{"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/histopathological-image-classification-using","title":"Histopathological Image Classification using Discriminative Feature-oriented Dictionary Learning","arxiv_id":"1506.05032","date":"2015-06-16","proceeding":null,"authors":["Tiep Huu Vu","Hojjat Seyed Mousavi","Vishal Monga","Arvind UK Rao","Ganesh Rao"],"abstract":"In histopathological image analysis, feature extraction for classification is\na challenging task due to the diversity of histology features suitable for each\nproblem as well as presence of rich geometrical structures. In this paper, we\npropose an automatic feature discovery framework via learning class-specific\ndictionaries and present a low-complexity method for classification and disease\ngrading in histopathology. Essentially, our Discriminative Feature-oriented\nDictionary Learning (DFDL) method learns class-specific dictionaries such that\nunder a sparsity constraint, the learned dictionaries allow representing a new\nimage sample parsimoniously via the dictionary corresponding to the class\nidentity of the sample. At the same time, the dictionary is designed to be\npoorly capable of representing samples from other classes. Experiments on three\nchallenging real-world image databases: 1) histopathological images of\nintraductal breast lesions, 2) mammalian kidney, lung and spleen images\nprovided by the Animal Diagnostics Lab (ADL) at Pennsylvania State University,\nand 3) brain tumor images from The Cancer Genome Atlas (TCGA) database, reveal\nthe merits of our proposal over state-of-the-art alternatives. {Moreover, we\ndemonstrate that DFDL exhibits a more graceful decay in classification accuracy\nagainst the number of training images which is highly desirable in practice\nwhere generous training is often not available","url_abs":"http://arxiv.org/abs/1506.05032v5","url_pdf":"http://arxiv.org/pdf/1506.05032v5.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":"histopathological-image-classification-using","repo_url":"https://github.com/tiepvupsu/DICTOL","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"unanswered"}},{"paper_slug":"histopathological-image-classification-using","repo_url":"https://github.com/tiepvupsu/DICTOL_python","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"none","reach":{"status":"unanswered"}}],"tasks":[{"task_slug":"classification-1","task_name":"Classification"},{"task_slug":"dictionary-learning","task_name":"Dictionary Learning"},{"task_slug":"classification","task_name":"General Classification"},{"task_slug":"image-classification","task_name":"Image Classification"},{"task_slug":"medical-image-analysis","task_name":"Medical Image Analysis"},{"task_slug":"image-classification","task_name":"image-classification"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1506.05032","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}