发布日期: 2025-11-05

更新日期: 2025-11-06

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2025-11-05 更新

Flip Learning: Weakly Supervised Erase to Segment Nodules in Breast Ultrasound

Authors:Yuhao Huang, Ao Chang, Haoran Dou, Xing Tao, Xinrui Zhou, Yan Cao, Ruobing Huang, Alejandro F Frangi, Lingyun Bao, Xin Yang, Dong Ni

Accurate segmentation of nodules in both 2D breast ultrasound (BUS) and 3D automated breast ultrasound (ABUS) is crucial for clinical diagnosis and treatment planning. Therefore, developing an automated system for nodule segmentation can enhance user independence and expedite clinical analysis. Unlike fully-supervised learning, weakly-supervised segmentation (WSS) can streamline the laborious and intricate annotation process. However, current WSS methods face challenges in achieving precise nodule segmentation, as many of them depend on inaccurate activation maps or inefficient pseudo-mask generation algorithms. In this study, we introduce a novel multi-agent reinforcement learning-based WSS framework called Flip Learning, which relies solely on 2D/3D boxes for accurate segmentation. Specifically, multiple agents are employed to erase the target from the box to facilitate classification tag flipping, with the erased region serving as the predicted segmentation mask. The key contributions of this research are as follows: (1) Adoption of a superpixel/supervoxel-based approach to encode the standardized environment, capturing boundary priors and expediting the learning process. (2) Introduction of three meticulously designed rewards, comprising a classification score reward and two intensity distribution rewards, to steer the agents’ erasing process precisely, thereby avoiding both under- and over-segmentation. (3) Implementation of a progressive curriculum learning strategy to enable agents to interact with the environment in a progressively challenging manner, thereby enhancing learning efficiency. Extensively validated on the large in-house BUS and ABUS datasets, our Flip Learning method outperforms state-of-the-art WSS methods and foundation models, and achieves comparable performance as fully-supervised learning algorithms.

二维乳腺超声（BUS）和三维自动乳腺超声（ABUS）中结节的精确分割对于临床诊断和治疗计划至关重要。因此，开发一种结节分割的自动化系统可以提高用户独立性并加快临床分析速度。与完全监督学习不同，弱监督分割（WSS）可以简化繁琐且复杂的注释过程。然而，当前的WSS方法在实现精确结节分割方面面临挑战，因为它们中的许多方法依赖于不准确的激活图或低效的伪掩膜生成算法。在这项研究中，我们引入了一种基于多智能体强化学习的新型弱监督分割框架，称为Flip Learning，它仅依赖于2D/3D框进行精确分割。具体来说，使用多个智能体从目标框中删除目标，以促进分类标签翻转，所删除的区域作为预测的分割掩膜。本研究的关键贡献如下：（1）采用基于超像素/超体素的方法对标准化环境进行编码，捕获边界先验并加速学习过程。（2）引入了三种精心设计的奖励，包括分类得分奖励和两种强度分布奖励，以精确引导智能体的擦除过程，从而避免欠分割和过分割。（3）实现了一种渐进的课程学习策略，使智能体能够以渐进的、具有挑战性的方式与环境进行交互，从而提高学习效率。在大型内部BUS和ABUS数据集上进行了广泛验证，我们的Flip Learning方法优于最新的WSS方法和基础模型，并且其性能与完全监督学习算法相当。

论文及项目相关链接

PDF Accepted by Medical Image Analysis. 24 pages, 13 figures, 20 tabels

Summary

该文本介绍了在二维乳腺超声（BUS）和三维自动乳腺超声（ABUS）中精确分割结节对于临床诊断和治疗计划的重要性。为此，开发一种自动化结节分割系统可以增强用户独立性并加速临床分析。本研究引入了一种基于多智能体强化学习的新型弱监督分割（WSS）框架，称为Flip Learning，该框架仅依赖二维/三维盒子进行精确分割。其通过利用多个智能体擦除目标来促使分类标签翻转，进而通过擦除区域作为预测分割掩膜。本研究的关键贡献包括：采用基于超像素/超体素的方法对标准化环境进行编码，以捕获边界先验并加速学习过程；引入三种精心设计的奖励来精确引导智能体的擦除过程，避免欠分割和过分割；实施渐进式课程学习策略，使智能体能够以渐进方式与环境互动，从而提高学习效率。经过在家庭BUS和ABUS数据集上的广泛验证，Flip Learning方法优于其他先进的WSS方法和基础模型，其性能与全监督学习算法相当。

Key Takeaways