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

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arxiv 2005.08645 v1 pith:7WVDK32H submitted 2020-05-09 cs.CV cs.LGeess.IV

Jevgenij Gamper , Navid Alemi Kooohbanani , Nasir Rajpoot This is my paper · ORCID
classification cs.CVcs.LGeess.IV
keywords classificationlearningmulti-taskareacancercancerscombinecomputational
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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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  1. RepSNet: A Nucleus Instance Segmentation model based on Boundary Regression and Structural Re-parameterization

    eess.IV 2025-05 conditional novelty 6.0 of 10

    RepSNet combines four-direction boundary distance regression with a boundary voting mechanism and reparameterizable encoder-decoder to reach mPQ 0.5633 on the authors' Lizard split and 0.478 on the official CoNIC test set.

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