FusionFM: Fusing Eye-specific Foundational Models for Optimized Ophthalmic Diagnosis
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| Main Authors: | , , , , , , , , , , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866911107002662912 |
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| author | Zou, Ke Goh, Jocelyn Hui Lin Zhou, Yukun Lin, Tian Yew, Samantha Min Er Srinivasan, Sahana Wang, Meng Santos, Rui Somfai, Gabor M. Fu, Huazhu Chen, Haoyu Keane, Pearse A. Cheng, Ching-Yu Tham, Yih Chung |
| author_facet | Zou, Ke Goh, Jocelyn Hui Lin Zhou, Yukun Lin, Tian Yew, Samantha Min Er Srinivasan, Sahana Wang, Meng Santos, Rui Somfai, Gabor M. Fu, Huazhu Chen, Haoyu Keane, Pearse A. Cheng, Ching-Yu Tham, Yih Chung |
| contents | Foundation models (FMs) have shown great promise in medical image analysis by improving generalization across diverse downstream tasks. In ophthalmology, several FMs have recently emerged, but there is still no clear answer to fundamental questions: Which FM performs the best? Are they equally good across different tasks? What if we combine all FMs together? To our knowledge, this is the first study to systematically evaluate both single and fused ophthalmic FMs. To address these questions, we propose FusionFM, a comprehensive evaluation suite, along with two fusion approaches to integrate different ophthalmic FMs. Our framework covers both ophthalmic disease detection (glaucoma, diabetic retinopathy, and age-related macular degeneration) and systemic disease prediction (diabetes and hypertension) based on retinal imaging. We benchmarked four state-of-the-art FMs (RETFound, VisionFM, RetiZero, and DINORET) using standardized datasets from multiple countries and evaluated their performance using AUC and F1 metrics. Our results show that DINORET and RetiZero achieve superior performance in both ophthalmic and systemic disease tasks, with RetiZero exhibiting stronger generalization on external datasets. Regarding fusion strategies, the Gating-based approach provides modest improvements in predicting glaucoma, AMD, and hypertension. Despite these advances, predicting systemic diseases, especially hypertension in external cohort remains challenging. These findings provide an evidence-based evaluation of ophthalmic FMs, highlight the benefits of model fusion, and point to strategies for enhancing their clinical applicability. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_11721 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | FusionFM: Fusing Eye-specific Foundational Models for Optimized Ophthalmic Diagnosis Zou, Ke Goh, Jocelyn Hui Lin Zhou, Yukun Lin, Tian Yew, Samantha Min Er Srinivasan, Sahana Wang, Meng Santos, Rui Somfai, Gabor M. Fu, Huazhu Chen, Haoyu Keane, Pearse A. Cheng, Ching-Yu Tham, Yih Chung Computer Vision and Pattern Recognition Artificial Intelligence Foundation models (FMs) have shown great promise in medical image analysis by improving generalization across diverse downstream tasks. In ophthalmology, several FMs have recently emerged, but there is still no clear answer to fundamental questions: Which FM performs the best? Are they equally good across different tasks? What if we combine all FMs together? To our knowledge, this is the first study to systematically evaluate both single and fused ophthalmic FMs. To address these questions, we propose FusionFM, a comprehensive evaluation suite, along with two fusion approaches to integrate different ophthalmic FMs. Our framework covers both ophthalmic disease detection (glaucoma, diabetic retinopathy, and age-related macular degeneration) and systemic disease prediction (diabetes and hypertension) based on retinal imaging. We benchmarked four state-of-the-art FMs (RETFound, VisionFM, RetiZero, and DINORET) using standardized datasets from multiple countries and evaluated their performance using AUC and F1 metrics. Our results show that DINORET and RetiZero achieve superior performance in both ophthalmic and systemic disease tasks, with RetiZero exhibiting stronger generalization on external datasets. Regarding fusion strategies, the Gating-based approach provides modest improvements in predicting glaucoma, AMD, and hypertension. Despite these advances, predicting systemic diseases, especially hypertension in external cohort remains challenging. These findings provide an evidence-based evaluation of ophthalmic FMs, highlight the benefits of model fusion, and point to strategies for enhancing their clinical applicability. |
| title | FusionFM: Fusing Eye-specific Foundational Models for Optimized Ophthalmic Diagnosis |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence |
| url | https://arxiv.org/abs/2508.11721 |