MixCL: Pixel label matters to contrastive learning

Understand

Contrastive learning and self-supervised techniques have gained prevalence in computer vision for the past few years.

• It is essential for medical image analysis, which is often notorious for its lack of annotations.
• Most existing self-supervised methods applied in natural imaging tasks focus on designing proxy tasks for unlabeled data.
• For example, contrastive learning is often based on the fact that an image and its transformed version share the same identity.