Neighbor Does Matter: Density-Aware Contrastive Learning for Medical Semi-supervised Segmentation
Journal:
arXiv
Published Date:
Dec 27, 2024
Abstract
In medical image analysis, multi-organ semi-supervised segmentation faces
challenges such as insufficient labels and low contrast in soft tissues. To
address these issues, existing studies typically employ semi-supervised
segmentation techniques using pseudo-labeling and consistency regularization.
However, these methods mainly rely on individual data samples for training,
ignoring the rich neighborhood information present in the feature space. In
this work, we argue that supervisory information can be directly extracted from
the geometry of the feature space. Inspired by the density-based clustering
hypothesis, we propose using feature density to locate sparse regions within
feature clusters. Our goal is to increase intra-class compactness by addressing
sparsity issues. To achieve this, we propose a Density-Aware Contrastive
Learning (DACL) strategy, pushing anchored features in sparse regions towards
cluster centers approximated by high-density positive samples, resulting in
more compact clusters. Specifically, our method constructs density-aware
neighbor graphs using labeled and unlabeled data samples to estimate feature
density and locate sparse regions. We also combine label-guided co-training
with density-guided geometric regularization to form complementary supervision
for unlabeled data. Experiments on the Multi-Organ Segmentation Challenge
dataset demonstrate that our proposed method outperforms state-of-the-art
methods, highlighting its efficacy in medical image segmentation tasks.
