面向稀疏标注 PET 脑疾病预测的跨域双流特征解耦
Cross-domain Dual-stream Feature Disentanglement for Brain Disorder Prediction with Sparsely Labeled PET
在 PET 标注稀疏时用 MRI 做跨模态域自适应，其「先显式解耦关键/非关键脑区、再分别做拓扑加权对齐与高置信融合」的差异化解耦思路，可迁移到任何源域充足、目标域标注稀缺的跨模态医学分类任务。
原文依据 we propose a Dual-Stream feature Disentanglement and Alignment (DSDA) framework designed for collaborative optimization of cross-modal domain adaptation
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论正电子发射断层扫描（PET）可用于多种脑疾病的早期诊断。然而，PET 扫描的标注需要专业核医学专家参与，导致精确标注的 PET 数据极为稀缺。基于 MRI 的跨模态域自适应方法能够在 PET 标注稀疏的情况下提升脑疾病分类准确率。但现有方法未能平衡跨模态任务中消除域差异与保留模态特有判别信息这两项核心需求。强制对齐往往会破坏两种模态各自的核心病理判别特征，难以满足跨模态脑疾病分类的协同优化需求。为此，我们提出双流特征解耦与对齐（DSDA）框架，用于跨模态域自适应与脑疾病分类的协同优化。该框架首先动态评估并显式地将与分类任务相关的关键脑区同保持脑结构完整性的非关键脑区解耦，随后对两类脑区施加差异化处理：对非关键脑区采用拓扑加权特征对齐，对关键脑区采用高置信特征融合。这种差异化处理使模型在有效对齐特征的同时保留关键判别信息。在多个数据集（如 ADNI、AIBL 和 PPMI）上的大量实验结果表明 DSDA 的有效性，并有助于达到最优性能。
英文原文摘要 1685 字符
Positron Emission Tomography (PET) can be used for the early diagnosis of various brain disorders. However, the annotation of PET scans requires the involvement of specialized nuclear medicine experts, making accurately annotated PET data extremely scarce. MRI-based cross-modal domain adaptation methods can improve the brain disorder classification accuracy with sparsely labeled PET data. However, existing methods fail to balance the core requirements of domain discrepancy elimination and modality-specific discriminative information retention in cross-modal tasks. Forced alignment often undermines the core pathological discriminative features of both modalities, making it difficult to meet the collaborative optimization demands of cross-modal brain disorder classification. To address this, we propose a Dual-Stream feature Disentanglement and Alignment (DSDA) framework designed for collaborative optimization of cross-modal domain adaptation and brain disorder classification. This framework first dynamically evaluates and explicitly decouples the critical brain regions relevant to the classification task from the non-critical regions that preserve brain structural integrity. It then applies differential processing to the two types of brain regions: topology-weighted feature alignment for non-critical regions and high-confidence feature fusion for critical regions. This differential processing ensures that the model effectively aligns features while preserving key discriminative information. Extensive experimental results on various datasets (e.g., ADNI, AIBL, and PPMI) demonstrate the effectiveness of DSDA which helps achieve the state-of-the-art performance.