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Is an Ultra Large Natural Image-Based Foundation Model Superior to a Retina-Specific Model for Detecting Ocular and Systemic Diseases?

Created by
  • Haebom

Author

Qingshan Hou, Yukun Zhou, Jocelyn Hui Lin Goh, Ke Zou, Samantha Min Er Yew, Sahana Srinivasan, Meng Wang, Thaddaeus Lo, Xiaofeng Lei, Siegfried K. Wagner, Mark A. Chia, Dawei Yang, Hongyang Jiang, An Ran Ran, Rui Santos, Gabor Mark Somfai, Juan Helen Zhou, Haoyu Chen, Qingyu Chen, Carol Y. Cheung, Pearse A. Keane, Yih Chung Tham

Outline

This paper presents the results of a study on the application of Foundation Models (FMs) in the medical field, particularly in ophthalmology. We compared and evaluated RETFound, a retina-specific FM, and DINOv2, a general-purpose vision FM, for various ophthalmic disease detection and systemic disease prediction tasks. After fine-tuning, we compared performance using eight publicly available ophthalmology datasets, the Moorfields AlzEye, and UK Biobank datasets. DINOv2 outperformed diabetic retinopathy and multiple ophthalmic disease detection, while RETFound outperformed heart failure, myocardial infarction, and ischemic stroke prediction. This highlights the importance of selecting the right FM for a given task.

Takeaways, Limitations

Takeaways:
We identified the relative strengths of general-purpose FM (DINOv2) and domain-specific FM (RETFound).
The suitability of each FM for diagnosing specific ophthalmic diseases and predicting systemic diseases was presented.
This suggests that selecting FMs appropriate to the task characteristics is important for optimizing clinical performance.
Limitations:
Limitations in generalizability due to limitations in the dataset used in the study.
Further research is needed on other types of FM or more clinical challenges.
While the differences in performance for certain conditions may be subtle, they may be statistically significant. Further analysis is needed to determine clinical utility.
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