Autoadaptive Medical Segment Anything Model

• URL: http://arxiv.org/abs/2507.01828v1
• Date: Wed, 02 Jul 2025 15:44:32 GMT
• Title: Autoadaptive Medical Segment Anything Model
• Authors: Tyler Ward, Meredith K. Owen, O'Kira Coleman, Brian Noehren, Abdullah-Al-Zubaer Imran,
• Abstract summary: We propose ADA-SAM (automated, domain-specific, and adaptive segment anything model), a novel multitask learning framework for medical image segmentation.<n>Our proposed method outperforms both fully-supervised and semi-supervised baselines by double digits in limited label settings.
• Score: 0.2359291431338925
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Medical image segmentation is a key task in the imaging workflow, influencing many image-based decisions. Traditional, fully-supervised segmentation models rely on large amounts of labeled training data, typically obtained through manual annotation, which can be an expensive, time-consuming, and error-prone process. This signals a need for accurate, automatic, and annotation-efficient methods of training these models. We propose ADA-SAM (automated, domain-specific, and adaptive segment anything model), a novel multitask learning framework for medical image segmentation that leverages class activation maps from an auxiliary classifier to guide the predictions of the semi-supervised segmentation branch, which is based on the Segment Anything (SAM) framework. Additionally, our ADA-SAM model employs a novel gradient feedback mechanism to create a learnable connection between the segmentation and classification branches by using the segmentation gradients to guide and improve the classification predictions. We validate ADA-SAM on real-world clinical data collected during rehabilitation trials, and demonstrate that our proposed method outperforms both fully-supervised and semi-supervised baselines by double digits in limited label settings. Our code is available at: https://github.com/tbwa233/ADA-SAM.

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