{"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/y-net-joint-segmentation-and-classification","title":"Y-Net: Joint Segmentation and Classification for Diagnosis of Breast Biopsy Images","arxiv_id":"1806.01313","date":"2018-06-04","proceeding":null,"authors":["Sachin Mehta","Ezgi Mercan","Jamen Bartlett","Donald Weave","Joann G. Elmore","Linda Shapiro"],"abstract":"In this paper, we introduce a conceptually simple network for generating\ndiscriminative tissue-level segmentation masks for the purpose of breast cancer\ndiagnosis. Our method efficiently segments different types of tissues in breast\nbiopsy images while simultaneously predicting a discriminative map for\nidentifying important areas in an image. Our network, Y-Net, extends and\ngeneralizes U-Net by adding a parallel branch for discriminative map generation\nand by supporting convolutional block modularity, which allows the user to\nadjust network efficiency without altering the network topology. Y-Net delivers\nstate-of-the-art segmentation accuracy while learning 6.6x fewer parameters\nthan its closest competitors. The addition of descriptive power from Y-Net's\ndiscriminative segmentation masks improve diagnostic classification accuracy by\n7% over state-of-the-art methods for diagnostic classification. Source code is\navailable at: https://sacmehta.github.io/YNet.","url_abs":"http://arxiv.org/abs/1806.01313v1","url_pdf":"http://arxiv.org/pdf/1806.01313v1.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":"y-net-joint-segmentation-and-classification","repo_url":"https://github.com/sacmehta/YNet","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null},{"paper_slug":"y-net-joint-segmentation-and-classification","repo_url":"https://github.com/sacmehta/3d-espnet","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"descriptive","task_name":"Descriptive"},{"task_slug":"diagnostic","task_name":"Diagnostic"},{"task_slug":"classification","task_name":"General Classification"},{"task_slug":"medical-image-segmentation","task_name":"Medical Image Segmentation"},{"task_slug":"segmentation","task_name":"Segmentation"}],"methods":[{"method_slug":"concatenated-skip-connection","method_name":"Concatenated Skip Connection"},{"method_slug":"convolution","method_name":"Convolution"},{"method_slug":"max-pooling","method_name":"Max Pooling"},{"method_slug":"relu","method_name":"ReLU"},{"method_slug":"u-net","method_name":"U-Net"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}