DAWN: Domain-Adaptive Weakly Supervised Nuclei Segmentation via Cross-Task Interactions

TL;DR

This paper presents DAWN, a domain-adaptive weakly supervised nuclei segmentation framework that leverages cross-task interactions and pseudo-label optimization to improve segmentation accuracy with limited annotations.

Contribution

It introduces a novel domain-adaptive framework using cross-task interactions and pseudo-label optimization for weakly supervised nuclei segmentation.

Findings

Outperforms existing weakly supervised methods on six datasets.

Achieves comparable or better results than fully supervised approaches.

Effective domain adaptation with limited annotated data.

Abstract

Weakly supervised segmentation methods have gained significant attention due to their ability to reduce the reliance on costly pixel-level annotations during model training. However, the current weakly supervised nuclei segmentation approaches typically follow a two-stage pseudo-label generation and network training process. The performance of the nuclei segmentation heavily relies on the quality of the generated pseudo-labels, thereby limiting its effectiveness. This paper introduces a novel domain-adaptive weakly supervised nuclei segmentation framework using cross-task interaction strategies to overcome the challenge of pseudo-label generation. Specifically, we utilize weakly annotated data to train an auxiliary detection task, which assists the domain adaptation of the segmentation network. To enhance the efficiency of domain adaptation, we design a consistent feature constraint module integrating prior knowledge from the source domain. Furthermore, we develop pseudo-label optimization and interactive training methods to improve the domain transfer capability. To validate the effectiveness of our proposed method, we conduct extensive comparative and ablation experiments on six datasets. The results demonstrate the superiority of our approach over existing weakly supervised approaches. Remarkably, our method achieves comparable or even better performance than fully supervised methods. Our code will be released in https://github.com/zhangye-zoe/DAWN.