Multi-Task Learning in Histo-pathology for Widely Generalizable Model

Abstract

In this work we show preliminary results of deep multi-task learning in the area of computational pathology. We combine 11 tasks ranging from patch-wise oral cancer classification, one of the most prevalent cancers in the developing world, to multi-tissue nuclei instance segmentation and classification.

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Cited by in corpus (1)

• Learning Interpretable Microscopic Features of Tumor by Multi-task Adversarial CNNs To Improve Generalization