{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,2]],"date-time":"2026-07-02T20:27:57Z","timestamp":1783024077897,"version":"3.54.6"},"reference-count":48,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"funder":[{"DOI":"10.13039\/501100005230","name":"Natural Science Foundation of Chongqing Municipality","doi-asserted-by":"publisher","award":["CSTB2023NSCQ-MSX0239"],"award-info":[{"award-number":["CSTB2023NSCQ-MSX0239"]}],"id":[{"id":"10.13039\/501100005230","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100007957","name":"Chongqing Municipal Education Commission","doi-asserted-by":"publisher","award":["KJQN202101116"],"award-info":[{"award-number":["KJQN202101116"]}],"id":[{"id":"10.13039\/501100007957","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004867","name":"Chongqing University of Technology","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100004867","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Biomedical Signal Processing and Control"],"published-print":{"date-parts":[[2026,10]]},"DOI":"10.1016\/j.bspc.2026.110777","type":"journal-article","created":{"date-parts":[[2026,6,17]],"date-time":"2026-06-17T19:22:21Z","timestamp":1781724141000},"page":"110777","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"C","title":["Enhancing Multi-Task learning for neurodegenerative disease Diagnosis: Utilizing prototype and orthogonal constraints"],"prefix":"10.1016","volume":"125","author":[{"given":"Ying","family":"Long","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qiongmin","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Siyi","family":"Yu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yin","family":"Shi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaowei","family":"Tan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"issue":"9","key":"10.1016\/j.bspc.2026.110777_b0005","doi-asserted-by":"crossref","first-page":"9439","DOI":"10.1109\/TCYB.2021.3056104","article-title":"Multimodal gait recognition for neurodegenerative diseases[J]","volume":"52","author":"Zhao","year":"2021","journal-title":"IEEE Trans. Cybern."},{"key":"10.1016\/j.bspc.2026.110777_b0010","unstructured":"World Health Organization. Dementia[EB\/OL]. (2023-3-15)[2024-1-31]. World Health Organization. Dementia[EB\/OL]. (2023-3-15)[2024-1-31]. https:\/\/www.who.int\/news-room\/fact-sheets\/detail\/dementia."},{"key":"10.1016\/j.bspc.2026.110777_b0015","unstructured":"American Parkinson Disease Association. Parkinson\u2019s Disease HANDBOOK[EB\/OL]. (2023-5-5)[2024-1-31]. https:\/\/www.apdaparkinson.org\/wp-content\/uploads\/2020\/11\/APDA21496-Basic-Handbook-D4V1-1-2.pdf."},{"key":"10.1016\/j.bspc.2026.110777_b0020","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s10916-018-0932-7","article-title":"Classification of Alzheimer\u2019s disease based on eight-layer convolutional neural network with leaky rectified linear unit and max pooling[J]","volume":"42","author":"Wang","year":"2018","journal-title":"J. Med. Syst."},{"key":"10.1016\/j.bspc.2026.110777_b0025","doi-asserted-by":"crossref","unstructured":"Suk HI, Shen D. Deep ensemble sparse regression network for Alzheimer\u2019s disease diagnosis[C]\/\/Machine Learning in Medical Imaging: 7th International Workshop, MLMI 2016, Held in Conjunction with MICCAI 2016, Athens, Greece, October 17, 2016, Proceedings 7. Springer International Publishing, 2016: 113-121.","DOI":"10.1007\/978-3-319-47157-0_14"},{"key":"10.1016\/j.bspc.2026.110777_b0030","unstructured":"Alissa M. Parkinson\u2019s Disease Diagnosis Using Deep Learning[J]. arXiv preprint arXiv:2101.05631, 2021."},{"key":"10.1016\/j.bspc.2026.110777_b0035","article-title":"Explainable classification of Parkinson\u2019s disease using deep learning trained on a large multi-center database of T1-weighted MRI datasets","volume":"38","author":"Camacho","year":"2023","journal-title":"NeuroImage: Clinical."},{"key":"10.1016\/j.bspc.2026.110777_b0040","doi-asserted-by":"crossref","first-page":"167","DOI":"10.1016\/j.neurobiolaging.2016.05.024","article-title":"Clinical validity of medial temporal atrophy as a biomarker for Alzheimer\u2019s disease in the context of a structured 5-phase development framework[J]","volume":"52","author":"Ten Kate","year":"2017","journal-title":"Neurobiol. Aging"},{"key":"10.1016\/j.bspc.2026.110777_b0045","doi-asserted-by":"crossref","first-page":"16","DOI":"10.1016\/j.parkreldis.2020.11.010","article-title":"Hierarchical cluster analysis of multimodal imaging data identifies brain atrophy and cognitive patterns in Parkinson\u2019s disease[J]","volume":"82","author":"Inguanzo","year":"2021","journal-title":"Parkinsonism Relat. Disord."},{"key":"10.1016\/j.bspc.2026.110777_b0050","first-page":"3994","author":"Misra","year":"2016","journal-title":"Cross-Stitch Networks for Multi-Task Learning[c].\/\/proceedings of the IEEE Conference on Computer Vision and Pattern Recognition."},{"key":"10.1016\/j.bspc.2026.110777_b0055","doi-asserted-by":"crossref","first-page":"41","DOI":"10.1023\/A:1007379606734","article-title":"Multitask learning[J]","volume":"28","author":"Caruana","year":"1997","journal-title":"Mach. Learn."},{"issue":"1","key":"10.1016\/j.bspc.2026.110777_b0060","first-page":"9977","volume":"33","author":"Liu","year":"2019","journal-title":"Loss-Balanced Task Weighting to Reduce Negative Transfer in Multi-Task Learning[c].\/\/proceedings of the AAAI Conference on Artificial Intelligence."},{"key":"10.1016\/j.bspc.2026.110777_b0065","doi-asserted-by":"crossref","unstructured":"Tang H, Liu J, Zhao M, et al. Progressive layered extraction (ple): A novel multi-task learning (mtl) model for personalized recommendations[C]\/\/Proceedings of the 14th ACM Conference on Recommender Systems. 2020: 269-278.","DOI":"10.1145\/3383313.3412236"},{"key":"10.1016\/j.bspc.2026.110777_b0070","first-page":"1440","author":"Girshick","year":"2015","journal-title":"Fast r-Cnn[c].\/\/proceedings of the IEEE International Conference on Computer Vision."},{"key":"10.1016\/j.bspc.2026.110777_b0075","first-page":"1","article-title":"ChroNet: a multi-task learning based approach for prediction of multiple chronic diseases[J]","author":"Feng","year":"2021","journal-title":"Multimed. Tools Appl."},{"key":"10.1016\/j.bspc.2026.110777_b0080","unstructured":"Luong MT, Le QV, Sutskever I, et al. Multi-task sequence to sequence learning[J]. arxiv preprint arxiv:1511.06114, 2015."},{"key":"10.1016\/j.bspc.2026.110777_b0085","doi-asserted-by":"crossref","unstructured":"Kongyoung S, Macdonald C, Ounis I. Multi-task learning using dynamic task weighting for conversational question answering[C]\/\/Proceedings of the 5th International Workshop on Search-Oriented Conversational AI (SCAI). 2020: 17-26.","DOI":"10.18653\/v1\/2020.scai-1.3"},{"key":"10.1016\/j.bspc.2026.110777_b0090","first-page":"596","article-title":"Perceive your users in depth: Learning universal user representations from multiple e-commerce tasks[C].\/\/Proceedings of the 24th ACM SIGKDD","author":"Ni","year":"2018","journal-title":"International Conference on Knowledge Discovery & Data Mining."},{"key":"10.1016\/j.bspc.2026.110777_b0095","doi-asserted-by":"crossref","unstructured":"Xin SH, Li Z, Zou P, et al. ATNN: adversarial two-tower neural network for new item\u2019s popularity prediction in E-commerce[C].\/\/2021 IEEE 37th International Conference on Data Engineering (ICDE). 2021: 2499-2510.","DOI":"10.1109\/ICDE51399.2021.00282"},{"key":"10.1016\/j.bspc.2026.110777_b0100","series-title":"\/\/proceedings of the 2025 International Joint Conference on Neural Networks (IJCNN","first-page":"1","article-title":"Improve Multi-task Medical image Segmentation by Localized Worst-case Balancing[C]","author":"Pan","year":"2025"},{"issue":"3","key":"10.1016\/j.bspc.2026.110777_b0105","doi-asserted-by":"crossref","DOI":"10.1016\/j.irbm.2024.100840","article-title":"A Multi-Task Residual Network for Alzheimer\u2019s Disease Classification and Brain Age Prediction[J]","volume":"45","author":"Qian","year":"2024","journal-title":"IRBM"},{"key":"10.1016\/j.bspc.2026.110777_b0110","doi-asserted-by":"crossref","unstructured":"K. Gayathri, C. V. K and S. Lokesh. A Hybrid CNN\u2013U-Net Deep Learning Framework for Multi-Task Lung Disease Classification and Segmentation[C].\/\/Proceedings of the 2025 International Conference on Next Generation Computing Systems (ICNGCS). 2025:1-6.","DOI":"10.1109\/ICNGCS64900.2025.11183261"},{"key":"10.1016\/j.bspc.2026.110777_b0115","first-page":"7482","author":"Kendall","year":"2018","journal-title":"Multi-Task Learning Using Uncertainty to Weigh Losses for Scene Geometry and Semantics[c].\/\/proceedings of the IEEE Conference on Computer Vision and Pattern Recognition."},{"key":"10.1016\/j.bspc.2026.110777_b0120","first-page":"794","author":"Chen","year":"2018","journal-title":"Gradnorm: Gradient Normalization for Adaptive Loss Balancing in Deep Multitask networks[C].\/\/InInternational Conference on Machine Learning."},{"key":"10.1016\/j.bspc.2026.110777_b0125","first-page":"5334","author":"Lu","year":"2017","journal-title":"Fully-Adaptive Feature Sharing in Multi-Task Networks with Applications in Person Attribute Classification[c].\/\/proceedings of the IEEE Conference on Computer Vision and Pattern Recognition."},{"key":"10.1016\/j.bspc.2026.110777_b0130","first-page":"3994","author":"Misra","year":"2016","journal-title":"Cross-Stitch Networks for Multi-Task Learning[c]\/\/proceedings of the IEEE Conference on Computer Vision and Pattern Recognition."},{"key":"10.1016\/j.bspc.2026.110777_b0135","volume":"2","author":"Ruder","year":"2017","journal-title":"Sluice Networks: Learning What to Share between Loosely Related Tasks[j]."},{"key":"10.1016\/j.bspc.2026.110777_b0140","series-title":"24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining","article-title":"Modeling task relationships in multi-task learning with multi-gate mixture-of-experts[C]\/\/Proceedings of the","author":"Ma","year":"2018"},{"issue":"1","key":"10.1016\/j.bspc.2026.110777_b0145","doi-asserted-by":"crossref","first-page":"79","DOI":"10.1162\/neco.1991.3.1.79","article-title":"Adaptive mixtures of local experts[J]","volume":"3","author":"Jacobs","year":"1991","journal-title":"Neural Comput."},{"key":"10.1016\/j.bspc.2026.110777_b0150","doi-asserted-by":"crossref","unstructured":"Tang H, Liu J, Zhao M, et al. Progressive layered extraction (ple): A novel multi-task learning (mtl) model for personalized recommendations[C]\/\/Proceedings of the 14th ACM Conference on Recommender Systems. 2020: 269-278.","DOI":"10.1145\/3383313.3412236"},{"key":"10.1016\/j.bspc.2026.110777_b0155","unstructured":"Aur\u00e9lien Bellet, Amaury Habrard, and Marc Sebban. A survey on metric learning for feature vectors and structured data. arXiv preprint arXiv:1306.6709, 2013."},{"key":"10.1016\/j.bspc.2026.110777_b0160","doi-asserted-by":"crossref","DOI":"10.1016\/j.measurement.2021.109954","article-title":"Automatic epilepsy detection based on generalized convolutional prototype learning[J]","volume":"184","author":"Lyu","year":"2021","journal-title":"Measurement"},{"key":"10.1016\/j.bspc.2026.110777_b0165","doi-asserted-by":"crossref","DOI":"10.1016\/j.bspc.2021.102713","article-title":"Prototype transfer generative adversarial network for unsupervised breast cancer histology image classification[J]","volume":"68","author":"Wang","year":"2021","journal-title":"Biomed. Signal Process. Control"},{"key":"10.1016\/j.bspc.2026.110777_b0170","doi-asserted-by":"crossref","first-page":"16","DOI":"10.1016\/j.patcog.2018.04.020","article-title":"A novel and simple strategy for evolving prototype based clustering[J]","volume":"82","author":"M\u00e1rquez","year":"2018","journal-title":"Pattern Recogn."},{"key":"10.1016\/j.bspc.2026.110777_b0175","first-page":"2472","article-title":"Prototype Feature Extraction for Multi-task Learning[C]\/\/Proceedings of the ACM","author":"Xin","year":"2022","journal-title":"Web Conference"},{"key":"10.1016\/j.bspc.2026.110777_b0180","doi-asserted-by":"crossref","unstructured":"Zhao B, Wen X, Han K. Learning Semi-supervised Gaussian Mixture Models for Generalized Category Discovery[J]. arxiv preprint arxiv:2305.06144, 2023.","DOI":"10.1109\/ICCV51070.2023.01524"},{"issue":"1","key":"10.1016\/j.bspc.2026.110777_b0185","doi-asserted-by":"crossref","first-page":"244","DOI":"10.1109\/TMI.2024.3435450","article-title":"Prototype Learning Guided Hybrid Network for Breast Tumor Segmentation in DCE-MRI[J]","volume":"40","author":"Zhou","year":"2025","journal-title":"IEEE Trans. Med. Imaging"},{"issue":"6","key":"10.1016\/j.bspc.2026.110777_b0190","doi-asserted-by":"crossref","first-page":"2568","DOI":"10.1109\/TMI.2025.3541830","article-title":"Cross- and Intra-image Prototypical Learning for Multi-Label Disease Diagnosis and Interpretation[J]","volume":"44","author":"Wang","year":"2025","journal-title":"IEEE Trans. Med. Imaging"},{"issue":"2","key":"10.1016\/j.bspc.2026.110777_b0195","doi-asserted-by":"crossref","first-page":"535","DOI":"10.1007\/s10107-014-0816-7","article-title":"A framework of constraint preserving update schemes for optimization on stiefel manifold[J]","volume":"153","author":"Jiang","year":"2015","journal-title":"Math. Program."},{"key":"10.1016\/j.bspc.2026.110777_b0200","doi-asserted-by":"crossref","first-page":"662","DOI":"10.1016\/j.ins.2021.11.068","article-title":"Orthogonally constrained matrix factorization for robust unsupervised feature selection with local preserving[J]","volume":"586","author":"Luo","year":"2022","journal-title":"Inf. Sci."},{"key":"10.1016\/j.bspc.2026.110777_b0205","doi-asserted-by":"crossref","unstructured":"Han D, Kim J. Unsupervised simultaneous orthogonal basis clustering feature selection[C].\/\/Proceedings of the IEEE conference on computer vision and pattern recognition 2015: 5016-5023.","DOI":"10.1109\/CVPR.2015.7299136"},{"key":"10.1016\/j.bspc.2026.110777_b0210","doi-asserted-by":"crossref","DOI":"10.1155\/2020\/5343214","article-title":"SVD-CNN: a convolutional neural network model with orthogonal constraints based on SVD for context-aware citation recommendation[J]","author":"Tao","year":"2020","journal-title":"Comput. Intell. Neurosci."},{"key":"10.1016\/j.bspc.2026.110777_b0215","doi-asserted-by":"crossref","unstructured":"Liu P, Qiu X, Huang X. Adversarial multi-task learning for text classification[J]. arXiv preprint arXiv:1704.05742, 2017.","DOI":"10.18653\/v1\/P17-1001"},{"key":"10.1016\/j.bspc.2026.110777_b0220","doi-asserted-by":"crossref","DOI":"10.1016\/j.measurement.2022.111310","article-title":"Image feature selection based on orthogonal \u21132, 0 norms[J]","volume":"199","author":"Huang","year":"2022","journal-title":"Measurement"},{"key":"10.1016\/j.bspc.2026.110777_b0225","doi-asserted-by":"crossref","first-page":"400","DOI":"10.1016\/j.ins.2022.10.068","article-title":"Orthogonal autoencoder regression for image classification[J]","volume":"618","author":"Yang","year":"2022","journal-title":"Inf. Sci."},{"key":"10.1016\/j.bspc.2026.110777_b0230","doi-asserted-by":"crossref","first-page":"1361","DOI":"10.1016\/j.media.2022.102698","article-title":"Orthogonal latent space learning with feature weighting and graph learning for multimodal Alzheimer\u2019s disease diagnosis[J]","volume":"84","author":"Chen","year":"2023","journal-title":"Med. Image Anal."},{"key":"10.1016\/j.bspc.2026.110777_b0235","series-title":"31st ACM International Conference on Information & Knowledge Management","first-page":"386","article-title":"GDOD: Effective Gradient Descent using Orthogonal Decomposition for Multi-Task Learning[C].\/\/Proceedings of the","author":"Dong","year":"2022"},{"issue":"2","key":"10.1016\/j.bspc.2026.110777_b0240","doi-asserted-by":"crossref","first-page":"783","DOI":"10.1109\/TPAMI.2020.3011866","article-title":"Learning backtrackless aligned-spatial graph convolutional networks for graph classification[J]","volume":"44","author":"Bai","year":"2020","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."}],"container-title":["Biomedical Signal Processing and Control"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1746809426013315?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1746809426013315?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,7,2]],"date-time":"2026-07-02T20:10:59Z","timestamp":1783023059000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S1746809426013315"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,10]]},"references-count":48,"alternative-id":["S1746809426013315"],"URL":"https:\/\/doi.org\/10.1016\/j.bspc.2026.110777","relation":{},"ISSN":["1746-8094"],"issn-type":[{"value":"1746-8094","type":"print"}],"subject":[],"published":{"date-parts":[[2026,10]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Enhancing Multi-Task learning for neurodegenerative disease Diagnosis: Utilizing prototype and orthogonal constraints","name":"articletitle","label":"Article Title"},{"value":"Biomedical Signal Processing and Control","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.bspc.2026.110777","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 Elsevier Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies.","name":"copyright","label":"Copyright"}],"article-number":"110777"}}