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 this https URL.
Abstract (translated)
弱监督分割方法因其在模型训练过程中减少对昂贵像素级注释的依赖而受到广泛关注。然而，当前的弱监督核分割方法通常遵循两个阶段的伪标签生成和网络训练过程。核分割的表现很大程度上取决于生成的伪标签的质量，从而限制了其有效性的提高。本文提出了一种使用跨任务交互策略的新颖领域自适应弱监督核分割框架，以克服伪标签生成的挑战。具体来说，我们利用弱标注数据来训练辅助检测任务，从而帮助分割网络的领域适应。为了提高领域适应的效率，我们设计了一个一致的特征约束模块，整合了源域的知识。此外，我们还开发了伪标签优化和交互训练方法，以提高领域转移能力。为了验证我们提出的方法的有效性，我们在六个数据集上进行了广泛的比较和消融实验。结果表明，与现有弱监督方法相比，我们的方法具有优越性。值得注意的是，我们的方法甚至可能实现与完全监督方法相媲美的或更好的性能。我们的代码将在此处发布：https://URL。
URL
https://arxiv.org/abs/2404.14956
