000285030 001__ 285030
000285030 005__ 20260205163133.0
000285030 0247_ $$2doi$$a10.1109/TRPMS.2025.3577309
000285030 0247_ $$2ISSN$$a2469-7311
000285030 0247_ $$2ISSN$$a2469-7303
000285030 037__ $$aDZNE-2026-00155
000285030 082__ $$a624
000285030 1001_ $$00000-0002-5896-4975$$aArco, Juan E.$$b0
000285030 245__ $$aExplainable Intermodality Medical Information Transfer Using Siamese Autoencoders
000285030 260__ $$aNew York, NY$$bIEEE$$c2026
000285030 3367_ $$2DRIVER$$aarticle
000285030 3367_ $$2DataCite$$aOutput Types/Journal article
000285030 3367_ $$0PUB:(DE-HGF)16$$2PUB:(DE-HGF)$$aJournal Article$$bjournal$$mjournal$$s1770305357_11293
000285030 3367_ $$2BibTeX$$aARTICLE
000285030 3367_ $$2ORCID$$aJOURNAL_ARTICLE
000285030 3367_ $$00$$2EndNote$$aJournal Article
000285030 520__ $$aMedical imaging fusion combines complementary information from multiple modalities to enhance diagnostic accuracy. However, evaluating the quality of fused images remains challenging, with many studies relying solely on classification performance, which may lead to incorrect conclusions. We introduce a novel framework for improving image fusion, focusing on preserving fine-grained details. Our model uses a siamese autoencoder to process T1-MRI and FDG-PET images in the context of Alzheimer’s disease (AD). The framework optimizes fusion by minimizing reconstruction error between generated and input images, while maximizing differences between modalities through cosine distance. Additionally, we propose a supervised variant, incorporating binary cross-entropy loss between diagnostic labels and probabilities. Fusion quality is rigorously assessed through three tests: 1) classification of AD patients and controls using fused images; 2) an atlas-based occlusion test for identifying regions relevant to cognitive decline; and 3) analysis of structural–functional relationships via Euclidean distance. Results show an AUC of 0.92 for AD detection, reveal the involvement of brain regions linked to preclinical AD stages, and demonstrate preserved structural–functional brain networks, indicating that subtle differences are successfully captured through our fusion approach.
000285030 536__ $$0G:(DE-HGF)POF4-353$$a353 - Clinical and Health Care Research (POF4-353)$$cPOF4-353$$fPOF IV$$x0
000285030 588__ $$aDataset connected to CrossRef, Journals: pub.dzne.de
000285030 7001_ $$0P:(DE-2719)9003258$$aJimenez-Mesa, Carmen$$b1$$udzne
000285030 7001_ $$00000-0003-2690-1926$$aOrtiz, Andrés$$b2
000285030 7001_ $$aRamírez, Javier$$b3
000285030 7001_ $$0P:(DE-2719)2811659$$aLevin, Johannes$$b4$$udzne
000285030 7001_ $$00000-0001-7069-1714$$aGórriz, Juan M.$$b5
000285030 773__ $$0PERI:(DE-600)2867672-5$$a10.1109/TRPMS.2025.3577309$$gVol. 10, no. 2, p. 192 - 209$$n2$$p192 - 209$$tIEEE transactions on radiation and plasma medical sciences$$v10$$x2469-7311$$y2026
000285030 8564_ $$uhttps://pub.dzne.de/record/285030/files/DZNE-2026-00155.pdf$$yRestricted
000285030 8564_ $$uhttps://pub.dzne.de/record/285030/files/DZNE-2026-00155.pdf?subformat=pdfa$$xpdfa$$yRestricted
000285030 9101_ $$0I:(DE-HGF)0$$6P:(DE-2719)9003258$$aExternal Institute$$b1$$kExtern
000285030 9101_ $$0I:(DE-588)1065079516$$6P:(DE-2719)2811659$$aDeutsches Zentrum für Neurodegenerative Erkrankungen$$b4$$kDZNE
000285030 9131_ $$0G:(DE-HGF)POF4-353$$1G:(DE-HGF)POF4-350$$2G:(DE-HGF)POF4-300$$3G:(DE-HGF)POF4$$4G:(DE-HGF)POF$$aDE-HGF$$bGesundheit$$lNeurodegenerative Diseases$$vClinical and Health Care Research$$x0
000285030 915__ $$0StatID:(DE-HGF)0100$$2StatID$$aJCR$$bIEEE T RADIAT PLASMA : 2022$$d2025-01-07
000285030 915__ $$0StatID:(DE-HGF)0200$$2StatID$$aDBCoverage$$bSCOPUS$$d2025-01-07
000285030 915__ $$0StatID:(DE-HGF)0300$$2StatID$$aDBCoverage$$bMedline$$d2025-01-07
000285030 915__ $$0StatID:(DE-HGF)0199$$2StatID$$aDBCoverage$$bClarivate Analytics Master Journal List$$d2025-01-07
000285030 915__ $$0StatID:(DE-HGF)0112$$2StatID$$aWoS$$bEmerging Sources Citation Index$$d2025-01-07
000285030 915__ $$0StatID:(DE-HGF)0150$$2StatID$$aDBCoverage$$bWeb of Science Core Collection$$d2025-01-07
000285030 915__ $$0StatID:(DE-HGF)9900$$2StatID$$aIF < 5$$d2025-01-07
000285030 9201_ $$0I:(DE-2719)1111015$$kClinical Research (Munich)$$lClinical Research (Munich)$$x0
000285030 9201_ $$0I:(DE-2719)1111016$$kAG Levin$$lClinical Neurodegeneration$$x1
000285030 980__ $$ajournal
000285030 980__ $$aEDITORS
000285030 980__ $$aVDBINPRINT
000285030 980__ $$aI:(DE-2719)1111015
000285030 980__ $$aI:(DE-2719)1111016
000285030 980__ $$aUNRESTRICTED