{"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/two-stage-convolutional-neural-network-for","title":"Two-Stage Convolutional Neural Network for Breast Cancer Histology Image Classification","arxiv_id":"1803.04054","date":"2018-03-11","proceeding":null,"authors":["Kamyar Nazeri","Azad Aminpour","Mehran Ebrahimi"],"abstract":"This paper explores the problem of breast tissue classification of microscopy\nimages. Based on the predominant cancer type the goal is to classify images\ninto four categories of normal, benign, in situ carcinoma, and invasive\ncarcinoma. Given a suitable training dataset, we utilize deep learning\ntechniques to address the classification problem. Due to the large size of each\nimage in the training dataset, we propose a patch-based technique which\nconsists of two consecutive convolutional neural networks. The first\n\"patch-wise\" network acts as an auto-encoder that extracts the most salient\nfeatures of image patches while the second \"image-wise\" network performs\nclassification of the whole image. The first network is pre-trained and aimed\nat extracting local information while the second network obtains global\ninformation of an input image. We trained the networks using the ICIAR 2018\ngrand challenge on BreAst Cancer Histology (BACH) dataset. The proposed method\nyields 95 % accuracy on the validation set compared to previously reported 77 %\naccuracy rates in the literature. Our code is publicly available at\nhttps://github.com/ImagingLab/ICIAR2018","url_abs":"http://arxiv.org/abs/1803.04054v2","url_pdf":"http://arxiv.org/pdf/1803.04054v2.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":"two-stage-convolutional-neural-network-for","repo_url":"https://github.com/ImagingLab/ICIAR2018","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"breast-cancer-histology-image-classification","task_name":"Breast Cancer Histology Image Classification"},{"task_slug":"classification-1","task_name":"Classification"},{"task_slug":"classification","task_name":"General Classification"},{"task_slug":"image-classification","task_name":"Image Classification"},{"task_slug":"two","task_name":"Vocal Bursts Valence Prediction"},{"task_slug":"image-classification","task_name":"image-classification"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1803.04054","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}