{"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/residual-and-plain-convolutional-neural","title":"Residual and Plain Convolutional Neural Networks for 3D Brain MRI Classification","arxiv_id":"1701.06643","date":"2017-01-23","proceeding":null,"authors":["Sergey Korolev","Amir Safiullin","Mikhail Belyaev","Yulia Dodonova"],"abstract":"In the recent years there have been a number of studies that applied deep\nlearning algorithms to neuroimaging data. Pipelines used in those studies\nmostly require multiple processing steps for feature extraction, although\nmodern advancements in deep learning for image classification can provide a\npowerful framework for automatic feature generation and more straightforward\nanalysis. In this paper, we show how similar performance can be achieved\nskipping these feature extraction steps with the residual and plain 3D\nconvolutional neural network architectures. We demonstrate the performance of\nthe proposed approach for classification of Alzheimer's disease versus mild\ncognitive impairment and normal controls on the Alzheimer's Disease National\nInitiative (ADNI) dataset of 3D structural MRI brain scans.","url_abs":"http://arxiv.org/abs/1701.06643v1","url_pdf":"http://arxiv.org/pdf/1701.06643v1.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":"residual-and-plain-convolutional-neural","repo_url":"https://github.com/mediteamC/teamC","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok"}},{"paper_slug":"residual-and-plain-convolutional-neural","repo_url":"https://github.com/neuro-ml/resnet_cnn_mri_adni","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok"}},{"paper_slug":"residual-and-plain-convolutional-neural","repo_url":"https://github.com/west-gates/3DCNN-Vis","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok"}}],"tasks":[{"task_slug":"classification-1","task_name":"Classification"},{"task_slug":"deep-learning","task_name":"Deep Learning"},{"task_slug":"classification","task_name":"General Classification"},{"task_slug":"image-classification","task_name":"Image Classification"},{"task_slug":"mri-classification","task_name":"MRI classification"},{"task_slug":"image-classification","task_name":"image-classification"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/1701.06643","atlas_url":"https://app.syntology.ai/?focus=1701.06643","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}