{"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/deep-retinal-image-understanding","title":"Deep Retinal Image Understanding","arxiv_id":"1609.01103","date":"2016-09-05","proceeding":null,"authors":["Kevis-Kokitsi Maninis","Jordi Pont-Tuset","Pablo Arbeláez","Luc van Gool"],"abstract":"This paper presents Deep Retinal Image Understanding (DRIU), a unified\nframework of retinal image analysis that provides both retinal vessel and optic\ndisc segmentation. We make use of deep Convolutional Neural Networks (CNNs),\nwhich have proven revolutionary in other fields of computer vision such as\nobject detection and image classification, and we bring their power to the\nstudy of eye fundus images. DRIU uses a base network architecture on which two\nset of specialized layers are trained to solve both the retinal vessel and\noptic disc segmentation. We present experimental validation, both qualitative\nand quantitative, in four public datasets for these tasks. In all of them, DRIU\npresents super-human performance, that is, it shows results more consistent\nwith a gold standard than a second human annotator used as control.","url_abs":"http://arxiv.org/abs/1609.01103v1","url_pdf":"http://arxiv.org/pdf/1609.01103v1.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":"deep-retinal-image-understanding","repo_url":"https://github.com/PB17151764/2020UM-Summer-Research","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"classification","task_name":"General Classification"},{"task_slug":"image-classification","task_name":"Image Classification"},{"task_slug":"object-detection","task_name":"Object Detection"},{"task_slug":"optic-disc-segmentation","task_name":"Optic Disc Segmentation"},{"task_slug":"segmentation","task_name":"Segmentation"},{"task_slug":"image-classification","task_name":"image-classification"},{"task_slug":"object-detection-1","task_name":"object-detection"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}