{"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/reconstructing-subject-specific-effect-maps","title":"Reconstructing Subject-Specific Effect Maps","arxiv_id":"1701.02610","date":"2017-01-10","proceeding":null,"authors":["Ender Konukoglu","Ben Glocker"],"abstract":"Predictive models allow subject-specific inference when analyzing disease\nrelated alterations in neuroimaging data. Given a subject's data, inference can\nbe made at two levels: global, i.e. identifiying condition presence for the\nsubject, and local, i.e. detecting condition effect on each individual\nmeasurement extracted from the subject's data. While global inference is widely\nused, local inference, which can be used to form subject-specific effect maps,\nis rarely used because existing models often yield noisy detections composed of\ndispersed isolated islands. In this article, we propose a reconstruction\nmethod, named RSM, to improve subject-specific detections of predictive\nmodeling approaches and in particular, binary classifiers. RSM specifically\naims to reduce noise due to sampling error associated with using a finite\nsample of examples to train classifiers. The proposed method is a wrapper-type\nalgorithm that can be used with different binary classifiers in a diagnostic\nmanner, i.e. without information on condition presence. Reconstruction is posed\nas a Maximum-A-Posteriori problem with a prior model whose parameters are\nestimated from training data in a classifier-specific fashion. Experimental\nevaluation is performed on synthetically generated data and data from the\nAlzheimer's Disease Neuroimaging Initiative (ADNI) database. Results on\nsynthetic data demonstrate that using RSM yields higher detection accuracy\ncompared to using models directly or with bootstrap averaging. Analyses on the\nADNI dataset show that RSM can also improve correlation between\nsubject-specific detections in cortical thickness data and non-imaging markers\nof Alzheimer's Disease (AD), such as the Mini Mental State Examination Score\nand Cerebrospinal Fluid amyloid-$\\beta$ levels. Further reliability studies on\nthe longitudinal ADNI dataset show improvement on detection reliability when\nRSM is used.","url_abs":"http://arxiv.org/abs/1701.02610v3","url_pdf":"http://arxiv.org/pdf/1701.02610v3.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":"reconstructing-subject-specific-effect-maps","repo_url":"https://github.com/orobix/Visual-Feature-Attribution-Using-Wasserstein-GANs-Pytorch","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"unanswered"}}],"tasks":[{"task_slug":"diagnostic","task_name":"Diagnostic"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}