Abstract
The number of people suffering from retinal diseases increases with population aging and the popularity of electronic screens. Previous studies on deep learning based automatic screening generally focused on specific types of retinal diseases, such as diabetic retinopathy and glaucoma. Since patients may suffer from various types of retinal diseases simultaneously, these solutions are not clinically practical. To address this issue, we propose a novel deep learning based method that can recognise 36 different retinal diseases with a single model. More specifically, the proposed method uses a region-specific multi-task recognition model by learning diseases affecting different regions of the retina with three sub-networks. The three sub-networks are semantically trained to recognise diseases affecting optic-disc, macula and entire retina. Our contribution is two-fold. First, we use multitask learning for retinal disease classification and achieve significant improvements for recognising three main groups of retinal diseases in general, macular and optic-disc regions. Second, we collect a multi-label retinal dataset to the community as standard benchmark and release it for further research opportunities.
Original language | English |
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Title of host publication | Medical Image Computing and Computer Assisted Intervention – MICCAI 2019 |
Subtitle of host publication | 22nd International Conference, Proceedings |
Editors | Dinggang Shen, Tianming Liu, Terry M. Peters, Lawrence H. Staib, Caroline Essert, Sean Zhou, Pew-Thian Yap, Ali Khan |
Place of Publication | Cham Switzerland |
Publisher | Springer |
Pages | 30-38 |
Number of pages | 9 |
Edition | 1st |
ISBN (Electronic) | 9783030322397 |
ISBN (Print) | 9783030322380 |
DOIs | |
Publication status | Published - 2019 |
Event | Medical Image Computing and Computer-Assisted Intervention 2019 - Shenzhen, China Duration: 13 Oct 2019 → 17 Oct 2019 Conference number: 22nd https://www.miccai2019.org/ https://link.springer.com/book/10.1007/978-3-030-32239-7 (Proceedings) |
Publication series
Name | Lecture Notes in Computer Science |
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Volume | 11764 |
ISSN (Print) | 0302-9743 |
ISSN (Electronic) | 1611-3349 |
Conference
Conference | Medical Image Computing and Computer-Assisted Intervention 2019 |
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Abbreviated title | MICCAI 2019 |
Country/Territory | China |
City | Shenzhen |
Period | 13/10/19 → 17/10/19 |
Internet address |