{"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/an-overview-of-deep-learning-in-medical","title":"An overview of deep learning in medical imaging focusing on MRI","arxiv_id":"1811.10052","date":"2018-11-25","proceeding":null,"authors":["Alexander Selvikvåg Lundervold","Arvid Lundervold"],"abstract":"What has happened in machine learning lately, and what does it mean for the\nfuture of medical image analysis? Machine learning has witnessed a tremendous\namount of attention over the last few years. The current boom started around\n2009 when so-called deep artificial neural networks began outperforming other\nestablished models on a number of important benchmarks. Deep neural networks\nare now the state-of-the-art machine learning models across a variety of areas,\nfrom image analysis to natural language processing, and widely deployed in\nacademia and industry. These developments have a huge potential for medical\nimaging technology, medical data analysis, medical diagnostics and healthcare\nin general, slowly being realized. We provide a short overview of recent\nadvances and some associated challenges in machine learning applied to medical\nimage processing and image analysis. As this has become a very broad and fast\nexpanding field we will not survey the entire landscape of applications, but\nput particular focus on deep learning in MRI.\n  Our aim is threefold: (i) give a brief introduction to deep learning with\npointers to core references; (ii) indicate how deep learning has been applied\nto the entire MRI processing chain, from acquisition to image retrieval, from\nsegmentation to disease prediction; (iii) provide a starting point for people\ninterested in experimenting and perhaps contributing to the field of machine\nlearning for medical imaging by pointing out good educational resources,\nstate-of-the-art open-source code, and interesting sources of data and problems\nrelated medical imaging.","url_abs":"http://arxiv.org/abs/1811.10052v2","url_pdf":"http://arxiv.org/pdf/1811.10052v2.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":"an-overview-of-deep-learning-in-medical","repo_url":"https://github.com/MMIV-ML/DLMI2018","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[{"task_slug":"machine-learning","task_name":"BIG-bench Machine Learning"},{"task_slug":"deep-learning","task_name":"Deep Learning"},{"task_slug":"disease-prediction","task_name":"Disease Prediction"},{"task_slug":"image-retrieval","task_name":"Image Retrieval"},{"task_slug":"medical-image-analysis","task_name":"Medical Image Analysis"},{"task_slug":"retrieval","task_name":"Retrieval"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":"https://app.syntology.ai/?focus=1811.10052","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}