{"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/weakly-supervised-multiple-instance-learning","title":"Weakly supervised multiple instance learning histopathological tumor segmentation","arxiv_id":"2004.05024","date":"2020-04-10","proceeding":null,"authors":["Marvin Lerousseau","Maria Vakalopoulou","Marion Classe","Julien Adam","Enzo Battistella","Alexandre Carré","Théo Estienne","Théophraste Henry","Eric Deutsch","Nikos Paragios"],"abstract":"Histopathological image segmentation is a challenging and important topic in medical imaging with tremendous potential impact in clinical practice. State of the art methods rely on hand-crafted annotations which hinder clinical translation since histology suffers from significant variations between cancer phenotypes. In this paper, we propose a weakly supervised framework for whole slide imaging segmentation that relies on standard clinical annotations, available in most medical systems. In particular, we exploit a multiple instance learning scheme for training models. The proposed framework has been evaluated on multi-locations and multi-centric public data from The Cancer Genome Atlas and the PatchCamelyon dataset. Promising results when compared with experts' annotations demonstrate the potentials of the presented approach. The complete framework, including $6481$ generated tumor maps and data processing, is available at https://github.com/marvinler/tcga_segmentation.","url_abs":"https://arxiv.org/abs/2004.05024v4","url_pdf":"https://arxiv.org/pdf/2004.05024v4.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":"weakly-supervised-multiple-instance-learning","repo_url":"https://github.com/MarvinLer/tcga_segmentation","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":{"status":"ok","spdx":"AGPL-3.0"}}],"tasks":[{"task_slug":"histopathological-segmentation","task_name":"Histopathological Segmentation"},{"task_slug":"image-segmentation","task_name":"Image Segmentation"},{"task_slug":"multiple-instance-learning","task_name":"Multiple Instance Learning"},{"task_slug":"segmentation","task_name":"Segmentation"},{"task_slug":"semantic-segmentation","task_name":"Semantic Segmentation"},{"task_slug":"translation","task_name":"Translation"},{"task_slug":"tumor-segmentation","task_name":"Tumor Segmentation"},{"task_slug":"whole-slide-images","task_name":"whole slide images"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":"https://app.syntology.ai/?focus=2004.05024","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}