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This research aims to enhance the early detection of retinal abnormalities through a novel representation learning framework using minimal supervision.

July 13, 2026Scientific ReportsOpen Access

Clinically grounded retinal representation learning from minimal supervision

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This research aims to enhance the early detection of retinal abnormalities through a novel representation learning framework using minimal supervision.
Developed M-FunFound framework pretrained on binary abnormality labels from 113,645 fundus images.
Evaluated the model's performance across three tasks: abnormality classification, multi-label disease classification, and vessel segmentation.
Compared M-FunFound's performance with other pretrained models on various datasets.
Achieved AUCs of 0.945 (internal), 0.958 (JSIEC), and 0.829 (RFMiD) for binary abnormality classification.
Highest weighted F1-score of 0.725 across eight disease categories in multi-label classification.
Highest Dice score of 0.853 for vessel segmentation.

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Implication

Randomized trial demonstrates improved abnormality detection in retinal images, suggesting effective training with minimal supervision.

Cite This Study

Jang et al. (2026) studied this question.

synapsesocial.com/papers/6a54835d475c38bf615a5f26 https://doi.org/10.1038/s41598-026-60067-y

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Clinically grounded retinal representation learning from minimal supervision

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Discussion

Implication

Cite This Study

Clinically grounded retinal representation learning from minimal supervision

Key Points

Discussion

Implication

Cite This Study

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