Chen Yixin, Wang Yan, Xie Zhaoheng
Institute of Medical Technology and National Biomedical Imaging Center, Peking University, Beijing 100191, China.
School of Instrumentation and Optoelectronic Engineering, and State Key Laboratory of Software Development Environment, Beihang University, Beijing 100191, China.
Health Data Sci. 2025 Jan 7;5:0143. doi: 10.34133/hds.0143. eCollection 2025.
Source-free unsupervised domain adaptation (SFUDA) methods aim to address the challenge of domain shift while preserving data privacy. Existing SFUDA approaches construct reliable and confident pseudo-labels for target-domain data through denoising methods, thereby guiding the training of the target-domain model. The effectiveness of denoising approaches is influenced by the degree of domain gap between the source and target domains. A marked shift can cause the pseudo-labels to be unreliable, even after applying denoising. We propose a novel 2-stage framework for SFUDA called visual prompt source-free domain adaptation (VP-SFDA). We propose input-specific visual prompt in the first stage, prompting process, which bridges the target-domain data to source-domain distribution. Our method utilizes visual prompts and batch normalization constraint to enable the alignment model to learn domain-specific knowledge and align the target-domain data with the source-domain contribution. The second stage is the adaptation process, which aims at optimizing the segmentation model from the source domain to the target domain. This is accomplished through the denoising techniques, ultimately enhancing the performance. Our study presents a comparative analysis of several SFUDA techniques in the VP-SFDA framework across 4 tasks: abdominal magnetic resonance imaging (MRI) to computed tomography (CT), abdominal CT to MRI, cardiac MRI to CT, and cardiac CT to MRI. Notably, in the abdominal MRI to CT adaptation task, the VP-OS method achieved a remarkable improvement, increasing the average DICE score from 0.658 to 0.773 ( 0.01) and reducing the average surface distance (ASD) from 3.489 to 2.961 ( 0.01). Similarly, the VP-LD and VP-DPL methods also showed significant improvements over their base algorithms in both abdominal and cardiac MRI to CT tasks. This paper proposes VP-SFDA, a novel 2-stage framework for SFUDA in medical imaging, which achieves superior performance through input-specific visual prompts and batch normalization constraint for domain adaptation, coupled with denoising methods for enhanced results. Comparative experiments on 4 medical SFUDA tasks demonstrate that VO-SFDA surpasses existing methods, with ablation studies confirming the benefits of domain-specific patterns.
无源无监督域适应（SFUDA）方法旨在解决域转移挑战的同时保护数据隐私。现有的SFUDA方法通过去噪方法为目标域数据构建可靠且可信的伪标签，从而指导目标域模型的训练。去噪方法的有效性受源域和目标域之间域差距程度的影响。显著的转移可能导致即使应用去噪后伪标签也不可靠。我们提出了一种用于SFUDA的新颖两阶段框架，称为视觉提示无源域适应（VP-SFDA）。我们在第一阶段即提示过程中提出特定于输入的视觉提示，它将目标域数据与源域分布联系起来。我们的方法利用视觉提示和批归一化约束，使对齐模型能够学习特定于域的知识，并使目标域数据与源域贡献对齐。第二阶段是适应过程，其目的是将分割模型从源域优化到目标域。这通过去噪技术来实现，最终提高性能。我们的研究在VP-SFDA框架中针对4项任务对几种SFUDA技术进行了比较分析：腹部磁共振成像（MRI）到计算机断层扫描（CT）、腹部CT到MRI、心脏MRI到CT以及心脏CT到MRI。值得注意的是，在腹部MRI到CT的适应任务中，VP-OS方法取得了显著改进，平均DICE分数从0.658提高到0.773（±0.01），平均表面距离（ASD）从3.489降低到2.961（±0.01）。同样，在腹部和心脏MRI到CT任务中，VP-LD和VP-DPL方法相对于其基础算法也显示出显著改进。本文提出了VP-SFDA，这是一种用于医学成像中SFUDA的新颖两阶段框架，它通过特定于输入的视觉提示和用于域适应的批归一化约束以及用于增强结果的去噪方法实现了卓越性能。在4项医学SFUDA任务上的对比实验表明，VO-SFDA超越了现有方法，消融研究证实了特定于域模式的益处。
