English

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

Computer Vision and Pattern Recognition 2020-05-19 v1 Machine Learning Image and Video Processing

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.

Keywords

Cite

@article{arxiv.2005.08645,
  title  = {Multi-Task Learning in Histo-pathology for Widely Generalizable Model},
  author = {Jevgenij Gamper and Navid Alemi Kooohbanani and Nasir Rajpoot},
  journal= {arXiv preprint arXiv:2005.08645},
  year   = {2020}
}
R2 v1 2026-06-23T15:37:27.068Z