Multi-Task Learning in Histo-pathology for Widely Generalizable Model
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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.
Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). First posted on arXiv 9 May 2020.
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Citing papers per year: 2021: 1; 2022: 1; 2023: 0; 2024: 1; 2025: 1.
4 papers.
- 2021Lizard: A Large-Scale Dataset for Colonic Nuclear Instance Segmentation and Classificationcited by 89
- 2022One Model is All You Need: Multi-Task Learning Enables Simultaneous Histology Image Segmentation and Classificationcited by 38
- 2025RepSNet: A Nucleus Instance Segmentation model based on Boundary Regression and Structural Re-parameterizationcited by 2
- 2024A Survey on Cell Nuclei Instance Segmentation and Classification: Leveraging Context and Attentioncited by 0
- 2025RepSNet: A Nucleus Instance Segmentation model based on Boundary Regression and Structural Re-parameterization8 May 2025
- 2024A Survey on Cell Nuclei Instance Segmentation and Classification: Leveraging Context and Attention26 Jul 2024
- 2022One Model is All You Need: Multi-Task Learning Enables Simultaneous Histology Image Segmentation and Classification28 Feb 2022
- 2021Lizard: A Large-Scale Dataset for Colonic Nuclear Instance Segmentation and Classification25 Aug 2021