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| Main Authors: | , , , , , , , , , , , , , , , , , , , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2501.12016 |
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| _version_ | 1866910792229584896 |
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| author | Yew, Samantha Min Er Lei, Xiaofeng Goh, Jocelyn Hui Lin Chen, Yibing Srinivasan, Sahana Chee, Miao-li Pushpanathan, Krithi Zou, Ke Hou, Qingshan Da Soh, Zhi Xue, Cancan Yu, Marco Chak Yan Sabanayagam, Charumathi Tai, E Shyong Sim, Xueling Wang, Yaxing Jonas, Jost B. Nangia, Vinay Yang, Gabriel Dawei Ran, Emma Anran Cheung, Carol Yim-Lui Feng, Yangqin Zhou, Jun Goh, Rick Siow Mong Zhou, Yukun Keane, Pearse A. Liu, Yong Cheng, Ching-Yu Tham, Yih-Chung |
| author_facet | Yew, Samantha Min Er Lei, Xiaofeng Goh, Jocelyn Hui Lin Chen, Yibing Srinivasan, Sahana Chee, Miao-li Pushpanathan, Krithi Zou, Ke Hou, Qingshan Da Soh, Zhi Xue, Cancan Yu, Marco Chak Yan Sabanayagam, Charumathi Tai, E Shyong Sim, Xueling Wang, Yaxing Jonas, Jost B. Nangia, Vinay Yang, Gabriel Dawei Ran, Emma Anran Cheung, Carol Yim-Lui Feng, Yangqin Zhou, Jun Goh, Rick Siow Mong Zhou, Yukun Keane, Pearse A. Liu, Yong Cheng, Ching-Yu Tham, Yih-Chung |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_12016 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Are Traditional Deep Learning Model Approaches as Effective as a Retinal-Specific Foundation Model for Ocular and Systemic Disease Detection? Yew, Samantha Min Er Lei, Xiaofeng Goh, Jocelyn Hui Lin Chen, Yibing Srinivasan, Sahana Chee, Miao-li Pushpanathan, Krithi Zou, Ke Hou, Qingshan Da Soh, Zhi Xue, Cancan Yu, Marco Chak Yan Sabanayagam, Charumathi Tai, E Shyong Sim, Xueling Wang, Yaxing Jonas, Jost B. Nangia, Vinay Yang, Gabriel Dawei Ran, Emma Anran Cheung, Carol Yim-Lui Feng, Yangqin Zhou, Jun Goh, Rick Siow Mong Zhou, Yukun Keane, Pearse A. Liu, Yong Cheng, Ching-Yu Tham, Yih-Chung Computer Vision and Pattern Recognition Machine Learning 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. |
| title | Are Traditional Deep Learning Model Approaches as Effective as a Retinal-Specific Foundation Model for Ocular and Systemic Disease Detection? |
| topic | Computer Vision and Pattern Recognition Machine Learning |
| url | https://arxiv.org/abs/2501.12016 |