Domain and Task-Focused Example Selection for Data-Efficient Contrastive Medical Image Segmentation

• URL: http://arxiv.org/abs/2505.19208v1
• Date: Sun, 25 May 2025 16:11:48 GMT
• Title: Domain and Task-Focused Example Selection for Data-Efficient Contrastive Medical Image Segmentation
• Authors: Tyler Ward, Aaron Moseley, Abdullah-Al-Zubaer Imran,
• Abstract summary: We propose a novel self-supervised contrastive learning framework for medical image segmentation, dubbed PolyCL.<n>PolyCL learns and transfers context-aware discriminant features useful for segmentation from an innovative surrogate.<n>We show that PolyCL outperforms fully-supervised and self-supervised baselines in both low-data and cross-domain scenarios.
• Score: 0.2765106384328772
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Segmentation is one of the most important tasks in the medical imaging pipeline as it influences a number of image-based decisions. To be effective, fully supervised segmentation approaches require large amounts of manually annotated training data. However, the pixel-level annotation process is expensive, time-consuming, and error-prone, hindering progress and making it challenging to perform effective segmentations. Therefore, models must learn efficiently from limited labeled data. Self-supervised learning (SSL), particularly contrastive learning via pre-training on unlabeled data and fine-tuning on limited annotations, can facilitate such limited labeled image segmentation. To this end, we propose a novel self-supervised contrastive learning framework for medical image segmentation, leveraging inherent relationships of different images, dubbed PolyCL. Without requiring any pixel-level annotations or unreasonable data augmentations, our PolyCL learns and transfers context-aware discriminant features useful for segmentation from an innovative surrogate, in a task-related manner. Additionally, we integrate the Segment Anything Model (SAM) into our framework in two novel ways: as a post-processing refinement module that improves the accuracy of predicted masks using bounding box prompts derived from coarse outputs, and as a propagation mechanism via SAM 2 that generates volumetric segmentations from a single annotated 2D slice. Experimental evaluations on three public computed tomography (CT) datasets demonstrate that PolyCL outperforms fully-supervised and self-supervised baselines in both low-data and cross-domain scenarios. Our code is available at https://github.com/tbwa233/PolyCL.

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