Multiscale Switch for Semi-Supervised and Contrastive Learning in Medical Ultrasound Image Segmentation

• URL: http://arxiv.org/abs/2603.18655v1
• Date: Thu, 19 Mar 2026 09:20:37 GMT
• Title: Multiscale Switch for Semi-Supervised and Contrastive Learning in Medical Ultrasound Image Segmentation
• Abstract summary: Medical ultrasound image segmentation faces significant challenges due to limited labeled data and characteristic imaging artifacts.<n>We propose Switch, a novel SSL framework with two key innovations.<n>Our framework integrates these components within a teacher-student architecture to effectively leverage both labeled and unlabeled data.
• Score: 9.346812835233482
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Medical ultrasound image segmentation faces significant challenges due to limited labeled data and characteristic imaging artifacts including speckle noise and low-contrast boundaries. While semi-supervised learning (SSL) approaches have emerged to address data scarcity, existing methods suffer from suboptimal unlabeled data utilization and lack robust feature representation mechanisms. In this paper, we propose Switch, a novel SSL framework with two key innovations: (1) Multiscale Switch (MSS) strategy that employs hierarchical patch mixing to achieve uniform spatial coverage; (2) Frequency Domain Switch (FDS) with contrastive learning that performs amplitude switching in Fourier space for robust feature representations. Our framework integrates these components within a teacher-student architecture to effectively leverage both labeled and unlabeled data. Comprehensive evaluation across six diverse ultrasound datasets (lymph nodes, breast lesions, thyroid nodules, and prostate) demonstrates consistent superiority over state-of-the-art methods. At 5\% labeling ratio, Switch achieves remarkable improvements: 80.04\% Dice on LN-INT, 85.52\% Dice on DDTI, and 83.48\% Dice on Prostate datasets, with our semi-supervised approach even exceeding fully supervised baselines. The method maintains parameter efficiency (1.8M parameters) while delivering superior performance, validating its effectiveness for resource-constrained medical imaging applications. The source code is publicly available at https://github.com/jinggqu/Switch

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