{"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/a-unified-framework-for-tumor-proliferation","title":"A Unified Framework for Tumor Proliferation Score Prediction in Breast Histopathology","arxiv_id":"1612.07180","date":"2016-12-21","proceeding":null,"authors":["Kyunghyun Paeng","Sangheum Hwang","Sunggyun Park","Minsoo Kim"],"abstract":"We present a unified framework to predict tumor proliferation scores from\nbreast histopathology whole slide images. Our system offers a fully automated\nsolution to predicting both a molecular data-based, and a mitosis\ncounting-based tumor proliferation score. The framework integrates three\nmodules, each fine-tuned to maximize the overall performance: An image\nprocessing component for handling whole slide images, a deep learning based\nmitosis detection network, and a proliferation scores prediction module. We\nhave achieved 0.567 quadratic weighted Cohen's kappa in mitosis counting-based\nscore prediction and 0.652 F1-score in mitosis detection. On Spearman's\ncorrelation coefficient, which evaluates predictive accuracy on the molecular\ndata based score, the system obtained 0.6171. Our approach won first place in\nall of the three tasks in Tumor Proliferation Assessment Challenge 2016 which\nis MICCAI grand challenge.","url_abs":"http://arxiv.org/abs/1612.07180v2","url_pdf":"http://arxiv.org/pdf/1612.07180v2.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":"a-unified-framework-for-tumor-proliferation","repo_url":"https://github.com/CODAIT/deep-histopath","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":null}],"tasks":[{"task_slug":"mitosis-detection","task_name":"Mitosis Detection"},{"task_slug":"whole-slide-images","task_name":"whole slide images"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}