  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.
