  Self-supervised learning (SSL) has emerged as a promising paradigm for
medical image analysis by harnessing unannotated data. Despite their potential,
the existing SSL approaches overlook the high anatomical similarity inherent in
medical images. This makes it challenging for SSL methods to capture diverse
semantic content in medical images consistently. This work introduces a novel
and generalized solution that implicitly exploits anatomical similarities by
integrating codebooks in SSL. The codebook serves as a concise and informative
dictionary of visual patterns, which not only aids in capturing nuanced
anatomical details but also facilitates the creation of robust and generalized
feature representations. In this context, we propose CoBooM, a novel framework
for self-supervised medical image learning by integrating continuous and
discrete representations. The continuous component ensures the preservation of
fine-grained details, while the discrete aspect facilitates coarse-grained
feature extraction through the structured embedding space. To understand the
effectiveness of CoBooM, we conduct a comprehensive evaluation of various
medical datasets encompassing chest X-rays and fundus images. The experimental
results reveal a significant performance gain in classification and
segmentation tasks.
