{"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/automatic-brain-tumor-grading-from-mri-data","title":"Automatic brain tumor grading from MRI data using convolutional neural networks and quality assessment","arxiv_id":"1809.09468","date":"2018-09-25","proceeding":null,"authors":["Sergio Pereira","Raphael Meier","Victor Alves","Mauricio Reyes","Carlos A. Silva"],"abstract":"Glioblastoma Multiforme is a high grade, very aggressive, brain tumor, with\npatients having a poor prognosis. Lower grade gliomas are less aggressive, but\nthey can evolve into higher grade tumors over time. Patient management and\ntreatment can vary considerably with tumor grade, ranging from tumor resection\nfollowed by a combined radio- and chemotherapy to a \"wait and see\" approach.\nHence, tumor grading is important for adequate treatment planning and\nmonitoring. The gold standard for tumor grading relies on histopathological\ndiagnosis of biopsy specimens. However, this procedure is invasive, time\nconsuming, and prone to sampling error. Given these disadvantages, automatic\ntumor grading from widely used MRI protocols would be clinically important, as\na way to expedite treatment planning and assessment of tumor evolution. In this\npaper, we propose to use Convolutional Neural Networks for predicting tumor\ngrade directly from imaging data. In this way, we overcome the need for expert\nannotations of regions of interest. We evaluate two prediction approaches: from\nthe whole brain, and from an automatically defined tumor region. Finally, we\nemploy interpretability methodologies as a quality assurance stage to check if\nthe method is using image regions indicative of tumor grade for classification.","url_abs":"http://arxiv.org/abs/1809.09468v1","url_pdf":"http://arxiv.org/pdf/1809.09468v1.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":"automatic-brain-tumor-grading-from-mri-data","repo_url":"https://github.com/sergiormpereira/brain_tumor_grading","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"management","task_name":"Management"},{"task_slug":"prognosis","task_name":"Prognosis"}],"methods":[{"method_slug":"interpretability","method_name":"Interpretability"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}