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Chest X-ray is an essential diagnostic tool in the identification of chestdiseases given its high sensitivity to pathological abnormalities in the lungs.However, image-driven diagnosis is still challenging due to heterogeneity insize and location of pathology, as well as visual similarities andco-occurrence of separate pathology. Since disease-related regions often occupya relatively small portion of diagnostic images, classification models based ontraditional convolutional neural networks (CNNs) are adversely affected giventheir locality bias. While CNNs were previously augmented with attention mapsor spatial masks to guide focus on potentially critical regions, learninglocalization guidance under heterogeneity in the spatial distribution ofpathology is challenging. To improve multi-label classification performance,here we propose a novel method, HydraViT, that synergistically combines atransformer backbone with a multi-branch output module with learned weighting.The transformer backbone enhances sensitivity to long-range context in X-rayimages, while using the self-attention mechanism to adaptively focus ontask-critical regions. The multi-branch output module dedicates an independentbranch to each disease label to attain robust learning across separate diseaseclasses, along with an aggregated branch across labels to maintain sensitivityto co-occurrence relationships among pathology. Experiments demonstrate that,on average, HydraViT outperforms competing attention-guided methods by 1.2%,region-guided methods by 1.4%, and semantic-guided methods by 1.0% inmulti-label classification performance.

HydraViT: Adaptive Multi-Branch Transformer for Multi-Label Disease Classification from Chest X-ray Images