通过空间证据与临床先验耦合，提升胸部X光多标签分类精度。
C$^2$A: Coupling Spatial Evidence with Clinical Priors via Co-occurrence Aware Class Attention for Multi-Label Chest X-Ray Classification
- 基于每类疾病的空间注意力图生成局部特征描述符。
- 利用可学习图融合共现关系，单步消息传递提升相关病灶判别力。
- 在共现强且空间证据模糊的病种上表现突出，如肺不张提升1.5分。
胸腔病变很少孤立出现，但标准多标签分类器依赖共享全局描述符，忽略病灶位置及共现模式。本文提出C²A（共现感知类别注意力）分类头，显式耦合空间证据与临床先验。首先，将池化操作建模为每类学习到的空间注意力图的期望，生成各疾病的局部特征描述符。其次，通过从经验性标签共现数据初始化的可学习图，耦合这些描述符；仅需一次残差消息传递步骤，在相关发现间共享证据，其对每个得分的影响由显式的双线性交互实现。在CheXpert数据集上，C²A取得0.895的宏平均AUROC，优于先进上下文门控基线。关键优势体现在共现性强且空间证据模糊的类别上，例如肺不张性能相比GCG提升+1.5，验证了先验的正则化作用，且仅增加一次线性投影和一个C×C边矩阵，开销极小。
原文摘要 · Abstract (English)
Thoracic pathologies rarely occur in isolation, yet standard multi-label classifiers rely on shared global descriptors, discarding \emph{where} findings lie and \emph{how} they co-occur. We propose \textbf{C$\mathbf{^2}$A} (Co-occurrence Aware Class Attention), a classification head that explicitly couples spatial evidence with clinical priors. First, C$^2$A casts pooling as an expectation over learned per-class spatial attention maps, yielding localized descriptors for each disease. Second, it couples these descriptors via a learnable graph warm-started from empirical label co-occurrence. A single residual message-passing step shares evidence among related findings, proving to be a bounded perturbation of the identity where co-occurrence enters each logit through an explicit bilinear interaction. On CheXpert, C$^2$A achieves a superior $0.895$ macro-mean AUROC, outperforming advanced context-gating baselines. Crucially, gains concentrate on highly co-occurrent classes with ambiguous spatial evidence (rescuing Atelectasis by $+1.5$ over GCG), demonstrating the prior's regularizing effect with a negligible overhead of one linear projection and a $C\!\times\!C$ edge matrix.
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