TARDis: 用于不完整多模态肿瘤分割与分类的时间衰减表示解耦
TARDis: Time Attenuated Representation Disentanglement for Incomplete Multi-Modal Tumor Segmentation and Classification
AI总结：
TARDis通过时间衰减表示解耦框架，解决多模态肿瘤分割与分类中缺失模态问题，提升诊断精度并降低辐射暴露。
摘要：
在对比增强计算机断层扫描（CT）中肿瘤的准确诊断和分割本质上是由对比剂随时间变化的血流动力学特征驱动的。然而，在实际临床实践中，完整的时间动态往往难以通过严格的辐射剂量限制和不同机构之间不一致的采集协议来捕捉，导致普遍存在的缺失模态问题。现有的深度学习方法通常将缺失的阶段视为缺失的独立通道，忽略了血流动力学的固有时间连续性。在本工作中，我们提出了时间衰减表示解耦（TARDis），一种新的物理意识框架，将缺失的模态重新定义为连续时间衰减曲线上的缺失样本点。我们首先假设潜在特征可以解耦为时间不变的静态组件（解剖结构）和时间依赖的动态组件（灌注）。我们通过双路径架构实现这一点：一个基于量化路径使用可学习嵌入字典提取一致的解剖结构，另一个基于概率路径使用血流动力学条件变分自动编码器来建模动态增强，该模型基于估计的扫描时间进行建模。这种设计使网络能够通过从学习的潜在分布中采样来推断缺失的血流动力学特征。在大规模多模态私有腹部CT数据集（2282名患者）和两个公开数据集上的广泛实验表明，TARDis显著优于最先进的不完整模态框架。值得注意的是，我们的方法在极端数据稀疏情况下仍能保持稳健的诊断性能，突显了其在减少辐射暴露的同时维持诊断精度的潜力。
英文摘要：
The accurate diagnosis and segmentation of tumors in contrast-enhanced Computed Tomography (CT) are fundamentally driven by the distinctive hemodynamic profiles of contrast agents over time. However, in real-world clinical practice, complete temporal dynamics are often hard to capture by strict radiation dose limits and inconsistent acquisition protocols across institutions, leading to a prevalent missing modality problem. Existing deep learning approaches typically treat missing phases as absent independent channels, ignoring the inherent temporal continuity of hemodynamics. In this work, we propose Time Attenuated Representation Disentanglement (TARDis), a novel physics-aware framework that redefines missing modalities as missing sample points on a continuous Time-Attenuation Curve. We first hypothesize that the latent feature can be disentangled into a time-invariant static component (anatomy) and a time-dependent dynamic component (perfusion). We achieve this via a dual-path architecture: a quantization-based path using a learnable embedding dictionary to extract consistent anatomical structures, and a probabilistic path using a Hemodynamic Conditional Variational Autoencoder to model dynamic enhancement conditioned on the estimated scan time. This design allows the network to infer missing hemodynamic features by sampling from the learned latent distribution. Extensive experiments on a large-scale multi-modal private abdominal CT dataset (2,282 patients) and two public datasets demonstrate that TARDis significantly outperforms state-of-the-art incomplete modality frameworks. Notably, our method maintains robust diagnostic performance even in extreme data-sparsity scenarios, highlighting its potential for reducing radiation exposure while maintaining diagnostic precision.
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