  Biomedical image segmentation plays a significant role in computer-aided
diagnosis. However, existing CNN based methods rely heavily on massive manual
annotations, which are very expensive and require huge human resources. In this
work, we adopt a coarse-to-fine strategy and propose a self-supervised
correction learning paradigm for semi-supervised biomedical image segmentation.
Specifically, we design a dual-task network, including a shared encoder and two
independent decoders for segmentation and lesion region inpainting,
respectively. In the first phase, only the segmentation branch is used to
obtain a relatively rough segmentation result. In the second step, we mask the
detected lesion regions on the original image based on the initial segmentation
map, and send it together with the original image into the network again to
simultaneously perform inpainting and segmentation separately. For labeled
data, this process is supervised by the segmentation annotations, and for
unlabeled data, it is guided by the inpainting loss of masked lesion regions.
Since the two tasks rely on similar feature information, the unlabeled data
effectively enhances the representation of the network to the lesion regions
and further improves the segmentation performance. Moreover, a gated feature
fusion (GFF) module is designed to incorporate the complementary features from
the two tasks. Experiments on three medical image segmentation datasets for
different tasks including polyp, skin lesion and fundus optic disc segmentation
well demonstrate the outstanding performance of our method compared with other
semi-supervised approaches. The code is available at
https://github.com/ReaFly/SemiMedSeg.
