Local Contrastive Learning for Medical Image Recognition

Understand

The proliferation of Deep Learning (DL)-based methods for radiographic image analysis has created a great demand for expert-labeled radiology data.

• Recent self-supervised frameworks have alleviated the need for expert labeling by obtaining supervision from associated radiology reports.
• These frameworks, however, struggle to distinguish the subtle differences between different pathologies in medical images.
• Additionally, many of them do not provide interpretation between image regions and text, making it difficult for radiologists to assess model predictions.