通过分阶段去噪混合，提升医疗图像分割在无源域适应下的精度与鲁棒性
Aligning What You Separate: Denoised Patch Mixing for Source-Free Domain Adaptation in Medical Image Segmentation
- 按样本难易度分离可靠与不可靠图像，从简单样本开始逐步适应
- 用蒙特卡洛去噪掩码优化伪标签，有效抑制噪声像素影响
- 跨域混合可靠区域特征，提升边界分割准确率，适合医学影像应用
在隐私约束下，无源域适应（SFDA）正成为医疗图像分割的有力方案，但现有方法常忽略样本难易度，在域偏移下难以应对噪声监督。本文提出新框架，结合难样本选择与去噪块混合，渐进式对齐目标域分布。首先，基于熵-相似性分析将未标注图像分为可靠与不可靠子集，使适应从简单样本开始，逐步引入更难样本；其次，通过基于蒙特卡洛的去噪掩码精炼伪标签，抑制不可靠像素，稳定训练过程；最后，利用域内与跨域目标混合不同子集的图像块，传递可靠语义并缓解噪声影响。在基准数据集上的实验显示，该方法优于现有SFDA与UDA方法，显著提升边界分割精度，实现最先进的Dice与ASSD指标。研究强调了渐进式适应与去噪监督在域偏移下实现鲁棒分割的重要性。
原文摘要 · Abstract (English)
Source-Free Domain Adaptation (SFDA) is emerging as a compelling solution for medical image segmentation under privacy constraints, yet current approaches often ignore sample difficulty and struggle with noisy supervision under domain shift. We present a new SFDA framework that leverages Hard Sample Selection and Denoised Patch Mixing to progressively align target distributions. First, unlabeled images are partitioned into reliable and unreliable subsets through entropy-similarity analysis, allowing adaptation to start from easy samples and gradually incorporate harder ones. Next, pseudo-labels are refined via Monte Carlo-based denoising masks, which suppress unreliable pixels and stabilize training. Finally, intra- and inter-domain objectives mix patches between subsets, transferring reliable semantics while mitigating noise. Experiments on benchmark datasets show consistent gains over prior SFDA and UDA methods, delivering more accurate boundary delineation and achieving state-of-the-art Dice and ASSD scores. Our study highlights the importance of progressive adaptation and denoised supervision for robust segmentation under domain shift.
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