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

arXiv: https://arxiv.org/abs/2005.08645 · HTML: https://mlsanity.com/paper/2005.08645/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 May 2020.

## Built on by (most cited first, 4 of 4)

- [Lizard: A Large-Scale Dataset for Colonic Nuclear Instance Segmentation and Classification](https://mlsanity.com/paper/2108.11195/) (2021)
- [One Model is All You Need: Multi-Task Learning Enables Simultaneous Histology Image Segmentation and Classification](https://mlsanity.com/paper/2203.00077/) (2022)
- [RepSNet: A Nucleus Instance Segmentation model based on Boundary Regression and Structural Re-parameterization](https://mlsanity.com/paper/2505.05073/) (2025)
- [A Survey on Cell Nuclei Instance Segmentation and Classification: Leveraging Context and Attention](https://mlsanity.com/paper/2407.18673/) (2024)

## Abstract (arXiv)

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.