Localized Region Contrast for Enhancing Self-Supervised Learning in Medical Image Segmentation
This work addresses the challenge of learning fine-grained representations for dense prediction tasks in medical imaging, where labeled data is scarce, offering an incremental improvement over existing methods.
The paper tackles the problem of self-supervised learning for medical image segmentation by proposing a Localized Region Contrast framework, which improves segmentation performance in limited annotation settings, as demonstrated on three multi-organ datasets with significant gains.
Recent advancements in self-supervised learning have demonstrated that effective visual representations can be learned from unlabeled images. This has led to increased interest in applying self-supervised learning to the medical domain, where unlabeled images are abundant and labeled images are difficult to obtain. However, most self-supervised learning approaches are modeled as image level discriminative or generative proxy tasks, which may not capture the finer level representations necessary for dense prediction tasks like multi-organ segmentation. In this paper, we propose a novel contrastive learning framework that integrates Localized Region Contrast (LRC) to enhance existing self-supervised pre-training methods for medical image segmentation. Our approach involves identifying Super-pixels by Felzenszwalb's algorithm and performing local contrastive learning using a novel contrastive sampling loss. Through extensive experiments on three multi-organ segmentation datasets, we demonstrate that integrating LRC to an existing self-supervised method in a limited annotation setting significantly improves segmentation performance. Moreover, we show that LRC can also be applied to fully-supervised pre-training methods to further boost performance.