Abstract
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 novel few-shot learning approach 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 few-shot learning in medical imaging. Our results show that using only ~10% of the labeled data, we can build predictive models that match the performance of classifiers trained in a large-scale data setting.
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URL
https://arxiv.org/abs/2005.02231
