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Multi-label classification of chest X-ray images is frequently performedusing discriminative approaches, i.e. learning to map an image directly to itsbinary labels. Such approaches make it challenging to incorporate auxiliaryinformation such as annotation uncertainty or a dependency among the labels.Building towards this, we propose a novel knowledge graph reformulation ofmulti-label classification, which not only readily increases predictiveperformance of an encoder but also serves as a general framework forintroducing new domain knowledge.  Specifically, we construct a multi-modal knowledge graph out of the chestX-ray images and its labels and pose multi-label classification as a linkprediction problem. Incorporating auxiliary information can then simply beachieved by adding additional nodes and relations among them. When tested on apublicly-available radiograph dataset (CheXpert), our relational-reformulationusing a naive knowledge graph outperforms the state-of-art by achieving anarea-under-ROC curve of 83.5%, an improvement of "sim 1" over a purelydiscriminative approach.

A Relational-learning Perspective to Multi-label Chest X-ray Classification