通过旋转隐空间分离生物与技术因素，提升医学影像中疾病模式发现能力
Disentanglement of Biological and Technical Factors via Latent Space Rotation in Clinical Imaging Improves Disease Pattern Discovery
- 后处理旋转隐空间，主动学习并分离成像技术带来的域偏移
- 在真实多中心数据上，聚类一致性提升19.01%（ARI）、16.85%（NMI）、12.39%（Dice）
- 无需标注即可发现稳定组织类型模式，适合多中心影像数据的生物标志物挖掘
利用机器学习从医学影像中识别新疾病模式可拓展可识别病灶的语汇，支持诊断与预后评估。然而，图像外观不仅受生物学差异影响，也受厂商、扫描及重建参数等技术因素干扰，导致域偏移，阻碍数据表示学习和有意义聚类的发现。为此，我们提出一种通过后处理隐空间旋转主动学习域偏移的方法，实现生物与技术因素的解耦。在真实世界异构临床数据上的结果表明，所学解耦表示可在不同采集条件下稳定生成代表组织类型的聚类。相较于纠缠表示，聚类一致性分别提升19.01%（ARI）、16.85%（NMI）和12.39%（Dice），优于四种先进归一化方法。利用这些聚类量化特发性肺纤维化患者的组织成分，显著提升Cox生存预测性能。这表明该无标签框架能有效促进多中心常规影像数据中的生物标志物发现。代码已开源：https://github.com/cirmuw/latent-space-rotation-disentanglement。
原文摘要 · Abstract (English)
Identifying new disease-related patterns in medical imaging data with the help of machine learning enlarges the vocabulary of recognizable findings. This supports diagnostic and prognostic assessment. However, image appearance varies not only due to biological differences, but also due to imaging technology linked to vendors, scanning- or re- construction parameters. The resulting domain shifts impedes data representation learning strategies and the discovery of biologically meaningful cluster appearances. To address these challenges, we introduce an approach to actively learn the domain shift via post-hoc rotation of the data latent space, enabling disentanglement of biological and technical factors. Results on real-world heterogeneous clinical data showcase that the learned disentangled representation leads to stable clusters representing tissue-types across different acquisition settings. Cluster consistency is improved by +19.01% (ARI), +16.85% (NMI), and +12.39% (Dice) compared to the entangled representation, outperforming four state-of-the-art harmonization methods. When using the clusters to quantify tissue composition on idiopathic pulmonary fibrosis patients, the learned profiles enhance Cox survival prediction. This indicates that the proposed label-free framework facilitates biomarker discovery in multi-center routine imaging data. Code is available on GitHub https://github.com/cirmuw/latent-space-rotation-disentanglement.
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