WeakPolyp: You Only Look Bounding Box for Polyp Segmentation
Jun Wei Yiwen Hu Shuguang Cui S. Kevin Zhou Zhen Li
Limited by expensive pixel-level labels, polyp segmentation models are
plagued by data shortage and suffer from impaired generalization. In contrast,
polyp bounding box annotations are much cheaper and more accessible. Thus, to
reduce labeling cost, we propose to learn a weakly supervised polyp
segmentation model (i.e., WeakPolyp) completely based on bounding box
annotations. However, coarse bounding boxes contain too much noise. To avoid
interference, we introduce the mask-to-box (M2B) transformation. By supervising
the outer box mask of the prediction instead of the prediction itself, M2B
greatly mitigates the mismatch between the coarse label and the precise
prediction. But, M2B only provides sparse supervision, leading to non-unique
predictions. Therefore, we further propose a scale consistency (SC) loss for
dense supervision. By explicitly aligning predictions across the same image at
different scales, the SC loss largely reduces the variation of predictions.
Note that our WeakPolyp is a plug-and-play model, which can be easily ported to
other appealing backbones. Besides, the proposed modules are only used during
training, bringing no computation cost to inference. Extensive experiments
demonstrate the effectiveness of our proposed WeakPolyp, which surprisingly
achieves a comparable performance with a fully supervised model, requiring no
mask annotations at all.