings by +8% and +5% for the
prediction of MSI and HRD in external cohorts, respectively.