Deep learning approach for automated detection of retinal pathology from ultra-widefield retinal images
Publication
, Conference
Hu, M; Amason, J; Lee, T; Gao, Q; Borkar, D; Pajic, M; Hadziahmetovic, M
Published in: INVESTIGATIVE OPHTHALMOLOGY & VISUAL SCIENCE
2021
Duke Scholars
Published In
INVESTIGATIVE OPHTHALMOLOGY & VISUAL SCIENCE
EISSN
1552-5783
ISSN
0146-0404
Publication Date
2021
Volume
62
Issue
8
Related Subject Headings
- Ophthalmology & Optometry
- 3212 Ophthalmology and optometry
- 11 Medical and Health Sciences
- 06 Biological Sciences
Citation
APA
Chicago
ICMJE
MLA
NLM
Hu, M., Amason, J., Lee, T., Gao, Q., Borkar, D., Pajic, M., & Hadziahmetovic, M. (2021). Deep learning approach for automated detection of retinal pathology from ultra-widefield retinal images. In INVESTIGATIVE OPHTHALMOLOGY & VISUAL SCIENCE (Vol. 62).
Hu, Mingzhe, Joshua Amason, Terry Lee, Qitong Gao, Durga Borkar, Miroslav Pajic, and Majda Hadziahmetovic. “Deep learning approach for automated detection of retinal pathology from ultra-widefield retinal images.” In INVESTIGATIVE OPHTHALMOLOGY & VISUAL SCIENCE, Vol. 62, 2021.
Hu M, Amason J, Lee T, Gao Q, Borkar D, Pajic M, et al. Deep learning approach for automated detection of retinal pathology from ultra-widefield retinal images. In: INVESTIGATIVE OPHTHALMOLOGY & VISUAL SCIENCE. 2021.
Hu, Mingzhe, et al. “Deep learning approach for automated detection of retinal pathology from ultra-widefield retinal images.” INVESTIGATIVE OPHTHALMOLOGY & VISUAL SCIENCE, vol. 62, no. 8, 2021.
Hu M, Amason J, Lee T, Gao Q, Borkar D, Pajic M, Hadziahmetovic M. Deep learning approach for automated detection of retinal pathology from ultra-widefield retinal images. INVESTIGATIVE OPHTHALMOLOGY & VISUAL SCIENCE. 2021.
Published In
INVESTIGATIVE OPHTHALMOLOGY & VISUAL SCIENCE
EISSN
1552-5783
ISSN
0146-0404
Publication Date
2021
Volume
62
Issue
8
Related Subject Headings
- Ophthalmology & Optometry
- 3212 Ophthalmology and optometry
- 11 Medical and Health Sciences
- 06 Biological Sciences