Semantic-Aware Contrastive Learning for Multi-Object Medical Image Segmentation.

ABSTRACT: Medical image segmentation, or computing voxel-wise semantic masks, is a fundamental yet challenging task in medical imaging domain. To increase the ability of encoder-decoder neural networks to perform this task across large clinical cohorts, contrastive learning provides an opportunity to stabilize model initialization and enhances downstream tasks performance without ground-truth voxel-wise labels. However, multiple target objects with different semantic meanings and contrast level may exist in a single image, which poses a problem for adapting traditional contrastive learning methods from prevalent "image-level classification" to "pixel-level segmentation". In this article, we propose a simple semantic-aware contrastive learning approach leveraging attention masks and image-wise labels

SUBMITTER: Lee HH 

PROVIDER: S-EPMC10524443 | biostudies-literature | 2023 Sep

REPOSITORIES: biostudies-literature