  Chest X-rays (CXRs) are a medical imaging modality that is used to infer a
large number of abnormalities. While it is hard to define an exhaustive list of
these abnormalities, which may co-occur on a chest X-ray, few of them are quite
commonly observed and are abundantly represented in CXR datasets used to train
deep learning models for automated inference. However, it is challenging for
current models to learn independent discriminatory features for labels that are
rare but may be of high significance. Prior works focus on the combination of
multi-label and long tail problems by introducing novel loss functions or some
mechanism of re-sampling or re-weighting the data. Instead, we propose that it
is possible to achieve significant performance gains merely by choosing an
initialization for a model that is closer to the domain of the target dataset.
This method can complement the techniques proposed in existing literature, and
can easily be scaled to new labels. Finally, we also examine the veracity of
synthetically generated data to augment the tail labels and analyse its
contribution to improving model performance.
