Nodule segmentation from breast ultrasound images is challenging yet essential for the diagnosis. Weakly-supervised segmentation (WSS) can help reduce time-consuming and cumbersome manual annotation. Unlike existing weakly-supervised approaches, in this study, we propose a novel and general WSS framework called Flip Learning, which only needs the box annotation. Specifically, the target in the label box will be erased gradually to flip the classification tag, and the erased region will be considered as the segmentation result finally. Our contribution is three-fold. First, our proposed approach erases on superpixel level using a Multi-agent Reinforcement Learning framework to exploit the prior boundary knowledge and accelerate the learning process. Second, we design two rewards: classification score and intensity distribution reward, to avoid under- and over-segmentation, respectively. Third, we adopt a coarse-to-fine learning strategy to reduce the residual errors and improve the segmentation performance. Extensively validated on a large dataset, our proposed approach achieves competitive performance and shows great potential to narrow the gap between fully-supervised and weakly-supervised learning.

中文翻译：

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翻转学习：擦除以分段

乳房超声图像的结节分割具有挑战性，但对诊断至关重要。弱监督分割 (WSS) 可以帮助减少耗时且繁琐的手动注释。与现有的弱监督方法不同，在本研究中，我们提出了一种新颖且通用的 WSS 框架，称为翻转学习，它只需要框注释。具体来说，标签框中的目标会逐渐被擦除以翻转分类标签，最后将擦除的区域作为分割结果。我们的贡献是三方面的。首先，我们提出的方法使用多智能体强化学习框架在超像素级别擦除，以利用先验边界知识并加速学习过程。其次，我们设计了两个奖励：分类得分和强度分布奖励，分别避免欠分割和过分割。第三，我们采用由粗到细的学习策略来减少残差并提高分割性能。在大型数据集上进行了广泛验证，我们提出的方法取得了有竞争力的性能，并显示出缩小全监督学习和弱监督学习之间差距的巨大潜力。