  The widely used ChestX-ray14 dataset addresses an important medical image
classification problem and has the following caveats: 1) many lung pathologies
are visually similar, 2) a variant of diseases including lung cancer,
tuberculosis, and pneumonia are present in a single scan, i.e. multiple labels
and 3) The incidence of healthy images is much larger than diseased samples,
creating imbalanced data. These properties are common in medical domain.
Existing literature uses stateof- the-art DensetNet/Resnet models being
transfer learned where output neurons of the networks are trained for
individual diseases to cater for multiple diseases labels in each image.
However, most of them don't consider relationship between multiple classes. In
this work we have proposed a novel error function, Multi-label Softmax Loss
(MSML), to specifically address the properties of multiple labels and
imbalanced data. Moreover, we have designed deep network architecture based on
fine-grained classification concept that incorporates MSML. We have evaluated
our proposed method on various network backbones and showed consistent
performance improvements of AUC-ROC scores on the ChestX-ray14 dataset. The
proposed error function provides a new method to gain improved performance
across wider medical datasets.
