GCL: Gradient-Guided Contrastive Learning for Medical Image Segmentation
  with Multi-Perspective Meta Labels
Yixuan Wu Jintai Chen Jiahuan Yan Yiheng Zhu Danny Z. Chen Jian Wu
Since annotating medical images for segmentation tasks commonly incurs
expensive costs, it is highly desirable to design an annotation-efficient
method to alleviate the annotation burden. Recently, contrastive learning has
exhibited a great potential in learning robust representations to boost
downstream tasks with limited labels. In medical imaging scenarios, ready-made
meta labels (i.e., specific attribute information of medical images) inherently
reveal semantic relationships among images, which have been used to define
positive pairs in previous work. However, the multi-perspective semantics
revealed by various meta labels are usually incompatible and can incur
intractable "semantic contradiction" when combining different meta labels. In
this paper, we tackle the issue of "semantic contradiction" in a
gradient-guided manner using our proposed Gradient Mitigator method, which
systematically unifies multi-perspective meta labels to enable a pre-trained
model to attain a better high-level semantic recognition ability. Moreover, we
emphasize that the fine-grained discrimination ability is vital for
segmentation-oriented pre-training, and develop a novel method called Gradient
Filter to dynamically screen pixel pairs with the most discriminating power
based on the magnitude of gradients. Comprehensive experiments on four medical
image segmentation datasets verify that our new method GCL: (1) learns
informative image representations and considerably boosts segmentation
performance with limited labels, and (2) shows promising generalizability on
out-of-distribution datasets.