Self-supervised learning framework application for medical image analysis: a review and summary
X Zeng, N Abdullah, P Sumari - BioMedical Engineering OnLine, 2024 - Springer
Manual annotation of medical image datasets is labor-intensive and prone to biases.
Moreover, the rate at which image data accumulates significantly outpaces the speed of …
Moreover, the rate at which image data accumulates significantly outpaces the speed of …
Self-supervised learning for medical image analysis: Discriminative, restorative, or adversarial?
Discriminative, restorative, and adversarial learning have proven beneficial for self-
supervised learning schemes in computer vision and medical imaging. Existing efforts …
supervised learning schemes in computer vision and medical imaging. Existing efforts …
Representing Part-Whole Hierarchies in Foundation Models by Learning Localizability Composability and Decomposability from Anatomy via Self Supervision
MRH Taher, MB Gotway… - Proceedings of the IEEE …, 2024 - openaccess.thecvf.com
Humans effortlessly interpret images by parsing them into part-whole hierarchies; deep
learning excels in learning multi-level feature spaces but they often lack explicit coding of …
learning excels in learning multi-level feature spaces but they often lack explicit coding of …
ACE: Anatomically Consistent Embeddings in Composition and Decomposition
Medical images acquired from standardized protocols show consistent macroscopic or
microscopic anatomical structures, and these structures consist of composable …
microscopic anatomical structures, and these structures consist of composable …