Grad-CAM-Based Investigation into Acute-Stage Fluorescein Angiography Images to Predict Long-Term Visual Prognosis of Branch Retinal Vein Occlusion.

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作者： Michiyuki,Saito ^[1] ; Mizuho,Mitamura ^[1] ; Mayuko,Kimura ^[1] ; Yuki,Ito ^[2] ; Hiroaki,Endo ^[2] ; Satoshi,Katsuta ^[2] ; Manabu,Kase ^[2] ; Susumu,Ishida ^[1]

作者单位： Department of Ophthalmology, Faculty of Medicine and Graduate School of Medicine, Hokkaido University, N-15, W-7, Kita-ku, Sapporo 060-8638, Japan. ^[1] Department of Ophthalmology, Teine Keijinkai Hospital, Sapporo 006-0811, Japan. ^[2]

关键词 ResNet branch retinal vein occlusion deep learning explainable artificial intelligence fluorescein angiography gradient-weighted class activation mapping (Grad-CAM)visual prognosis

DOI 10.3390/jcm13175271

PMID 39274484

发布时间 2024-09-16

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Journal of clinical medicine

2024年13卷17期

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Grad-CAM-Based Investigation into Acute-Stage Fluorescein Angiography Images to Predict Long-Term Visual Prognosis of Branch Retinal Vein Occlusion.

Journal of clinical medicine

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Journal of clinical medicine

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Grad-CAM-Based Investigation into Acute-Stage Fluorescein Angiography Images to Predict Long-Term Visual Prognosis of Branch Retinal Vein Occlusion.

Journal of clinical medicine

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Journal of clinical medicine

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