{"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/alzheimers-disease-diagnostics-by-adaptation","title":"Alzheimer's Disease Diagnostics by Adaptation of 3D Convolutional Network","arxiv_id":"1607.00455","date":"2016-07-02","proceeding":null,"authors":["Ehsan Hosseini-Asl","Robert Keynto","Ayman El-Baz"],"abstract":"Early diagnosis, playing an important role in preventing progress and\ntreating the Alzheimer\\{'}s disease (AD), is based on classification of\nfeatures extracted from brain images. The features have to accurately capture\nmain AD-related variations of anatomical brain structures, such as, e.g.,\nventricles size, hippocampus shape, cortical thickness, and brain volume. This\npaper proposed to predict the AD with a deep 3D convolutional neural network\n(3D-CNN), which can learn generic features capturing AD biomarkers and adapt to\ndifferent domain datasets. The 3D-CNN is built upon a 3D convolutional\nautoencoder, which is pre-trained to capture anatomical shape variations in\nstructural brain MRI scans. Fully connected upper layers of the 3D-CNN are then\nfine-tuned for each task-specific AD classification. Experiments on the\nCADDementia MRI dataset with no skull-stripping preprocessing have shown our\n3D-CNN outperforms several conventional classifiers by accuracy. Abilities of\nthe 3D-CNN to generalize the features learnt and adapt to other domains have\nbeen validated on the ADNI dataset.","url_abs":"http://arxiv.org/abs/1607.00455v1","url_pdf":"http://arxiv.org/pdf/1607.00455v1.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":"alzheimers-disease-diagnostics-by-adaptation","repo_url":"https://github.com/ehosseiniasl/3d-convolutional-network","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"classification","task_name":"General Classification"},{"task_slug":null,"task_name":"Hippocampus"},{"task_slug":"skull-stripping","task_name":"Skull Stripping"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}