# Dynamic Pseudo Label Optimization in Point-Supervised Nuclei Segmentation

arXiv: https://arxiv.org/abs/2406.16427 · HTML: https://mlsanity.com/paper/2406.16427/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 51 references that are in the map.
- First posted on arXiv 24 Jun 2024.

## Builds on (most cited first, 8 of 8)

- [A Simple Framework for Contrastive Learning of Visual Representations](https://mlsanity.com/paper/2002.05709/) (2020)
- [SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers](https://mlsanity.com/paper/2105.15203/) (2021)
- [LISA++: An Improved Baseline for Reasoning Segmentation with Large Language Model](https://mlsanity.com/paper/2312.17240/) (2023)
- [DAWN: Domain-Adaptive Weakly Supervised Nuclei Segmentation via Cross-Task Interactions](https://mlsanity.com/paper/2404.14956/) (2024)
- [SEINE: Structure Encoding and Interaction Network for Nuclei Instance Segmentation](https://mlsanity.com/paper/2401.09773/) (2024)
- [Understanding the Tricks of Deep Learning in Medical Image Segmentation: Challenges and Future Directions](https://mlsanity.com/paper/2209.10307/) (2022)
- [Boundary-aware Contrastive Learning for Semi-supervised Nuclei Instance Segmentation](https://mlsanity.com/paper/2402.04756/) (2024)
- [H2ASeg: Hierarchical Adaptive Interaction and Weighting Network for Tumor Segmentation in PET/CT Images](https://mlsanity.com/paper/2403.18339/) (2024)

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

- [MedAgent-Pro: Towards Evidence-based Multi-modal Medical Diagnosis via Reasoning Agentic Workflow](https://mlsanity.com/paper/2503.18968/) (2025)
- [Spatio-Temporal Representation Decoupling and Enhancement for Federated Instrument Segmentation in Surgical Videos](https://mlsanity.com/paper/2506.23759/) (2025)
- [Category Prompt Mamba Network for Nuclei Segmentation and Classification](https://mlsanity.com/paper/2503.10422/) (2025)
- [LG-NuSegHop: A Local-to-Global Self-Supervised Pipeline For Nuclei Instance Segmentation](https://mlsanity.com/paper/2511.04892/) (2025)

## Abstract (arXiv)

Deep learning has achieved impressive results in nuclei segmentation, but the massive requirement for pixel-wise labels remains a significant challenge. To alleviate the annotation burden, existing methods generate pseudo masks for model training using point labels. However, the generated masks are inevitably different from the ground truth, and these dissimilarities are not handled reasonably during the network training, resulting in the subpar performance of the segmentation model. To tackle this issue, we propose a framework named DoNuSeg, enabling Dynamic pseudo label Optimization in point-supervised Nuclei Segmentation. Specifically, DoNuSeg takes advantage of class activation maps (CAMs) to adaptively capture regions with semantics similar to annotated points. To leverage semantic diversity in the hierarchical feature levels, we design a dynamic selection module to choose the optimal one among CAMs from different encoder blocks as pseudo masks. Meanwhile, a CAM-guided contrastive module is proposed to further enhance the accuracy of pseudo masks. In addition to exploiting the semantic information provided by CAMs, we consider location priors inherent to point labels, developing a task-decoupled structure for effectively differentiating nuclei. Extensive experiments demonstrate that DoNuSeg outperforms state-of-the-art point-supervised methods. The code is available at https://github.com/shinning0821/MICCAI24-DoNuSeg.