Abstract
Accurate identification and localization of abnormalities from radiology images play an integral part in clinical diagnosis and treatment planning. Building a highly
accurate prediction model for these tasks usually requires
a large number of images manually annotated with labels
and finding sites of abnormalities. In reality, however, such
annotated data are expensive to acquire, especially the ones
with location annotations. We need methods that can work
well with only a small amount of location annotations. To
address this challenge, we present a unified approach that
simultaneously performs disease identification and localization through the same underlying model for all images.
We demonstrate that our approach can effectively leverage
both class information as well as limited location annotation, and significantly outperforms the comparative reference baseline in both classification and localization tasks