Comparative Deep Learning Architectures to Detect Tiny Features in Ophthalmic Imaging-Reference-Cited by-同舟云学术

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Comparative Deep Learning Architectures to Detect Tiny Features in Ophthalmic Imaging

Published:2023-06 Issue: Volume: Page:
ISSN:
Container-title:2023 10th IEEE Swiss Conference on Data Science (SDS)
language:
Short-container-title:

Author:

Hartmann Julia¹,Maloca Peter²,Huwyler CéDric¹,Melchior Martin¹,Suter Susanne¹

Affiliation:

1. Institute for Data Science (I4DS) FHNW School of Engineering,Brugg-Windisch,Switzerland

2. University of Basel & Institute of Molecular and Clinical Ophthalmology (IOB),Switzerland

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10196673/10196677/10196712.pdf?arnumber=10196712

Reference14 articles.

1. Automated Segmentation of Lesions Including Subretinal Hyperreflective Material in Neovascular Age-related Macular Degeneration

2. Hyperreflective Foci as Biomarkers for Inflammation in Diabetic Macular Edema: Retrospective Analysis of Treatment Natextasciidieresis i ve Eyes From South India;arthi;Indian Journal of Ophthalmology,2021

3. SegNet: A deep convolutional encoder-decoder architecture for image segmentation;badrinarayanan;CoRR,2015

4. Automated Quantification of Pathological Fluids in Neovascular Age-Related Macular Degeneration, and Its Repeatability Using Deep Learning

5. Automated Segmentation of Lesions Including Subretinal Hyperreflective Material in Neovascular Age-related Macular Degeneration

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