胸片多标签分类老问题，C²A用共现先验加注意力，CheXpert上0.895 AUROC，比GCG强，开销还小，值得搞医学影像的看看。
C²A是一种新的分类头，将池化视为对学习到的每类空间注意力图的期望，为每种疾病生成局部描述符。它通过从经验标签共现初始化的可学习图耦合这些描述符，并用单次残差消息传递在相关发现间共享证据。在CheXpert上，C²A的宏平均AUROC达到0.895，优于先进上下文门控基线。增益集中在共现度高且空间证据模糊的类别上，如肺不张比GCG高出1.5个百分点，且仅增加一个线性投影和C×C边矩阵的开销。
C$^2$A: Coupling Spatial Evidence with Clinical Priors via Co-occurrence Aware Class Attention for Multi-Label Chest X-Ray Classification
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.