{"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/diabetic-retinopathy-detection-via-deep","title":"Diabetic Retinopathy Detection via Deep Convolutional Networks for Discriminative Localization and Visual Explanation","arxiv_id":"1703.10757","date":"2017-03-31","proceeding":null,"authors":["Zhiguang Wang","Jianbo Yang"],"abstract":"We proposed a deep learning method for interpretable diabetic retinopathy (DR) detection. The visual-interpretable feature of the proposed method is achieved by adding the regression activation map (RAM) after the global averaging pooling layer of the convolutional networks (CNN). With RAM, the proposed model can localize the discriminative regions of an retina image to show the specific region of interest in terms of its severity level. We believe this advantage of the proposed deep learning model is highly desired for DR detection because in practice, users are not only interested with high prediction performance, but also keen to understand the insights of DR detection and why the adopted learning model works. In the experiments conducted on a large scale of retina image dataset, we show that the proposed CNN model can achieve high performance on DR detection compared with the state-of-the-art while achieving the merits of providing the RAM to highlight the salient regions of the input image.","url_abs":"https://arxiv.org/abs/1703.10757v3","url_pdf":"https://arxiv.org/pdf/1703.10757v3.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":"diabetic-retinopathy-detection-via-deep","repo_url":"https://github.com/cauchyturing/kaggle_diabetic_RAM","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null},{"paper_slug":"diabetic-retinopathy-detection-via-deep","repo_url":"https://github.com/Ram-Aditya/Healthcare-Data-Analytics","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"diabetic-retinopathy-detection","task_name":"Diabetic Retinopathy Detection"}],"methods":[],"datasets_introduced":[{"slug":"diabetic-retinopathy-detection-dataset","name":"Diabetic Retinopathy Detection Dataset","full_name":""}],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/1703.10757","atlas_url":"https://app.syntology.ai/?focus=1703.10757","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}