2 citations · 2 across the 2 of their papers we have counts for
4 papers
OCT-FedSIR: Toward Trustworthy Federated Ophthalmic Learning under Annotation Noise
Sina Gholami, Abdulmoneam Ali, Tania Haghighi +8
Federated learning enables collaborative model development without centralizing patient data, but annotation reliability at participating institutions cannot always be assumed. In…
Anatomy-Aware Unsupervised Detection and Localization of Retinal Abnormalities in Optical Coherence Tomography
Tania Haghighi, Sina Gholami, Hamed Tabkhi +1
Reliable automated analysis of Optical Coherence Tomography (OCT) imaging is crucial for diagnosing retinal disorders but faces a critical barrier: the need for expensive, labor-in…
Multi-OCT-SelfNet: Integrating Self-Supervised Learning with Multi-Source Data Fusion for Enhanced Multi-Class Retinal Disease Classification
Fatema-E- Jannat, Sina Gholami, Jennifer I. Lim +3
In the medical domain, acquiring large datasets poses significant challenges due to privacy concerns. Nonetheless, the development of a robust deep-learning model for retinal disea…
OCT-SelfNet: A Self-Supervised Framework with Multi-Modal Datasets for Generalized and Robust Retinal Disease Detection
Fatema-E Jannat, Sina Gholami, Minhaj Nur Alam +1
Despite the revolutionary impact of AI and the development of locally trained algorithms, achieving widespread generalized learning from multi-modal data in medical AI remains a si…