Detection of diabetic retinopathy using longitudinal self-supervised learning

arXiv:2209.00915v3 Announce Type: replace-cross
Abstract: Longitudinal imaging is able to capture both static anatomical structures and dynamic changes in disease progression towards earlier and better patient-specific pathology management. However, conventional approaches for detecting diabetic retinopathy (DR) rarely take advantage of longitudinal information to improve DR analysis. In this work, we investigate the benefit of exploiting self-supervised learning with a longitudinal nature for DR diagnosis purposes. We compare different longitudinal self-supervised learning (LSSL) methods to model the disease progression from longitudinal …

abstract analysis arxiv cs.ai cs.cv cs.lg detection disease dynamic however imaging information management pathology patient self-supervised learning supervised learning type work