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Few-shot Learning Using a Small-Sized Dataset of High-Resolution FUNDUS Images for Glaucoma Diagnosis
2017
Proceedings of the 2nd International Workshop on Multimedia for Personal Health and Health Care - MMHealth '17
Deep learning has recently attracted a lot of attention, mainly thanks to substantial gains in terms of effectiveness. However, there is still room for significant improvement, especially when dealing with use cases that come with a limited availability of data, as is often the case in the area of medical image analysis. In this paper, we introduce a novel approach for early diagnosis of glaucoma in high-resolution FUNDUS images, only requiring a small number of training samples. In particular,
doi:10.1145/3132635.3132650
dblp:conf/mm/KimZN17
fatcat:2ssbx4bblffpncwvcvv2lem6ee