Contrastive Learning Meets Transfer Learning: A Case Study In Medical Image Analysis

Understand

Annotated medical images are typically rarer than labeled natural images since they are limited by domain knowledge and privacy constraints.

• Recent advances in transfer and contrastive learning have provided effective solutions to tackle such issues from different perspectives.
• The state-of-the-art transfer learning (e.g., Big Transfer (BiT)) and contrastive learning (e.g., Simple Siamese Contrastive Learning (SimSiam)) approaches have been investigated independently, without considering the complementary nature of such techniques.
• It would be appealing to accelerate contrastive learning with transfer learning, given that slow convergence speed is a critical limitation of modern contrastive learning approaches.