# Weakly Supervised Multi-Task Learning for Cell Detection and Segmentation

arXiv: https://arxiv.org/abs/1910.12326 · HTML: https://mlsanity.com/paper/1910.12326/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Oct 2019.

## Built on by (most cited first, 16 of 16)

- [Multi-Class Cell Detection Using Spatial Context Representation](https://mlsanity.com/paper/2110.04886/) (2021)
- [Nuclei Segmentation with Point Annotations from Pathology Images via Self-Supervised Learning and Co-Training](https://mlsanity.com/paper/2202.08195/) (2022)
- [Deep Learning in Breast Cancer Imaging: A Decade of Progress and Future Directions](https://mlsanity.com/paper/2304.06662/) (2023)
- [Weakly Supervised Nuclei Segmentation via Instance Learning](https://mlsanity.com/paper/2202.01564/) (2022)
- [BoNuS: Boundary Mining for Nuclei Segmentation with Partial Point Labels](https://mlsanity.com/paper/2401.07437/) (2024)
- [Weakly Supervised Learning for cell recognition in immunohistochemical cytoplasm staining images](https://mlsanity.com/paper/2202.13372/) (2022)
- [Mixed-UNet: Refined Class Activation Mapping for Weakly-Supervised Semantic Segmentation with Multi-scale Inference](https://mlsanity.com/paper/2205.04227/) (2022)
- [Topology-Guided Multi-Class Cell Context Generation for Digital Pathology](https://mlsanity.com/paper/2304.02255/) (2023)
- [End-to-end cell recognition by point annotation](https://mlsanity.com/paper/2207.00176/) (2022)
- [A General Deep Learning framework for Neuron Instance Segmentation based on Efficient UNet and Morphological Post-processing](https://mlsanity.com/paper/2202.08682/) (2022)
- [Point-supervised Single-cell Segmentation via Collaborative Knowledge Sharing](https://mlsanity.com/paper/2304.10671/) (2023)
- [Unsupervised Dense Nuclei Detection and Segmentation with Prior Self-activation Map For Histology Images](https://mlsanity.com/paper/2210.07862/) (2022)
- [Weakly Supervised Deep Instance Nuclei Detection using Points Annotation in 3D Cardiovascular Immunofluorescent Images](https://mlsanity.com/paper/2208.00098/) (2022)
- [Label-efficient Contrastive Learning-based model for nuclei detection and classification in 3D Cardiovascular Immunofluorescent Images](https://mlsanity.com/paper/2309.03744/) (2023)
- [Entropy Bootstrapping for Weakly Supervised Nuclei Detection](https://mlsanity.com/paper/2411.13528/) (2024)
- [Leveraging Weak Supervision for Cell Localization in Digital Pathology Using Multitask Learning and Consistency Loss](https://mlsanity.com/paper/2412.15392/) (2024)

## Abstract (arXiv)

Cell detection and segmentation is fundamental for all downstream analysis of digital pathology images. However, obtaining the pixel-level ground truth for single cell segmentation is extremely labor intensive. To overcome this challenge, we developed an end-to-end deep learning algorithm to perform both single cell detection and segmentation using only point labels. This is achieved through the combination of different task orientated point label encoding methods and a multi-task scheduler for training. We apply and validate our algorithm on PMS2 stained colon rectal cancer and tonsil tissue images. Compared to the state-of-the-art, our algorithm shows significant improvement in cell detection and segmentation without increasing the annotation efforts.