发表机构
Korea Institute of Oriental Medicine; Islamic University of Technology（韩国韩医学研究院; 伊斯兰科技大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对医学成像中跨模态知识转移时获取配对数据难、处理模态差距及噪声传播问题，提出UnDA框架，通过与主干无关的对齐模块、不确定性加权最优传输及每个类别的ProtoNCE目标，实现无配对跨模态蒸馏，提升目标模态分割精度。
AI 中文摘要
基于多模态的方法在下游任务中通常优于单模态方法，因为不同模态提供互补信息，但获取配对临床数据在现实场景中仍是重大挑战。虽然跨模态知识蒸馏解决了这一问题，但现有方法在处理大模态差距和不确定源域预测的噪声传播方面存在困难。为克服这些挑战，我们提出了UnDA，这是一个用于无配对跨模态蒸馏的锚点引导框架。我们的方法引入了一个与主干无关的对齐模块，通过基于注意力的池化机制提取语义结构化的类令牌。为确保稳健的知识转移，我们提出了不确定性加权最优传输（UCT-OT），它基于预测置信度动态加权特征级对齐，有效抑制噪声监督。此外，每个类别的ProtoNCE目标维持稳定的原型记忆，以在无配对批次中强制全局可辨别性。在严格无配对设置下对代表性分割任务的评估表明，目标模态的准确性和边界精度持续提高，证明了无需配对数据集即可在异构数据源之间转移有意义的结构知识。
英文摘要
Multimodal based approaches often outperform single modality approaches in downstream tasks as the different modalities provide complementary information, yet acquiring paired clinical data remains a significant challenge in real world scenarios. While cross-modal knowledge distillation addresses this, existing methods often struggle with large modality gaps and the propagation of noise from uncertain source-domain predictions. To overcome these challenges, we propose UnDA, an anchor-guided framework for unpaired cross-modal distillation. Our approach introduces a backbone-agnostic Alignment Module that extracts semantically structured class tokens via an attention based pooling mechanism. To ensure robust knowledge transfer, we propose Uncertainty-Weighted Optimal Transport (UCT-OT), which dynamically weights feature-level alignment based on prediction confidence, effectively suppressing noisy supervision. Furthermore, a per-class ProtoNCE objective maintains stable prototype memories to enforce global discriminability across unpaired batches. Evaluations on representative segmentation tasks under strictly unpaired settings show consistent improvements in accuracy and boundary precision in the target modality, demonstrating that meaningful structural knowledge can be transferred across heterogeneous data sources without paired datasets.
