ScribblePolyp: Scribble-Supervised Polyp Segmentation through Dual
  Consistency Alignment
Zixun Zhang Yuncheng Jiang Jun Wei Hannah Cui Zhen Li
Automatic polyp segmentation models play a pivotal role in the clinical
diagnosis of gastrointestinal diseases. In previous studies, most methods
relied on fully supervised approaches, necessitating pixel-level annotations
for model training. However, the creation of pixel-level annotations is both
expensive and time-consuming, impeding the development of model generalization.
In response to this challenge, we introduce ScribblePolyp, a novel
scribble-supervised polyp segmentation framework. Unlike fully-supervised
models, ScribblePolyp only requires the annotation of two lines (scribble
labels) for each image, significantly reducing the labeling cost. Despite the
coarse nature of scribble labels, which leave a substantial portion of pixels
unlabeled, we propose a two-branch consistency alignment approach to provide
supervision for these unlabeled pixels. The first branch employs transformation
consistency alignment to narrow the gap between predictions under different
transformations of the same input image. The second branch leverages affinity
propagation to refine predictions into a soft version, extending additional
supervision to unlabeled pixels. In summary, ScribblePolyp is an efficient
model that does not rely on teacher models or moving average pseudo labels
during training. Extensive experiments on the SUN-SEG dataset underscore the
effectiveness of ScribblePolyp, achieving a Dice score of 0.8155, with the
potential for a 1.8% improvement in the Dice score through a straightforward
self-training strategy.