142 citations · 265 across the 5 of their papers we have counts for
5 papers
Contrastive learning-based pretraining improves representation and transferability of diabetic retinopathy classification models
Minhaj Nur Alam, Rikiya Yamashita, Vignav Ramesh +6
Self supervised contrastive learning based pretraining allows development of robust and generalized deep learning models with small, labeled datasets, reducing the burden of label…
I-ODA, Real-World Multi-modal Longitudinal Data for OphthalmicApplications
Nooshin Mojab, Vahid Noroozi, Abdullah Aleem +8
Data from clinical real-world settings is characterized by variability in quality, machine-type, setting, and source. One of the primary goals of medical computer vision is to deve…
Trans-pars-planar illumination enables 200° ultra-wide field pediatric fundus photography for easy examination of the retina
Devrim Toslak, Felix Chau, Muhammet Kazim Erol +4
This study is to test the feasibility of using trans-pars-planar illumination for ultrawide field pediatric fundus photography. Fundus examination of the peripheral retina is essen…
Transfer Learning for Automated OCTA Detection of Diabetic Retinopathy
David Le, Minhaj Alam, Cham Yao +4
Purpose: To test the feasibility of using deep learning for optical coherence tomography angiography (OCTA) detection of diabetic retinopathy (DR). Methods: A deep learning convolu…
Supervised machine learning based multi-task artificial intelligence classification of retinopathies
Minhaj Alam, David Le, Jennifer I. Lim +2
Artificial intelligence (AI) classification holds promise as a novel and affordable screening tool for clinical management of ocular diseases. Rural and underserved areas, which su…