CoBooM: Codebook Guided Bootstrapping for Medical Image Representation Learning

TL;DR

CoBooM introduces a codebook-guided self-supervised learning framework that leverages anatomical similarities in medical images to improve feature representation, leading to enhanced classification and segmentation performance.

Contribution

This work presents a novel framework integrating continuous and discrete representations via codebooks for improved medical image self-supervised learning.

Findings

Significant performance improvements in classification tasks.

Enhanced segmentation accuracy across datasets.

Effective capture of anatomical details using codebooks.

Abstract

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.