{"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/classification-of-breast-cancer-histology","title":"Classification of Breast Cancer Histology using Deep Learning","arxiv_id":"1802.08080","date":"2018-02-22","proceeding":null,"authors":["Aditya Golatkar","Deepak Anand","Amit Sethi"],"abstract":"Breast Cancer is a major cause of death worldwide among women. Hematoxylin\nand Eosin (H&E) stained breast tissue samples from biopsies are observed under\nmicroscopes for the primary diagnosis of breast cancer. In this paper, we\npropose a deep learning-based method for classification of H&E stained breast\ntissue images released for BACH challenge 2018 by fine-tuning Inception-v3\nconvolutional neural network (CNN) proposed by Szegedy et al. These images are\nto be classified into four classes namely, i) normal tissue, ii) benign tumor,\niii) in-situ carcinoma and iv) invasive carcinoma. Our strategy is to extract\npatches based on nuclei density instead of random or grid sampling, along with\nrejection of patches that are not rich in nuclei (non-epithelial) regions for\ntraining and testing. Every patch (nuclei-dense region) in an image is\nclassified in one of the four above mentioned categories. The class of the\nentire image is determined using majority voting over the nuclear classes. We\nobtained an average four class accuracy of 85% and an average two class\n(non-cancer vs. carcinoma) accuracy of 93%, which improves upon a previous\nbenchmark by Araujo et al.","url_abs":"http://arxiv.org/abs/1802.08080v2","url_pdf":"http://arxiv.org/pdf/1802.08080v2.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":"classification-of-breast-cancer-histology","repo_url":"https://github.com/AdityaGolatkar/Classification-of-Breast-Cancer-Histology-using-Deep-Learning","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":null}],"tasks":[{"task_slug":"classification-1","task_name":"Classification"},{"task_slug":"deep-learning","task_name":"Deep Learning"},{"task_slug":"classification","task_name":"General Classification"}],"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}