{"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/survival-prediction-using-ensemble-tumor","title":"Survival prediction using ensemble tumor segmentation and transfer learning","arxiv_id":"1810.04274","date":"2018-10-04","proceeding":null,"authors":["Mariano Cabezas","Sergi Valverde","Sandra González-Villà","Albert Clérigues","Mostafa Salem","Kaisar Kushibar","Jose Bernal","Arnau Oliver","Xavier Lladó"],"abstract":"Segmenting tumors and their subregions is a challenging task as demonstrated\nby the annual BraTS challenge. Moreover, predicting the survival of the patient\nusing mainly imaging features, while being a desirable outcome to evaluate the\ntreatment of the patient, it is also a difficult task. In this paper, we\npresent a cascaded pipeline to segment the tumor and its subregions and then we\nuse these results and other clinical features together with image features\ncoming from a pretrained VGG-16 network to predict the survival of the patient.\nPreliminary results with the training and validation dataset show a promising\nstart in terms of segmentation, while the prediction values could be improved\nwith further testing on the feature extraction part of the network.","url_abs":"http://arxiv.org/abs/1810.04274v1","url_pdf":"http://arxiv.org/pdf/1810.04274v1.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":"survival-prediction-using-ensemble-tumor","repo_url":"https://github.com/marianocabezas/challenges2018","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"survival-prediction","task_name":"Survival Prediction"},{"task_slug":"transfer-learning","task_name":"Transfer Learning"},{"task_slug":"tumor-segmentation","task_name":"Tumor Segmentation"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}