{"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/exploring-the-similarity-of-medical-imaging","title":"Exploring the similarity of medical imaging classification problems","arxiv_id":"1706.03509","date":"2017-06-12","proceeding":null,"authors":["Veronika Cheplygina","Pim Moeskops","Mitko Veta","Behdad Dasht Bozorg","Josien Pluim"],"abstract":"Supervised learning is ubiquitous in medical image analysis. In this paper we\nconsider the problem of meta-learning -- predicting which methods will perform\nwell in an unseen classification problem, given previous experience with other\nclassification problems. We investigate the first step of such an approach: how\nto quantify the similarity of different classification problems. We\ncharacterize datasets sampled from six classification problems by performance\nranks of simple classifiers, and define the similarity by the inverse of\nEuclidean distance in this meta-feature space. We visualize the similarities in\na 2D space, where meaningful clusters start to emerge, and show that the\nproposed representation can be used to classify datasets according to their\norigin with 89.3\\% accuracy. These findings, together with the observations of\nrecent trends in machine learning, suggest that meta-learning could be a\nvaluable tool for the medical imaging community.","url_abs":"http://arxiv.org/abs/1706.03509v1","url_pdf":"http://arxiv.org/pdf/1706.03509v1.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":"exploring-the-similarity-of-medical-imaging","repo_url":"https://github.com/tueimage/similarity-medical-2017","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"classification-1","task_name":"Classification"},{"task_slug":"classification","task_name":"General Classification"},{"task_slug":"medical-image-analysis","task_name":"Medical Image Analysis"},{"task_slug":"meta-learning","task_name":"Meta-Learning"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}