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Neural Eng."],"published-print":{"date-parts":[[2026,6,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>\n                    <jats:italic>Objective.<\/jats:italic>\n                    As a progressive neurodegenerative disorder, Alzheimer\u2019s disease (AD) requires early and accurate diagnosis to delay pathological progression and improve clinical outcomes. Recently, Deep-learning methods based on structural MRI (sMRI) have been widely used for computer-aided AD diagnosis. However, reliable deployment remains challenging due to its sample-specific heterogeneity and the lack of explicit mechanisms for handling prediction uncertainty. In addition, common test-time perturbations and augmentation-based inference strategies could introduce anatomical inconsistency in medical images, leading to unstable decisions for hard cases.\n                    <jats:italic>Approach.<\/jats:italic>\n                    Accordingly, TriPathNet with Arbiter, as an uncertainty-aware multi-path framework, is proposed in the study. The framework learns spatially complementary representations via multi-view parallel encoding with metric-guided coordination. In addition, an augmentation-aware arbitration mechanism is introduced to adaptively correct predictions for high-uncertainty samples during inference.\n                    <jats:italic>Main results.<\/jats:italic>\n                    On the ADNI dataset for the AD vs. cognitively normal task, the proposed method achieves 98.85% accuracy, 99.18% sensitivity, 97.06% specificity, and 99.61% AUC, yielding satisfactory performance.\n                    <jats:italic>Significance.<\/jats:italic>\n                    The results indicate that TriPathNet with Arbiter provides an accurate and robust solution for sMRI-based computer-aided AD diagnosis, maintaining stable performance under test-time perturbations.\n                  <\/jats:p>","DOI":"10.1088\/1741-2552\/ae7905","type":"journal-article","created":{"date-parts":[[2026,6,5]],"date-time":"2026-06-05T22:50:19Z","timestamp":1780699819000},"page":"036035","update-policy":"https:\/\/doi.org\/10.1088\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["Uncertainty-aware multi-path framework with dynamic arbitration for Alzheimer\u2019s disease MRI classification"],"prefix":"10.1088","volume":"23","author":[{"given":"Shichao","family":"Du","sequence":"first","affiliation":[{"name":"Jilin Normal University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-7387-8223","authenticated-orcid":true,"given":"Bing","family":"Zhu","sequence":"additional","affiliation":[{"name":"Jilin Normal University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Miao","family":"Yu","sequence":"additional","affiliation":[{"name":"Jilin Normal University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Junhu","family":"Liu","sequence":"additional","affiliation":[{"name":"Jilin Normal University"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"266","published-online":{"date-parts":[[2026,6,19]]},"reference":[{"key":"jneae7905bib1","doi-asserted-by":"publisher","DOI":"10.1016\/j.arr.2024.102481","type":"journal-article","article-title":"Pathogenesis, diagnostics, and therapeutics for Alzheimer\u2019s disease: breaking the memory barrier","volume":"101","author":"Kamatham","year":"2024","journal-title":"Ageing Res. 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