{"id":21280632,"url":"https://github.com/borgwardtlab/tda-cnn-ad","last_synced_at":"2025-03-15T14:11:39.520Z","repository":{"id":69455343,"uuid":"312543727","full_name":"BorgwardtLab/TDA-CNN-AD","owner":"BorgwardtLab","description":"Model combining topological descriptors with patch based MR imaging features","archived":false,"fork":false,"pushed_at":"2020-11-13T15:05:35.000Z","size":24,"stargazers_count":0,"open_issues_count":0,"forks_count":1,"subscribers_count":3,"default_branch":"main","last_synced_at":"2025-01-22T04:14:01.281Z","etag":null,"topics":[],"latest_commit_sha":null,"homepage":null,"language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/BorgwardtLab.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2020-11-13T10:25:34.000Z","updated_at":"2020-11-13T15:05:38.000Z","dependencies_parsed_at":"2023-03-11T06:07:09.716Z","dependency_job_id":null,"html_url":"https://github.com/BorgwardtLab/TDA-CNN-AD","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/BorgwardtLab%2FTDA-CNN-AD","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/BorgwardtLab%2FTDA-CNN-AD/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/BorgwardtLab%2FTDA-CNN-AD/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/BorgwardtLab%2FTDA-CNN-AD/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/BorgwardtLab","download_url":"https://codeload.github.com/BorgwardtLab/TDA-CNN-AD/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":243738987,"owners_count":20340002,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":[],"created_at":"2024-11-21T10:37:49.034Z","updated_at":"2025-03-15T14:11:39.486Z","avatar_url":"https://github.com/BorgwardtLab.png","language":"Python","funding_links":[],"categories":[],"sub_categories":[],"readme":"# TDA-CNN-AD\nModel combining topological descriptors with patch based MR imaging features.\n\nThis is a work in progress repositroy by F. Hensel and S. Brueningk according to the initial description in https://arxiv.org/abs/2011.06531. This code will be further imporved and hence may sightly deviate from the original description. \n\nIn out analysis we used T1-weighted MR images for AD and CN subjects from the Alzheimer's Disease Neuroimaging Initiative ([ADNI](http://adni.loni.usc.edu)). Data was preprocessed as described in the archive article using the [fmriprep](https://github.com/nipreps/fmriprep) pipeline. For the creation of persistence images, we first calculated the persistence diagrams of the full MRIs using [dipha](https://github.com/DIPHA/dipha) and then subsequently computed the persistence images using [persim](https://github.com/scikit-tda/persim).\n\nThe function run.py contains the code to run the image-patch-based 3D-CNN, the TDA 2D-CNN, and a combined model using both topoligical descriptors and a 3D image patch. The information for all 216 patched can be combined in a logistic regression model (ensemble model 1), whereas the preclassification layer encodings of the TDA 2D-CNN and single patch 3D-CNN can used as features for a single dense layer (ensemble model 2). \n\n\n\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fborgwardtlab%2Ftda-cnn-ad","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fborgwardtlab%2Ftda-cnn-ad","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fborgwardtlab%2Ftda-cnn-ad/lists"}