2022/09/02 by Rachid Zeghlache, Pierre-Henri Conze, Zeghlache, Rachid +13
Medicine · #Acute Ischemic Stroke Management #Artificial Intelligence (cs.AI) #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Machine Learning (cs.LG) #Retinal Diseases and Treatments #Retinal Imaging and Analysis
paper · pdf · doi:10.48550/arxiv.2209.00915
openalex publication_date 2022/09/02 · openalex created_date 2022/09/06 · openalex updated_date 2026/07/28
Longitudinal imaging is able to capture both static anatomical structures and\ndynamic changes in disease progression towards earlier and better\npatient-specific pathology management. However, conventional approaches for\ndetecting diabetic retinopathy (DR) rarely take advantage of longitudinal\ninformation to improve DR analysis. In this work, we investigate the benefit of\nexploiting self-supervised learning with a longitudinal nature for DR diagnosis\npurposes. We compare different longitudinal self-supervised learning (LSSL)\nmethods to model the disease progression from longitudinal retinal color fundus\nphotographs (CFP) to detect early DR severity changes using a pair of\nconsecutive exams. The experiments were conducted on a longitudinal DR\nscreening dataset with or without those trained encoders (LSSL) acting as a\nlongitudinal pretext task. Results achieve an AUC of 0.875 for the baseline\n(model trained from scratch) and an AUC of 0.96 (95% CI: 0.9593-0.9655 DeLong\ntest) with a p-value < 2.2e-16 on early fusion using a simple ResNet alike\narchitecture with frozen LSSL weights, suggesting that the LSSL latent space\nenables to encode the dynamic of DR progression.\n