{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,26]],"date-time":"2026-08-26T04:22:01Z","timestamp":1787718121845,"version":"build-2784847793"},"reference-count":28,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2026,8,24]],"date-time":"2026-08-24T00:00:00Z","timestamp":1787529600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61890951"],"award-info":[{"award-number":["61890951"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61371014"],"award-info":[{"award-number":["61371014"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"award":["61890951"],"award-info":[{"award-number":["61890951"]}],"id":[{"id":"https:\/\/ror.org\/01h0zpd94","id-type":"ROR","asserted-by":"publisher"}]},{"award":["61371014"],"award-info":[{"award-number":["61371014"]}],"id":[{"id":"https:\/\/ror.org\/01h0zpd94","id-type":"ROR","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100008416","name":"Wuhan National Laboratory for Optoelectronics","doi-asserted-by":"crossref","award":["WNLOKF027"],"award-info":[{"award-number":["WNLOKF027"]}],"id":[{"id":"10.13039\/501100008416","id-type":"DOI","asserted-by":"crossref"}]},{"award":["WNLOKF027"],"award-info":[{"award-number":["WNLOKF027"]}],"id":[{"id":"https:\/\/ror.org\/03c9ncn37","id-type":"ROR","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["NeuroSci"],"abstract":"<jats:p>Alzheimer\u2019s disease (AD) classification from structural magnetic resonance imaging (MRI) may benefit from weak supervision that uses clinically meaningful but imperfect supervisory signals. We evaluated a weakly supervised framework in which a multilayer perceptron (MLP) trained on age, sex, and Mini-Mental State Examination (MMSE) scores generated clinical pseudo-labels to initialize a patch-based fully convolutional network (FCN). For 260 Alzheimer\u2019s Disease Neuroimaging Initiative (ADNI) training participants, subsequent refinement combined 80% of the preceding MRI-model probability with 20% of the participant\u2019s ground-truth diagnostic label. This design preserves a dominant pseudo-label\/self-training component while using partial diagnostic guidance to stabilize refinement. The FCN generated whole-brain probability maps, and selected voxel probabilities were classified by a second MLP. The framework was developed using ADNI (n = 417). Using ADNI validation data only, iteration 3 and a classification threshold of 0.5 were selected and then applied unchanged to the held-out ADNI test set and the external AIBL (n = 182), FHS (n = 102), and NACC (n = 265) cohorts. The selected model achieved F1 scores of 0.853 in ADNI, 0.707 in AIBL, 0.765 in FHS, and 0.807 in NACC. These results support the feasibility and cross-cohort transferability of clinical pseudo-label-based weak supervision for MRI classification. The framework is not intended to be label-free; rather, it provides a transparent strategy for integrating imperfect clinical pseudo-labels with partially weighted diagnostic guidance during training.<\/jats:p>","DOI":"10.3390\/neurosci7050094","type":"journal-article","created":{"date-parts":[[2026,8,24]],"date-time":"2026-08-24T14:49:34Z","timestamp":1787582974000},"page":"94","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Weakly Supervised MRI-Based Classification of Alzheimer\u2019s Disease Using Clinical Pseudo-Labels"],"prefix":"10.3390","volume":"7","author":[{"given":"Rong","family":"Xiao","sequence":"first","affiliation":[{"name":"Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan 430074, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tingwei","family":"Quan","sequence":"additional","affiliation":[{"name":"Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan 430074, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9778-0864","authenticated-orcid":false,"given":"Xinglong","family":"Wu","sequence":"additional","affiliation":[{"name":"Hubei Key Laboratory of Intelligent Robot, School of Computer Science and Engineering, Wuhan Institute of Technology, Wuhan 430074, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Guoping","family":"Xu","sequence":"additional","affiliation":[{"name":"Hubei Key Laboratory of Intelligent Robot, School of Computer Science and Engineering, Wuhan Institute of Technology, Wuhan 430074, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5773-0360","authenticated-orcid":false,"given":"Shangbin","family":"Chen","sequence":"additional","affiliation":[{"name":"Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan 430074, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,8,24]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"519","DOI":"10.1016\/S1474-4422(04)00853-1","article-title":"PET imaging of amyloid in Alzheimer\u2019s disease","volume":"3","author":"Nordberg","year":"2004","journal-title":"Lancet Neurol."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"570","DOI":"10.1016\/j.jalz.2018.12.001","article-title":"Predicting diagnosis and cognition with 18F-AV-1451 tau PET and structural MRI in Alzheimer\u2019s disease","volume":"15","author":"Mattsson","year":"2019","journal-title":"Alzheimer\u2019s Dement."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"207","DOI":"10.1016\/S1474-4422(12)70291-0","article-title":"Tracking pathophysiological processes in Alzheimer\u2019s disease: An updated hypothetical model of dynamic biomarkers","volume":"12","author":"Jack","year":"2013","journal-title":"Lancet Neurol."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"868","DOI":"10.1016\/S1474-4422(12)70200-4","article-title":"Neuroimaging correlates of pathologically defined subtypes of Alzheimer\u2019s disease: A case-control study","volume":"11","author":"Whitwell","year":"2012","journal-title":"Lancet Neurol."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"67","DOI":"10.1038\/nrneurol.2009.215","article-title":"The clinical use of structural MRI in Alzheimer disease","volume":"6","author":"Frisoni","year":"2010","journal-title":"Nat. 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