MLVICX: Multi-Level Variance-Covariance Exploration for Chest X-ray Self-Supervised Representation Learning

arXiv:2403.11504v1 Announce Type: cross
Abstract: 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 …

abstract analysis annotation arxiv covariance cs.ai cs.cv cs.lg data deep learning eess.iv exploration fine-tuning however image making medical ray representation representation learning self-supervised learning ssl supervised learning tasks type variance x-ray