{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,20]],"date-time":"2026-08-20T02:52:03Z","timestamp":1787194323450,"version":"build-2736575974"},"reference-count":79,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2024,5,1]],"date-time":"2024-05-01T00:00:00Z","timestamp":1714521600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2024,5,1]],"date-time":"2024-05-01T00:00:00Z","timestamp":1714521600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2024,5,1]],"date-time":"2024-05-01T00:00:00Z","timestamp":1714521600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2024,5,1]],"date-time":"2024-05-01T00:00:00Z","timestamp":1714521600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2024,5,1]],"date-time":"2024-05-01T00:00:00Z","timestamp":1714521600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2024,5,1]],"date-time":"2024-05-01T00:00:00Z","timestamp":1714521600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2024,5,1]],"date-time":"2024-05-01T00:00:00Z","timestamp":1714521600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"funder":[{"DOI":"10.13039\/501100007129","name":"Shandong Province Natural Science Foundation","doi-asserted-by":"publisher","award":["ZR2023MF062"],"award-info":[{"award-number":["ZR2023MF062"]}],"id":[{"id":"10.13039\/501100007129","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100007129","name":"Shandong Province Natural Science Foundation","doi-asserted-by":"publisher","award":["ZR2020QF011"],"award-info":[{"award-number":["ZR2020QF011"]}],"id":[{"id":"10.13039\/501100007129","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","award":["RF1AG073297"],"award-info":[{"award-number":["RF1AG073297"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","award":["R01MH108560"],"award-info":[{"award-number":["R01MH108560"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100019274","name":"National Institute of Mental Health and Neuro Sciences","doi-asserted-by":"publisher","award":["R01MH108560"],"award-info":[{"award-number":["R01MH108560"]}],"id":[{"id":"10.13039\/100019274","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100019274","name":"National Institute of Mental Health and Neuro Sciences","doi-asserted-by":"publisher","award":["RF1AG073297"],"award-info":[{"award-number":["RF1AG073297"]}],"id":[{"id":"10.13039\/100019274","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Medical Image Analysis"],"published-print":{"date-parts":[[2024,5]]},"DOI":"10.1016\/j.media.2024.103135","type":"journal-article","created":{"date-parts":[[2024,3,6]],"date-time":"2024-03-06T11:43:19Z","timestamp":1709725399000},"page":"103135","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":8,"special_numbering":"C","title":["Hybrid representation learning for cognitive diagnosis in late-life depression over 5 years with structural MRI"],"prefix":"10.1016","volume":"94","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2234-9476","authenticated-orcid":false,"given":"Lintao","family":"Zhang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9347-2124","authenticated-orcid":false,"given":"Lihong","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6625-3682","authenticated-orcid":false,"given":"Minhui","family":"Yu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2154-2258","authenticated-orcid":false,"given":"Rong","family":"Wu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"David C.","family":"Steffens","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4074-2434","authenticated-orcid":false,"given":"Guy G.","family":"Potter","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0166-0807","authenticated-orcid":false,"given":"Mingxia","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.media.2024.103135_b1","doi-asserted-by":"crossref","DOI":"10.1016\/j.ssmph.2019.100399","article-title":"Changes in depressive symptoms over age among older Americans: Differences by gender, race\/ethnicity, education, and birth cohort","volume":"7","author":"Abrams","year":"2019","journal-title":"SSM-Population Health"},{"issue":"1","key":"10.1016\/j.media.2024.103135_b2","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/s41398-019-0514-6","article-title":"Mechanisms and treatment of late-life depression","volume":"9","author":"Alexopoulos","year":"2019","journal-title":"Transl. Psychiatry"},{"issue":"1","key":"10.1016\/j.media.2024.103135_b3","first-page":"9","article-title":"Survey on multiclass classification methods","volume":"19","author":"Aly","year":"2005","journal-title":"Neural Netw."},{"key":"10.1016\/j.media.2024.103135_b4","article-title":"Exploratory boosted feature selection and neural network framework for depression classification","author":"Arun","year":"2018","journal-title":"Int. J. Interact. Multimedia Artif. Intell."},{"key":"10.1016\/j.media.2024.103135_b5","series-title":"2018 IEEE Symposium Series on Computational Intelligence","first-page":"41","article-title":"A boosted machine learning approach for detection of depression","author":"Arun","year":"2018"},{"key":"10.1016\/j.media.2024.103135_b6","doi-asserted-by":"crossref","first-page":"2021","DOI":"10.2147\/CIA.S114100","article-title":"Assessment of late-life depression via self-report measures: A review","volume":"13","author":"Balsamo","year":"2018","journal-title":"Clin. Interv. Aging"},{"issue":"2","key":"10.1016\/j.media.2024.103135_b7","doi-asserted-by":"crossref","first-page":"118","DOI":"10.1016\/j.biopsych.2021.04.004","article-title":"Macro-and microscale stress\u2013associated alterations in brain structure: Translational link with depression","volume":"90","author":"Banasr","year":"2021","journal-title":"Biol. Psychiat."},{"key":"10.1016\/j.media.2024.103135_b8","doi-asserted-by":"crossref","DOI":"10.1016\/j.cie.2022.108060","article-title":"Detection of melanoma in dermoscopic images by integrating features extracted using handcrafted and deep learning models","volume":"168","author":"Bansal","year":"2022","journal-title":"Comput. Ind. Eng."},{"issue":"3","key":"10.1016\/j.media.2024.103135_b9","doi-asserted-by":"crossref","first-page":"908","DOI":"10.1002\/jmri.27908","article-title":"Deep generative medical image harmonization for improving cross-site generalization in deep learning predictors","volume":"55","author":"Bashyam","year":"2022","journal-title":"J. Magn. Resonance Imaging"},{"key":"10.1016\/j.media.2024.103135_b10","doi-asserted-by":"crossref","DOI":"10.1016\/j.neuroimage.2021.117834","article-title":"Associations between depression, lifestyle and brain structure: A longitudinal MRI study","volume":"231","author":"Binnewies","year":"2021","journal-title":"Neuroimage"},{"key":"10.1016\/j.media.2024.103135_b11","doi-asserted-by":"crossref","first-page":"182","DOI":"10.1016\/j.eswa.2019.05.023","article-title":"A text classification framework for simple and effective early depression detection over social media streams","volume":"133","author":"Burdisso","year":"2019","journal-title":"Expert Syst. Appl."},{"issue":"2","key":"10.1016\/j.media.2024.103135_b12","doi-asserted-by":"crossref","first-page":"325","DOI":"10.1007\/s40120-019-00148-5","article-title":"Diagnosing and treating depression in patients with Alzheimer\u2019s disease","volume":"8","author":"Burke","year":"2019","journal-title":"Neurol. Therapy"},{"issue":"3","key":"10.1016\/j.media.2024.103135_b13","doi-asserted-by":"crossref","first-page":"345","DOI":"10.31887\/DCNS.2008.10.3\/mabutters","article-title":"Pathways linking late-life depression to persistent cognitive impairment and dementia","volume":"10","author":"Butters","year":"2008","journal-title":"Dialogues Clin. Neurosci."},{"key":"10.1016\/j.media.2024.103135_b14","doi-asserted-by":"crossref","unstructured":"Chen, T., Guestrin, C., 2016. Xgboost: A scalable tree boosting system. In: Proceedings of the 22nd Acm Sigkdd International Conference on Knowledge Discovery and Data Mining. pp. 785\u2013794.","DOI":"10.1145\/2939672.2939785"},{"key":"10.1016\/j.media.2024.103135_b15","series-title":"Med3d: Transfer learning for 3D medical image analysis","author":"Chen","year":"2019"},{"issue":"7","key":"10.1016\/j.media.2024.103135_b16","doi-asserted-by":"crossref","first-page":"1805","DOI":"10.1109\/TBME.2015.2404809","article-title":"Domain transfer learning for MCI conversion prediction","volume":"62","author":"Cheng","year":"2015","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"10.1016\/j.media.2024.103135_b17","series-title":"Bert: Pre-training of deep bidirectional transformers for language understanding","author":"Devlin","year":"2018"},{"key":"10.1016\/j.media.2024.103135_b18","series-title":"An image is worth 16x16 words: Transformers for image recognition at scale","author":"Dosovitskiy","year":"2020"},{"key":"10.1016\/j.media.2024.103135_b19","series-title":"Computer Age Statistical Inference, Student Edition: Algorithms, Evidence, and Data Science","author":"Efron","year":"2021"},{"issue":"1","key":"10.1016\/j.media.2024.103135_b20","first-page":"1","article-title":"The self-reported Montgomery-\u00c5sberg depression rating scale is a useful evaluative tool in major depressive disorder","volume":"9","author":"Fantino","year":"2009","journal-title":"BMC Psych."},{"key":"10.1016\/j.media.2024.103135_b21","doi-asserted-by":"crossref","first-page":"104","DOI":"10.1016\/j.neuroimage.2017.11.024","article-title":"Harmonization of cortical thickness measurements across scanners and sites","volume":"167","author":"Fortin","year":"2018","journal-title":"NeuroImage"},{"issue":"3","key":"10.1016\/j.media.2024.103135_b22","doi-asserted-by":"crossref","first-page":"198","DOI":"10.1016\/S0004-9514(05)70034-9","article-title":"Mini-mental state examination (MMSE)","volume":"51","author":"Galea","year":"2005","journal-title":"Austral. J. Physiotherapy"},{"issue":"11","key":"10.1016\/j.media.2024.103135_b23","doi-asserted-by":"crossref","first-page":"1037","DOI":"10.1111\/cns.13048","article-title":"Machine learning in major depression: From classification to treatment outcome prediction","volume":"24","author":"Gao","year":"2018","journal-title":"CNS Neurosci. Therapeut."},{"issue":"5","key":"10.1016\/j.media.2024.103135_b24","doi-asserted-by":"crossref","first-page":"339","DOI":"10.1016\/j.biopsych.2016.12.032","article-title":"Late-life depression, hippocampal volumes, and hypothalamic-pituitary-adrenal axis regulation: a systematic review and meta-analysis","volume":"82","author":"Geerlings","year":"2017","journal-title":"Biol. Psychiat."},{"key":"10.1016\/j.media.2024.103135_b25","doi-asserted-by":"crossref","first-page":"14","DOI":"10.1016\/j.neurobiolaging.2021.09.025","article-title":"Depressive symptom profiles predict dementia onset and brain pathology in older persons. The AGES-Reykjavik study","volume":"111","author":"Gerritsen","year":"2022","journal-title":"Neurobiol. Aging"},{"key":"10.1016\/j.media.2024.103135_b26","doi-asserted-by":"crossref","first-page":"39","DOI":"10.1016\/j.media.2019.01.004","article-title":"Training recurrent neural networks robust to incomplete data: Application to Alzheimer\u2019s disease progression modeling","volume":"53","author":"Ghazi","year":"2019","journal-title":"Med. Image Anal."},{"issue":"3","key":"10.1016\/j.media.2024.103135_b27","doi-asserted-by":"crossref","first-page":"1173","DOI":"10.1109\/TBME.2021.3117407","article-title":"Domain adaptation for medical image analysis: A survey","volume":"69","author":"Guan","year":"2021","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"10.1016\/j.media.2024.103135_b28","series-title":"International Conference on Medical Image Computing and Computer-Assisted Intervention","first-page":"248","article-title":"Cost-sensitive meta-learning for progress prediction of subjective cognitive decline with brain structural MRI","author":"Guan","year":"2021"},{"issue":"4","key":"10.1016\/j.media.2024.103135_b29","doi-asserted-by":"crossref","first-page":"1385","DOI":"10.1109\/JBHI.2019.2891049","article-title":"Deep learning and handcrafted method fusion: higher diagnostic accuracy for melanoma dermoscopy images","volume":"23","author":"Hagerty","year":"2019","journal-title":"IEEE J. Biomed. Health Inf."},{"key":"10.1016\/j.media.2024.103135_b30","series-title":"The Elements of Statistical Learning: Data Mining, Inference, and Prediction","author":"Hastie","year":"2009"},{"key":"10.1016\/j.media.2024.103135_b31","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J., 2016. Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp. 770\u2013778.","DOI":"10.1109\/CVPR.2016.90"},{"issue":"11","key":"10.1016\/j.media.2024.103135_b32","doi-asserted-by":"crossref","first-page":"1339","DOI":"10.1586\/ern.12.127","article-title":"The association between late-life depression, mild cognitive impairment and dementia: Is inflammation the missing link?","volume":"12","author":"Hermida","year":"2012","journal-title":"Exp. Rev. Neurother."},{"issue":"2","key":"10.1016\/j.media.2024.103135_b33","doi-asserted-by":"crossref","first-page":"119","DOI":"10.2217\/nmt.14.5","article-title":"Depression versus dementia: is this construct still relevant?","volume":"4","author":"Ismail","year":"2014","journal-title":"Neurodegener. Dis. Manag."},{"issue":"4","key":"10.1016\/j.media.2024.103135_b34","doi-asserted-by":"crossref","first-page":"685","DOI":"10.1002\/jmri.21049","article-title":"The Alzheimer\u2019s disease neuroimaging initiative (ADNI): MRI methods","volume":"27","author":"Jack Jr.","year":"2008","journal-title":"J. Magn. Reson. Imaging: Off. J. Int. Soc. Magn. Reson. Med."},{"issue":"6","key":"10.1016\/j.media.2024.103135_b35","doi-asserted-by":"crossref","first-page":"355","DOI":"10.1111\/psyg.12176","article-title":"Patterns of hippocampal atrophy differ among Alzheimer\u2019s disease, amnestic mild cognitive impairment, and late-life depression","volume":"16","author":"Joko","year":"2016","journal-title":"Psychogeriatrics"},{"issue":"5","key":"10.1016\/j.media.2024.103135_b36","doi-asserted-by":"crossref","first-page":"330","DOI":"10.1016\/j.biopsych.2016.10.028","article-title":"Detecting neuroimaging biomarkers for depression: a meta-analysis of multivariate pattern recognition studies","volume":"82","author":"Kambeitz","year":"2017","journal-title":"Biol. Psychiat."},{"key":"10.1016\/j.media.2024.103135_b37","series-title":"International Conference on Information Processing in Medical Imaging","first-page":"597","article-title":"Unsupervised domain adaptation in brain lesion segmentation with adversarial networks","author":"Kamnitsas","year":"2017"},{"issue":"8","key":"10.1016\/j.media.2024.103135_b38","doi-asserted-by":"crossref","first-page":"565","DOI":"10.3390\/diagnostics10080565","article-title":"Multimodal brain tumor classification using deep learning and robust feature selection: A machine learning application for radiologists","volume":"10","author":"Khan","year":"2020","journal-title":"Diagnostics"},{"key":"10.1016\/j.media.2024.103135_b39","series-title":"Application of machine learning classification for structural brain MRI in mood disorders: Critical review from a clinical perspective","first-page":"71","author":"Kim","year":"2018"},{"issue":"6","key":"10.1016\/j.media.2024.103135_b40","doi-asserted-by":"crossref","first-page":"2319","DOI":"10.1214\/aos\/1032181157","article-title":"An overtraining-resistant stochastic modeling method for pattern recognition","volume":"24","author":"Kleinberg","year":"1996","journal-title":"Ann. Statist."},{"key":"10.1016\/j.media.2024.103135_b41","first-page":"13","article-title":"MRI-based classification models in prediction of mild cognitive impairment and dementia in late-life depression","author":"Lebedeva","year":"2017","journal-title":"Front. Aging Neurosci."},{"key":"10.1016\/j.media.2024.103135_b42","doi-asserted-by":"crossref","first-page":"190","DOI":"10.1016\/j.mri.2019.07.003","article-title":"Using deep Siamese neural networks for detection of brain asymmetries associated with Alzheimer\u2019s disease and mild cognitive impairment","volume":"64","author":"Liu","year":"2019","journal-title":"Magn. Reson. Imaging"},{"issue":"1","key":"10.1016\/j.media.2024.103135_b43","doi-asserted-by":"crossref","first-page":"147","DOI":"10.1038\/s41398-021-01269-y","article-title":"Late-life depression and increased risk of dementia: a longitudinal cohort study","volume":"11","author":"Ly","year":"2021","journal-title":"Transl. Psychiatry"},{"key":"10.1016\/j.media.2024.103135_b44","series-title":"Medical Imaging 2020: Computer-Aided Diagnosis","first-page":"259","article-title":"MRI image harmonization using cycle-consistent generative adversarial network","volume":"Vol. 11314","author":"Modanwal","year":"2020"},{"key":"10.1016\/j.media.2024.103135_b45","series-title":"2019 18th IEEE International Conference on Machine Learning and Applications","first-page":"1731","article-title":"Depression detection using feature extraction and deep learning from sMRI images","author":"Mousavian","year":"2019"},{"key":"10.1016\/j.media.2024.103135_b46","article-title":"Inference for the generalization error","volume":"12","author":"Nadeau","year":"1999","journal-title":"Advances in Neural Information Processing Systems"},{"issue":"4","key":"10.1016\/j.media.2024.103135_b47","doi-asserted-by":"crossref","first-page":"848","DOI":"10.1080\/13548506.2021.1896760","article-title":"Socio-demographic and educational factors associated with depression, anxiety and stress among health professions students","volume":"27","author":"Nayak","year":"2022","journal-title":"Psychol., Health Med."},{"issue":"2","key":"10.1016\/j.media.2024.103135_b48","doi-asserted-by":"crossref","first-page":"809","DOI":"10.1016\/j.neuroimage.2010.05.023","article-title":"Machine learning classification with confidence: application of transductive conformal predictors to MRI-based diagnostic and prognostic markers in depression","volume":"56","author":"Nouretdinov","year":"2011","journal-title":"Neuroimage"},{"key":"10.1016\/j.media.2024.103135_b49","first-page":"1","article-title":"An independent constructive multi-class classification algorithm for predicting the risk level of stress using multi-modal data","author":"Pankajavalli","year":"2022","journal-title":"Arab. J. Sci. Eng."},{"key":"10.1016\/j.media.2024.103135_b50","doi-asserted-by":"crossref","DOI":"10.1016\/j.cmpb.2021.106236","article-title":"TorchIO: A Python library for efficient loading, preprocessing, augmentation and patch-based sampling of medical images in deep learning","volume":"208","author":"P\u00e9rez-Garc\u00eda","year":"2021","journal-title":"Comput. Methods Programs Biomed."},{"key":"10.1016\/j.media.2024.103135_b51","doi-asserted-by":"crossref","DOI":"10.1111\/jon.13011","article-title":"Functional MRI in major depressive disorder: A review of findings, limitations, and future prospects","author":"Pilmeyer","year":"2022","journal-title":"J. Neuroimaging"},{"issue":"6","key":"10.1016\/j.media.2024.103135_b52","doi-asserted-by":"crossref","first-page":"427","DOI":"10.7326\/0003-4819-148-6-200803180-00005","article-title":"Prevalence of cognitive impairment without dementia in the United States","volume":"148","author":"Plassman","year":"2008","journal-title":"Ann. Int. Med."},{"issue":"9","key":"10.1016\/j.media.2024.103135_b53","doi-asserted-by":"crossref","first-page":"1567","DOI":"10.1038\/s41386-020-0715-y","article-title":"Frontal-executive and corticolimbic structural brain circuitry in older people with remitted depression, mild cognitive impairment, Alzheimer\u2019s dementia, and normal cognition","volume":"45","author":"Rashidi-Ranjbar","year":"2020","journal-title":"Neuropsychopharmacology"},{"key":"10.1016\/j.media.2024.103135_b54","series-title":"International Conference on Medical Image Computing and Computer-Assisted Intervention","first-page":"164","article-title":"Degenerative adversarial neuroimage nets: Generating images that mimic disease progression","author":"Ravi","year":"2019"},{"key":"10.1016\/j.media.2024.103135_b55","doi-asserted-by":"crossref","DOI":"10.1016\/j.neuroimage.2019.116189","article-title":"Automated anatomical labelling atlas 3","volume":"206","author":"Rolls","year":"2020","journal-title":"Neuroimage"},{"key":"10.1016\/j.media.2024.103135_b56","doi-asserted-by":"crossref","first-page":"221","DOI":"10.1016\/j.cogsys.2019.09.007","article-title":"Brain tumor detection using fusion of hand crafted and deep learning features","volume":"59","author":"Saba","year":"2020","journal-title":"Cogn. Syst. Res."},{"key":"10.1016\/j.media.2024.103135_b57","series-title":"2020 4th International Conference on Advanced Systems and Emergent Technologies (IC_ASET)","first-page":"229","article-title":"Hybrid CNN-SVM classifier for efficient depression detection system","author":"Saidi","year":"2020"},{"key":"10.1016\/j.media.2024.103135_b58","doi-asserted-by":"crossref","first-page":"130","DOI":"10.1109\/RBME.2019.2934500","article-title":"Automatic neuroimage processing and analysis in stroke\u2014A systematic review","volume":"13","author":"Sarmento","year":"2019","journal-title":"IEEE Rev. Biomed. Eng."},{"key":"10.1016\/j.media.2024.103135_b59","series-title":"Transformers in medical imaging: A survey","author":"Shamshad","year":"2022"},{"issue":"3","key":"10.1016\/j.media.2024.103135_b60","doi-asserted-by":"crossref","first-page":"1277","DOI":"10.1007\/s40747-020-00216-6","article-title":"A novel hand-crafted with deep learning features based fusion model for COVID-19 diagnosis and classification using chest X-ray images","volume":"7","author":"Shankar","year":"2021","journal-title":"Complex Intell. Syst."},{"key":"10.1016\/j.media.2024.103135_b61","doi-asserted-by":"crossref","first-page":"15","DOI":"10.3389\/fdata.2020.00015","article-title":"Improving diagnosis of depression with XGBOOST machine learning model and a large biomarkers dutch dataset (n=11,081)","author":"Sharma","year":"2020","journal-title":"Front. Big Data"},{"issue":"10","key":"10.1016\/j.media.2024.103135_b62","doi-asserted-by":"crossref","first-page":"1135","DOI":"10.1016\/j.jagp.2017.03.017","article-title":"Negative affectivity, aging, and depression: Results from the neurobiology of late-life depression (NBOLD) study","volume":"25","author":"Steffens","year":"2017","journal-title":"Am. J. Geriatr. Psychiatry"},{"issue":"4","key":"10.1016\/j.media.2024.103135_b63","doi-asserted-by":"crossref","first-page":"202","DOI":"10.1177\/0891988704269819","article-title":"Methodology and preliminary results from the neurocognitive outcomes of depression in the elderly study","volume":"17","author":"Steffens","year":"2004","journal-title":"J. Geriatr. Psychiatry Neurol."},{"key":"10.1016\/j.media.2024.103135_b64","series-title":"International Conference on Medical Image Computing and Computer-Assisted Intervention","first-page":"370","article-title":"Transformer based feature fusion for left ventricle segmentation in 4D flow MRI","author":"Sun","year":"2022"},{"issue":"4","key":"10.1016\/j.media.2024.103135_b65","doi-asserted-by":"crossref","first-page":"603","DOI":"10.1017\/S0033291709990857","article-title":"Relationship between baseline white-matter changes and development of late-life depressive symptoms: 3-year results from the LADIS study","volume":"40","author":"Teodorczuk","year":"2010","journal-title":"Psychol. Med."},{"key":"10.1016\/j.media.2024.103135_b66","doi-asserted-by":"crossref","first-page":"268","DOI":"10.1016\/j.jad.2022.06.056","article-title":"The association between clinical and biological characteristics of depression and structural brain alterations","volume":"312","author":"Toenders","year":"2022","journal-title":"J. Affect. Disord."},{"issue":"1","key":"10.1016\/j.media.2024.103135_b67","doi-asserted-by":"crossref","first-page":"38","DOI":"10.1177\/1550059420916634","article-title":"Major depressive disorder classification based on different convolutional neural network models: Deep learning approach","volume":"52","author":"Uyulan","year":"2021","journal-title":"Clin. EEG Neurosci."},{"issue":"11","key":"10.1016\/j.media.2024.103135_b68","article-title":"Visualizing data using t-SNE","volume":"9","author":"Van der Maaten","year":"2008","journal-title":"J. Mach. Learn. Res."},{"issue":"2","key":"10.1016\/j.media.2024.103135_b69","doi-asserted-by":"crossref","first-page":"77","DOI":"10.1023\/A:1019956318069","article-title":"A perspective view and survey of meta-learning","volume":"18","author":"Vilalta","year":"2002","journal-title":"Artif. Intell. Rev."},{"key":"10.1016\/j.media.2024.103135_b70","series-title":"2005 IEEE Engineering in Medicine and Biology 27th Annual Conference","first-page":"2387","article-title":"One-versus-the-rest (OVR) algorithm: An extension of common spatial patterns (CSP) algorithm to multi-class case","author":"Wu","year":"2006"},{"key":"10.1016\/j.media.2024.103135_b71","series-title":"International Conference on Medical Image Computing and Computer-Assisted Intervention","first-page":"140","article-title":"NestedFormer: Nested modality-aware transformer for brain tumor segmentation","author":"Xing","year":"2022"},{"key":"10.1016\/j.media.2024.103135_b72","doi-asserted-by":"crossref","unstructured":"Yan, H., Ding, Y., Li, P., Wang, Q., Xu, Y., Zuo, W., 2017. Mind the class weight bias: Weighted maximum mean discrepancy for unsupervised domain adaptation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp. 2272\u20132281.","DOI":"10.1109\/CVPR.2017.107"},{"key":"10.1016\/j.media.2024.103135_b73","series-title":"2021 IEEE 18th International Symposium on Biomedical Imaging","first-page":"208","article-title":"Deep factor regression for computer-aided analysis of major depressive disorders with structural MRI data","author":"Yang","year":"2021"},{"key":"10.1016\/j.media.2024.103135_b74","doi-asserted-by":"crossref","first-page":"15","DOI":"10.1016\/j.neucom.2022.04.062","article-title":"NLFFTNet: A non-local feature fusion transformer network for multi-scale object detection","volume":"493","author":"Zeng","year":"2022","journal-title":"Neurocomputing"},{"key":"10.1016\/j.media.2024.103135_b75","series-title":"AMIA Annual Symposium Proceedings","first-page":"1147","article-title":"Multi-view graph convolutional network and its applications on neuroimage analysis for Parkinson\u2019s disease","volume":"2018","author":"Zhang","year":"2018"},{"issue":"11","key":"10.1016\/j.media.2024.103135_b76","doi-asserted-by":"crossref","first-page":"994","DOI":"10.1111\/cns.12835","article-title":"Brain structure alterations in depression: Psychoradiological evidence","volume":"24","author":"Zhang","year":"2018","journal-title":"CNS Neurosci. Therapeut."},{"key":"10.1016\/j.media.2024.103135_b77","series-title":"Machine Learning in Medical Imaging","first-page":"259","article-title":"Understanding clinical progression of late-life depression to alzheimer\u2019s disease over 5 years with structural MRI","author":"Zhang","year":"2022"},{"key":"10.1016\/j.media.2024.103135_b78","doi-asserted-by":"crossref","first-page":"241","DOI":"10.1016\/j.ymeth.2022.04.015","article-title":"A transformer-based multi-features fusion model for prediction of conversion in mild cognitive impairment","volume":"204","author":"Zheng","year":"2022","journal-title":"Methods"},{"issue":"1","key":"10.1016\/j.media.2024.103135_b79","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/s41398-019-0680-6","article-title":"The rise and fall of MRI studies in major depressive disorder","volume":"9","author":"Zhuo","year":"2019","journal-title":"Transl. Psychiatry"}],"container-title":["Medical Image Analysis"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1361841524000604?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1361841524000604?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2025,10,23]],"date-time":"2025-10-23T18:58:52Z","timestamp":1761245932000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S1361841524000604"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,5]]},"references-count":79,"alternative-id":["S1361841524000604"],"URL":"https:\/\/doi.org\/10.1016\/j.media.2024.103135","relation":{},"ISSN":["1361-8415"],"issn-type":[{"value":"1361-8415","type":"print"}],"subject":[],"published":{"date-parts":[[2024,5]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Hybrid representation learning for cognitive diagnosis in late-life depression over 5 years with structural MRI","name":"articletitle","label":"Article Title"},{"value":"Medical Image Analysis","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.media.2024.103135","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2024 Elsevier B.V. All rights reserved.","name":"copyright","label":"Copyright"}],"article-number":"103135"}}