医学超声图像分割中的多尺度切换半监督与对比学习框架

摘要

医学超声图像分割面临标记数据有限及固有成像伪影（包括斑点噪声和低对比度边界）带来的重大挑战。虽然半监督学习方法已涌现以应对数据稀缺问题，但现有方法存在未标记数据利用欠佳且缺乏鲁棒特征表示机制的问题。本文提出Switch这一新型半监督学习框架，其具备两项核心创新：(1)采用分层块混合实现均匀空间覆盖的多尺度切换策略；(2)在傅里叶空间通过幅度切换执行对比学习的频域切换策略。本框架将上述组件集成于师生架构中，以有效利用标记与未标记数据。在六个多样化超声数据集（淋巴结、乳腺病灶、甲状腺结节和前列腺）上的综合评估表明，该方法始终优于现有最优方法。在5%标记比例下，Switch实现显著提升：LN-INT数据集Dice系数达80.04%，DDTI数据集达85.52%，前列腺数据集达83.48%，其半监督性能甚至超越全监督基线。该方法在保持参数效率（180万参数）的同时提供卓越性能，验证了其在资源受限的医学影像应用中的有效性。源代码已公开于https://github.com/jinggqu/Switch。

English

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

医学超声图像分割中的多尺度切换半监督与对比学习框架

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

摘要

Support