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EyeMVP model enhances retinal analysis using paired CFP-OCT pretraining

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Researchers have developed EyeMVP, a novel foundation model for retinal image analysis that integrates data from both color fundus photography (CFP) and optical coherence tomography (OCT). Pretrained on a large dataset of paired CFP-OCT images from over 112,000 patients, EyeMVP learns to enhance CFP representations with OCT-derived structural information. This allows for more accurate diagnoses using only CFP images during inference, showing improved performance on tasks like macular edema and myopic macular schisis detection compared to existing models and human ophthalmologists in exploratory studies. AI

RANK_REASON The cluster contains an academic paper detailing a new model and its performance on specific tasks. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

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EyeMVP model enhances retinal analysis using paired CFP-OCT pretraining

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The cluster contains an academic paper detailing a new model and its performance on specific tasks. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Zhuo Deng, Ruiheng Zhang, Ziheng Zhang, Weihao Gao, Yitong Li, Qian Wang, Lei Shao, Jiaoyue Dong, Zhixi Zeng, Lijian Fang, Haibo Wang, Xiaobin Lin, Tao Liu, Zhicheng Du, Zhengwei Zhang, Lin Yang, Zheng Gong, Xinyu Zhao, Zhenquan Wu, Fang Li, Zhiguang Zho… ·

    EyeMVP: OCT-Informed Fundus Representation Learning via Paired CFP--OCT Pretraining

    arXiv:2606.15129v1 Announce Type: cross Abstract: Color fundus photography (CFP) is the mainstay for large-scale retinal screening, yet its diagnostic capacity is constrained by the lack of depth-resolved structural information. Optical coherence tomography (OCT) provides cross-s…

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