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

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

Figures (10)

• Figure 1: A comparison of model performance between previous semi-supervised SOTAs and the proposed Switch method on the lymph node dataset (LN-INT). Our method achieved the best results across five data labeling ratios (i.e. models were trained using only part of the ground truth).
• Figure 2: Overview of our proposed method. $f_\mathrm{s}$ and $f_\mathrm{t}$ are the student and teacher network, respectively. Stage 1: The system splits labeled data into two subsets ($x_1$, $x_2$) and unlabeled data into two subsets ($u_1$, $u_2$), generates pseudo-labels for unlabeled images using $f_\mathrm{t}$, creates spatial masks for patch-based mixing, and combines unlabeled base images with labeled patches to create mixed training samples for semi-supervised learning. Stage 2: The system performs amplitude switching between labeled and unlabeled image batches in the frequency domain, creates new mixed images using the same spatial masks from MSS, and applies CL to align feature representations between original and frequency-switched mixed images, while enforcing consistency regularization between their model predictions to enhance semi-supervised learning robustness. More details of MSS and FDS modules can be found in \ref{['fig:mss']} and \ref{['fig:fds']}.
• Figure 3: Overview of multiscale switch mechanism. $\mathcal{M}$ represents the binary mask with $p$ coarse patches and $q$ fine patches, while $\mathbin{\sim}\mathcal{M}$ refers to its complement. The white areas in both $\mathcal{M}$ and $\mathbin{\sim}\mathcal{M}$ stand for True, and the pixel at the corresponding position will be retained after multiplication. Vice versa for black regions.
• Figure 4: Overview of frequency domain switch approach. $x$ and $u$ are decomposed into amplitude ($A_x, A_u$) and phase ($P_x, P_u$) domain by fast Fourier transform ($\mathcal{F}$). The frequency region ($\mathcal{R}$) in amplitude domain is switched between $A_x$ and $A_u$, and then the pair of $x^r$ and $u^r$ are reconstructed by inverse fast Fourier transform ($\mathcal{F}^{-1}$). The relative difference of ($x, x^r$) and ($u, u^r$) made by FDS is shown in the dotted box.
• Figure 5: A comparison of IoU values of the proposed method and SOTAs on six datasets with five data labeling ratios. The maximum and minimum IoU values obtained by these methods for each dataset are marked in italics. All labeling ratios are displayed in the five corresponding corners of the radar charts.