{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/disc-aware-ensemble-network-for-glaucoma","title":"Disc-aware Ensemble Network for Glaucoma Screening from Fundus Image","arxiv_id":"1805.07549","date":"2018-05-19","proceeding":null,"authors":["Huazhu Fu","Jun Cheng","Yanwu Xu","Changqing Zhang","Damon Wing Kee Wong","Jiang Liu","Xiaochun Cao"],"abstract":"Glaucoma is a chronic eye disease that leads to irreversible vision loss.\nMost of the existing automatic screening methods firstly segment the main\nstructure, and subsequently calculate the clinical measurement for detection\nand screening of glaucoma. However, these measurement-based methods rely\nheavily on the segmentation accuracy, and ignore various visual features. In\nthis paper, we introduce a deep learning technique to gain additional\nimage-relevant information, and screen glaucoma from the fundus image directly.\nSpecifically, a novel Disc-aware Ensemble Network (DENet) for automatic\nglaucoma screening is proposed, which integrates the deep hierarchical context\nof the global fundus image and the local optic disc region. Four deep streams\non different levels and modules are respectively considered as global image\nstream, segmentation-guided network, local disc region stream, and disc polar\ntransformation stream. Finally, the output probabilities of different streams\nare fused as the final screening result. The experiments on two glaucoma\ndatasets (SCES and new SINDI datasets) show our method outperforms other\nstate-of-the-art algorithms.","url_abs":"http://arxiv.org/abs/1805.07549v1","url_pdf":"http://arxiv.org/pdf/1805.07549v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"disc-aware-ensemble-network-for-glaucoma","repo_url":"https://github.com/Aniladepu007/Joint-Optic-Disc-and-Cup-Segmentation-Based-on-Multi-Label-Deep-Network-and-Polar-Transformation","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null},{"paper_slug":"disc-aware-ensemble-network-for-glaucoma","repo_url":"https://github.com/HzFu/DENet_GlaucomaScreen","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":null},{"paper_slug":"disc-aware-ensemble-network-for-glaucoma","repo_url":"https://github.com/HzFu/MNet_DeepCDR","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok"}}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}