Multimodal deep learning with feature level fusion for identification of choroidal neovascularization activity in age‐related macular degeneration-Reference-Cited by-同舟云学术

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Multimodal deep learning with feature level fusion for identification of choroidal neovascularization activity in age‐related macular degeneration

Published:2021-06-23 Issue:2 Volume:100 Page:
ISSN:1755-375X
Container-title:Acta Ophthalmologica
language:en
Short-container-title:Acta Ophthalmologica

Author:

Jin Kai¹^ORCID,Yan Yan¹,Chen Menglu¹,Wang Jun²,Pan Xiangji¹,Liu Xindi¹,Liu Mushui³,Lou Lixia¹,Wang Yao¹^ORCID,Ye Juan¹^ORCID

Affiliation:

1. Department of Ophthalmology College of Medicine The Second Affiliated Hospital of Zhejiang University Hangzhou China

2. The School of Biomedical Engineering Shanghai Jiao Tong University Shanghai China

3. College of Computer Science and Technology Zhejiang University Hangzhou China

Funder

Natural Science Foundation of Zhejiang Province

National Key Research and Development Program of China

Publisher

Wiley

Subject

Ophthalmology,General Medicine

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1111/aos.14928

Reference26 articles.

1. BIOMARKERS OF NEOVASCULAR ACTIVITY IN AGE-RELATED MACULAR DEGENERATION USING OPTICAL COHERENCE TOMOGRAPHY ANGIOGRAPHY

2. Quantitative Optical Coherence Tomography Angiography Parameters in Type 1 Macular Neovascularization Secondary to Age-Related Macular Degeneration

3. Predictors of neovascular activity during neovascular age-related macular degeneration treatment based on optical coherence tomography angiography

4. Automated Grading of Age-Related Macular Degeneration From Color Fundus Images Using Deep Convolutional Neural Networks

5. ChenT MaX LiuXet al. (2019):Multi‐view learning with feature level fusion for cervical dysplasia diagnosis medical image computing and computer assisted intervention. MICCAI11764:329–338.

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