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Poster
Poster
Automatic Optic Disk and Cup Segmentation of Fundus Images Using Deep Learning
- Citation Author(s):
- Submitted by:
- Akshay Chawla
- Last updated:
- 8 October 2018 - 7:18pm
- Document Type:
- Poster
- Document Year:
- 2018
- Event:
- Presenters:
- Akshay Chawla
- Paper Code:
- 2534
- Categories:
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• To automatically segment optic disk (OD) and cup regions in fundus images to derive clinical parameters, such as, cup-to-disk diameter ratio (CDR), to assist glaucoma diagnosis. An eye fundus camera is non-invasive and low-cost,
enabling the screening of a large number of patients quickly.
• Discuss various strategies on how to leverage multiple doctor annotations and prioritize pixels belonging to different regions during network optimization.
• Evaluate proposed approaches on the Drishti-GS dataset.