Are Traditional Deep Learning Model Approaches as Effective as a Retinal-Specific Foundation Model for Ocular and Systemic Disease Detection?
Abstrak
Background: RETFound, a self-supervised, retina-specific foundation model (FM), showed potential in downstream applications. However, its comparative performance with traditional deep learning (DL) models remains incompletely understood. This study aimed to evaluate RETFound against three ImageNet-pretrained supervised DL models (ResNet50, ViT-base, SwinV2) in detecting ocular and systemic diseases. Methods: We fine-tuned/trained RETFound and three DL models on full datasets, 50%, 20%, and fixed sample sizes (400, 200, 100 images, with half comprising disease cases; for each DR severity class, 100 and 50 cases were used. Fine-tuned models were tested internally using the SEED (53,090 images) and APTOS-2019 (3,672 images) datasets and externally validated on population-based (BES, CIEMS, SP2, UKBB) and open-source datasets (ODIR-5k, PAPILA, GAMMA, IDRiD, MESSIDOR-2). Model performance was compared using area under the receiver operating characteristic curve (AUC) and Z-tests with Bonferroni correction (P<0.05/3). Interpretation: Traditional DL models are mostly comparable to RETFound for ocular disease detection with large datasets. However, RETFound is superior in systemic disease detection with smaller datasets. These findings offer valuable insights into the respective merits and limitation of traditional models and FMs.
Penulis (29)
Samantha Min Er Yew
Xiaofeng Lei
Jocelyn Hui Lin Goh
Yibing Chen
Sahana Srinivasan
Miao-li Chee
Krithi Pushpanathan
Ke Zou
Qingshan Hou
Zhi Da Soh
Cancan Xue
Marco Chak Yan Yu
Charumathi Sabanayagam
E Shyong Tai
Xueling Sim
Yaxing Wang
Jost B. Jonas
Vinay Nangia
Gabriel Dawei Yang
Emma Anran Ran
Carol Yim-Lui Cheung
Yangqin Feng
Jun Zhou
Rick Siow Mong Goh
Yukun Zhou
Pearse A. Keane
Yong Liu
Ching-Yu Cheng
Yih-Chung Tham
Akses Cepat
- Tahun Terbit
- 2025
- Bahasa
- en
- Sumber Database
- arXiv
- Akses
- Open Access ✓