  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.
