Work

Multimodal Deep Learning for Diabetic Retinopathy: A Survey

Year: 2025

Type: conference-paper

Abstract: Diabetic retinopathy (DR) remains a leading cause of blindness globally, driving the rapid development of automated diagnostic systems leveraging deep learning. Recent research demonstrates that multimodal deep learning models that integrate retinal images, electronic health records (EHR), and clinical text data can surpass single-modality approaches in DR screening accuracy. In this survey, we systematically review advances from the last 3 years, highlighting key models and methodologies, as well as datasets and evaluation metrics. We find multimodal approaches consistently outperform single-modality models, and often by a clear margin, and identify data scarcity, domain adaptation, and interpretability as the three main hurdles ahead.

Source: IEEE International Conference on Multimedia Information Processing and Retrieval (MIPR)

DOI: 10.1109/mipr67560.2025.00032

Authors: Genyan Qin, Qinghua Peng, Yasha Zhou, Jun Peng, Qing Liu, Jian Xu

Institutions: Hunan University of Traditional Chinese Medicine, Tongren Hospital, First Affiliated Hospital of Hunan University of Traditional Chinese Medicine, Shanghai Jiao Tong University

Language: English

Topic: Retinal Imaging and Analysis

Subfield: Radiology, Nuclear Medicine and Imaging

Field: Medicine

Domain: Health Sciences

SDG: Good health and well-being

Open Access status: gold

Funders: National Natural Science Foundation of China, Hunan University, Natural Science Foundation of Hunan Province, Hunan University of Chinese Medicine