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