  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. However, pixel annotations contain much valuable information for
medical image segmentation, which is largely ignored in contrastive learning.
In this work, we propose a novel pre-training framework called Mixed
Contrastive Learning (MixCL) that leverages both image identities and pixel
labels for better modeling by maintaining identity consistency, label
consistency, and reconstruction consistency together. Consequently, thus
pre-trained model has more robust representations that characterize medical
images. Extensive experiments demonstrate the effectiveness of the proposed
method, improving the baseline by 5.28% and 14.12% in Dice coefficient when 5%
labeled data of Spleen and 15% of BTVC are used in fine-tuning, respectively.
