  Self-supervised learning (SSL) is potentially useful in reducing the need for
manual annotation and making deep learning models accessible for medical image
analysis tasks. By leveraging the representations learned from unlabeled data,
self-supervised models perform well on tasks that require little to no
fine-tuning. However, for medical images, like chest X-rays, which are
characterized by complex anatomical structures and diverse clinical conditions,
there arises a need for representation learning techniques that can encode
fine-grained details while preserving the broader contextual information. In
this context, we introduce MLVICX (Multi-Level Variance-Covariance Exploration
for Chest X-ray Self-Supervised Representation Learning), an approach to
capture rich representations in the form of embeddings from chest X-ray images.
Central to our approach is a novel multi-level variance and covariance
exploration strategy that empowers the model to detect diagnostically
meaningful patterns while reducing redundancy effectively. By enhancing the
variance and covariance of the learned embeddings, MLVICX promotes the
retention of critical medical insights by adapting both global and local
contextual details. We demonstrate the performance of MLVICX in advancing
self-supervised chest X-ray representation learning through comprehensive
experiments. The performance enhancements we observe across various downstream
tasks highlight the significance of the proposed approach in enhancing the
utility of chest X-ray embeddings for precision medical diagnosis and
comprehensive image analysis. For pertaining, we used the NIH-Chest X-ray
dataset, while for downstream tasks, we utilized NIH-Chest X-ray, Vinbig-CXR,
RSNA pneumonia, and SIIM-ACR Pneumothorax datasets. Overall, we observe more
than 3% performance gains over SOTA SSL approaches in various downstream tasks.
