Buyer Beware: confounding factors and biases abound when predicting omics-based biomarkers from histological images
Muhammad Dawood,
Kim Branson,
Sabine Tejpar
et al.
Abstract:SummaryBackgroundRecent advancements in computational pathology have introduced deep learning methods to predict genomic, transcriptomic and molecular biomarkers from routine histology whole slide images (WSIs) for cancer diagnosis, prognosis, and treatment. However, existing methods often overlook the critical role of co-dependencies among biomarker statuses during training and inference. We hypothesize that this oversight results in models that predict the combined effect of multiple interdependent biomarker… Show more
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