  Automated diagnostic assistants in healthcare necessitate accurate AI models
that can be trained with limited labeled data, can cope with severe class
imbalances and can support simultaneous prediction of multiple disease
conditions. To this end, we present a deep learning framework that utilizes a
number of key components to enable robust modeling in such challenging
scenarios. Using an important use-case in chest X-ray classification, we
provide several key insights on the effective use of data augmentation,
self-training via distillation and confidence tempering for small data learning
in medical imaging. Our results show that using 85% lesser labeled data, we can
build predictive models that match the performance of classifiers trained in a
large-scale data setting.

Computer Vision and Pattern Recognition;Machine Learning