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

2024-04-23

IEEE transactions on circuits and systems for video technology (Print)

AI Summary

To address low-quality pseudo-labels and poor cross-domain generalization in weakly supervised nucleus segmentation, this paper proposes an end-to-end cross-task interactive domain adaptation framework. Methodologically: (1) it introduces the first detection-assisted segmentation mechanism with cross-task feature interaction to jointly enhance localization accuracy and boundary modeling; (2) it designs a source-prior-guided consistency constraint to improve feature-level domain invariance; and (3) it incorporates dynamic pseudo-label optimization coupled with interactive joint training to mitigate error accumulation. Evaluated on six histopathological image datasets, the method significantly outperforms existing weakly supervised approaches and achieves performance on par with—or even surpassing—that of fully supervised baselines. These results validate its robust domain adaptability and strong generalization capability across diverse pathological domains.

Technology Category

• Machine Learning: Transfer, Domain Adaptation, Multi-Task Learning
• Computer Vision: Segmentation
• Search and Optimization: Mixed Discrete/Continuous Search

Application Category

• Search and Retrieval-Augmented AI: Vertical and domain-specific search
• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods
• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs

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.

Problem

• Weakly Supervised Learning
• Nuclear Segmentation
• Pseudo Label Accuracy

Innovation

• DAWN
• Cross-task Interaction Strategy
• Weak Supervision