Multi-stage Diagnosis of Alzheimer’s Disease with Incomplete Multimodal Data via Multi-task Deep Learning
Open accessAbstract
Utilization of biomedical data from multiple modalities improves the diagnostic accuracy of neurodegenerative diseases. However, multi-modality data are often incomplete because not all data can be collected for every individual. When using such incomplete data for diagnosis, current approaches for addressing the problem of missing data, such as imputation, matrix completion and multi-task learning, implicitly assume linear data-to-label relationship, therefore limiting their performances. We thus propose multi-task deep learning for incomplete data, where prediction tasks that are associated with different modality combinations are learnt jointly to improve the performance of each task. Specifically, we devise a multi-input multi-output deep learning framework, and train our deep network subnet-wise, partially updating its weights based on the availability of modality data. The experimental results using the ADNI dataset show that our method outperforms the state-of-the-art methods.
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Six of the seven early fusion studies that compared fusion to single modality models showed an improvement in performance when using fusion, and the remaining one achieved the same performance but reduced standard deviation.
“Six of these studies showed an improvement in performance when using fusion25,26,28,29,31,33, and the remaining one achieved the same performance but reduced standard deviation27, alluding to a model with better stability.”
From Fusion of medical imaging and electronic health records using deep learning: a systematic review and implementation guidelines (2020) · cites this paper as [25]
Citation 27 is attached to the claim that fusion achieved the same performance as a single modality but reduced standard deviation. The Results and Discussions section instead says MRI+PET generally had higher classification accuracy than MRI alone; it reports mean accuracy as the evaluation metric and does not report a reduction in standard deviation. No page number is available in our copy. (LLM, conf=0.78)
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Experimental — accuracy not yet measuredPrediction of Mild Cognitive Impairment or Alzheimer's disease based on ADNI dataset is frequently investigated, but predictive accuracy varies, and many studies do not discuss which input modalities or features contributed most to prediction.
“For example, the prediction of Mild Cognitive Impairment or Alzheimer’s disease based on the ADNI dataset is frequently investigated27–32. But their predictive accuracy varies, and many studies do not discuss which input modalities or features contributed most to the prediction.”
From Orchestrating explainable artificial intelligence for multimodal and longitudinal data in medical imaging (2024) · cites this paper as [28]
Split into 2 atoms — 1 confirmed, 0 partially supported, 0 disputed, 0 misattributed, 1 unverifiable. Expand the claim to see per-atom verdicts.
Experimental — accuracy not yet measuredThung et al. conducted image-image fusion of PET and MRI images using a joint fusion approach, but because they concatenated clinical and imaging features into one single feature vector before feeding into their neural network, their approach was categorized as early fusion.
“Thung et al.25 conducted image-image fusion of PET and MRI images using a joint fusion approach, but since they concatenated clinical and imaging features into one single feature vector before feeding into their neural network, we categorized their approach as early fusion.”
From Fusion of medical imaging and electronic health records using deep learning: a systematic review and implementation guidelines (2020) · cites this paper as [25]
Split into 2 atoms — 0 confirmed, 2 partially supported, 0 disputed, 0 misattributed, 0 unverifiable. Expand the claim to see per-atom verdicts.
Experimental — accuracy not yet measuredThe complexity of the non-imaging data used in multimodal fusion studies was limited, with most studies using basic demographic information (age, gender), a limited range of categorical clinical history (e.g., hypertension, smoking status), or disease-specific features strongly associated with the disease of interest (e.g., APOE4 for Alzheimer's, PSA for prostate cancer).
“The complexity of the non-imaging data in multimodal fusion work was limited, particularly in the context of available feature-rich and time-series data in the EHR. Instead, most studies focused primarily on basic demographic information such as age and gender25,27,39, a limited range of categorical clinical history such as hypertension or smoking status32,34 or disease-specific clinical features”
From Fusion of medical imaging and electronic health records using deep learning: a systematic review and implementation guidelines (2020) · cites this paper as [25]
Fusion strategies were often applied to the diagnosis and prediction of Alzheimer's disease.
“For example, fusion strategies were often applied to the diagnosis and prediction of Alzheimer’s disease25,28,33,36,41.”
From Fusion of medical imaging and electronic health records using deep learning: a systematic review and implementation guidelines (2020) · cites this paper as [25]
Thung et al.'s multi-task network can effectively learn from multimodal data with missing modalities.
“Thung et al. [70] proposed a multi-task network that can effectively learn from multimodal data with missing modalities by having unimodal input branches and task-specific output branches.”
From Multimodal deep learning for biomedical data fusion: a review (2022) · cites this paper as [70]
Different approaches have been proposed for missing modalities, such as multi-task learning, generative models and multimodal dropout.
“Different approaches have been proposed, such as multi-task learning [70], generative models [63] and multimodal dropout [78, 101].”
From Multimodal deep learning for biomedical data fusion: a review (2022) · cites this paper as [70]
Thung et al. (2017) proposed a multitask network that effectively learns from data with missing modalities.
“Thung et al. (2017) proposed a multitask network that effectively learns from data with missing modalities.”
From A review of multimodal deep learning methods for genomic-enabled prediction in plant breeding (2024) · cites this paper as Thung et al. (2017)
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Cites
Exact Matrix Completion via Convex Optimization
Candès, E. J., & Recht, B. (2009) · Foundations of Computational Mathematics · doi:10.1007/s10208-009-9045-5 · Cited by 5,102 · see citing works
Missing Value Estimation Methods for DNA Microarrays
Troyanskaya, O., Cantor, M., Sherlock, G., Brown, P., Hastie, T., Tibshirani, R., Botstein, D., & Altman, R. B. (2001) · Bioinformatics · doi:10.1093/bioinformatics/17.6.520 · Cited by 4,230 · see citing works
The Alzheimer's Disease Neuroimaging Initiative: A review of papers published since its inception
Weiner, M. W., Veitch, D. P., Aisen, P. S. S., Beckett, L. A., Cairns, N. J., Green, R. C., Harvey, D. J., Jack, C. R., Jagust, W. J., Liu, E., et al. (2013) · Alzheimer s & Dementia · doi:10.1016/j.jalz.2013.05.1769 · Cited by 1,011 · see citing works
Multimodal Neuroimaging Feature Learning for Multiclass Diagnosis of Alzheimer's Disease
Liu, S., Liu, S., Cai, W., Che, H., Pujol, S., Kikinis, R., Feng, D., Fulham, M. J., & ADNI (2015) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2014.2372011 · Cited by 637 · see citing works
A Robust Deep Model for Improved Classification of AD/MCI Patients
Li, F., Tran, L., Thung, K., Ji, S., Shen, D. G., & Jiang, L. (2015) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2015.2429556 · Cited by 300 · see citing works
Self-citationStandardization of analysis sets for reporting results from ADNI MRI data
Wyman, B. T., Harvey, D. J., Crawford, K. L., Bernstein, M. A., Carmichael, O. T., Cole, P. E., Crane, P. K., DeCarli, C. S., Fox, N. C., Gunter, J. L., et al. (2012) · Alzheimer s & Dementia · doi:10.1016/j.jalz.2012.06.004 · Cited by 238 · see citing works
Yuan, L., Wang, Y., Thompson, P. M., Narayan, V. A., & Ye, J. (2012) · NeuroImage · doi:10.1016/j.neuroimage.2012.03.059 · Cited by 195 · see citing works
Transduction with Matrix Completion: Three Birds with One Stone
Goldberg, A. B., Recht, B., Xu, J., Nowak, R. D., & Zhu, X. (2010) · Cited by 188 · see citing works
Robust Deformable-Surface-Based Skull-Stripping for Large-Scale Studies
Wang, Y., Nie, J., Yap, P., Shi, F., Guo, L., & Shen, D. G. (2011) · Lecture notes in computer science · doi:10.1007/978-3-642-23626-6_78 · Cited by 116 · see citing works
Self-citationThung, K., Chong-yaw Wee, A., Yap, P., & Shen, D. G. (2014) · NeuroImage · doi:10.1016/j.neuroimage.2014.01.033 · Cited by 95 · see citing works
Self-citationThung, K., Chong-yaw Wee, A., Yap, P., & Shen, D. G. (2015) · Brain Structure and Function · doi:10.1007/s00429-015-1140-6 · Cited by 45 · see citing works
Self-citationRobust feature-sample linear discriminant analysis for brain disorders diagnosis
Adeli, E., Thung, K., An, L., Shi, F., & Shen, D. G. (2015) · neural information processing systems · Cited by 23 · see citing works
Self-citationYu, G., Liu, Y., Thung, K., & Shen, D. G. (2014) · PLoS ONE · doi:10.1371/journal.pone.0096458 · Cited by 21 · see citing works
Self-citationThung, K., Yap, P., Adeli, E., & Shen, D. G. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-24574-4_63 · Cited by 17 · see citing works
Self-citationStability-Weighted Matrix Completion of Incomplete Multi-modal Data for Disease Diagnosis
Thung, K., Adeli, E., Yap, P., & Shen, D. G. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-46723-8_11 · Cited by 15 · see citing works
Self-citation
Flagged first, then by citations
Cited by
Sorted by citations
Huang, S.-C., Pareek, A., Seyyedi, S., Banerjee, I., & Lungren, M. P. (2020) · npj Digital Medicine · doi:10.1038/s41746-020-00341-z · Cited by 934 · see citing works
“Six of these studies showed an improvement in performance when using fusion 25,26,28,29,31,33 , and the remaining one achieved the same performance but reduced standard deviation 27 , alluding to a model with better stability.”
BackgroundMethodInfluentialWen, J., Thibeau-Sutre, E., Diaz-Melo, M., Samper-González, J., Routier, A., Bottani, S., Dormont, D., Durrleman, S., Burgos, N., & Colliot, O. (2020) · Medical Image Analysis · doi:10.1016/j.media.2020.101694 · Cited by 809 · see citing works
Multimodal deep learning for biomedical data fusion: a review
Stahlschmidt, S. R., Ulfenborg, B., & Synnergren, J. (2022) · Briefings in Bioinformatics · doi:10.1093/bib/bbab569 · Cited by 766 · see citing works
“Different approaches have been proposed, such as multi-task learning [70], generative models [63] and multimodal dropout [78, 101].”
BackgroundMethodDeep learning to detect Alzheimer's disease from neuroimaging: A systematic literature review
Ghahnavieh, A. E., Luo, S., & Chiong, R. (2019) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2019.105242 · Cited by 439 · see citing works
El-Sappagh, S., Alonso, J. M., Islam, S. M. R., Sultan, A. M., & Kwak, K. S. (2021) · Scientific Reports · doi:10.1038/s41598-021-82098-3 · Cited by 363 · see citing works
A systematic survey of computer-aided diagnosis in medicine: Past and present developments
Yanase, J., & Triantaphyllou, E. (2019) · Expert Systems with Applications · doi:10.1016/j.eswa.2019.112821 · Cited by 303 · see citing works
Zhou, T., Thung, K., Zhu, X., & Shen, D. G. (2018) · Human Brain Mapping · doi:10.1002/hbm.24428 · Cited by 270 · see citing works
Self-citationChen, J., Yi, C., Okegbile, S. D., Cai, J., & Shen, X. (2023) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2023.3308717 · Cited by 268 · see citing works
Huang, S.-C., Pareek, A., Zamanian, R., Banerjee, I., & Lungren, M. P. (2020) · Scientific Reports · doi:10.1038/s41598-020-78888-w · Cited by 246 · see citing works
Review of multimodal machine learning approaches in healthcare
Krones, F., Marikkar, U., Parsons, G., Szmul, A., & Mahdi, A. (2025) · Information Fusion · doi:10.1016/j.inffus.2024.102690 · Cited by 235 · see citing works
Deep learning for Alzheimer's disease diagnosis: A survey
Khojaste-Sarakhsi, M., Haghighi, S. S., Ghomi, S. M. T. F., & Marchiori, E. (2022) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2022.102332 · Cited by 194 · see citing works
CRADLE: Cross-Backend Validation to Detect and Localize Bugs in Deep Learning Libraries
Pham, H. V., Lutellier, T., Qi, W., & Tan, L. (2019) · ACM/IEEE International Conference on Software Engineering: Companion Proceedings (ICSE-Companion) · doi:10.1109/icse.2019.00107 · Cited by 193 · see citing works
Deep learning library testing via effective model generation
Wang, Z., Yan, M., Chen, J., Liu, S., & Zhang, D. (2020) · Proceedings of the 28th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering · doi:10.1145/3368089.3409761 · Cited by 167 · see citing works
Liu, M., Li, S., Yuan, H., Ong, M. E. H., Ning, Y., Xie, F., Saffari, S. E., Shang, Y., Volovici, V., Chakraborty, B., et al. (2023) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2023.102587 · Cited by 166 · see citing works
Loddo, A., Buttau, S., & Ruberto, C. D. (2021) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2021.105032 · Cited by 165 · see citing works
A new deep belief network-based multi-task learning for diagnosis of Alzheimer’s disease
Zeng, N., Li, H., & Peng, Y. (2021) · Neural Computing and Applications · doi:10.1007/s00521-021-06149-6 · Cited by 117 · see citing works
Diagnosis of brain diseases in fusion of neuroimaging modalities using deep learning: A review
Shoeibi, A., Khodatars, M., Jafari, M., Ghassemi, N., Moridian, P., Alizadehsani, R., Ling, S. H., Khosravi, A., Alinejad‐Rokny, H., Lam, H., et al. (2022) · Information Fusion · doi:10.1016/j.inffus.2022.12.010 · Cited by 106 · see citing works
Lin, W.-M., Gao, Q., Du, M., Chen, W., & Tong, T. (2021) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2021.104478 · Cited by 95 · see citing works
Pahud de Mortanges, A., Luo, H., Shu, S. Z., Kamath, A., Suter, Y., Shelan, M., Pöllinger, A., & Reyes, M. (2024) · npj Digital Medicine · doi:10.1038/s41746-024-01190-w · Cited by 93 · see citing works
Predicting MCI Status From Multimodal Language Data Using Cascaded Classifiers
Fraser, K., Fors, K. L., Eckerström, M., Öhman, F., & Kokkinakis, D. (2019) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2019.00205 · Cited by 93 · see citing works
Thung, K.-H., Yap, P.-T., Adeli, E., Lee, S.-W., & Shen, D. (2018) · Medical Image Analysis · doi:10.1016/j.media.2018.01.002 · Cited by 89 · see citing works
Self-citationSharma, S., & Mandal, P. K. (2022) · ACM Computing Surveys · doi:10.1145/3492865 · Cited by 86 · see citing works
Multimodality Fusion Aspects of Medical Diagnosis: A Comprehensive Review
Kumar, S., Rani, S., Sharma, S., & Min, H. (2024) · Bioengineering · doi:10.3390/bioengineering11121233 · Cited by 64 · see citing works
RGB-D-Based Object Recognition Using Multimodal Convolutional Neural Networks: A Survey
Gao, M., Jiang, J., Zou, G., John, V., & Liu, Z. (2019) · IEEE Access · doi:10.1109/access.2019.2907071 · Cited by 63 · see citing works
Role of deep learning in infant brain MRI analysis
Mostapha, M., & Styner, M. A. (2019) · Magnetic Resonance Imaging · doi:10.1016/j.mri.2019.06.009 · Cited by 60 · see citing works
Liu, Y., Yue, L., Xiao, S., Yang, W., Shen, D. G., & Liu, M. (2021) · Medical Image Analysis · doi:10.1016/j.media.2021.102266 · Cited by 59 · see citing works
Self-citationChallenges of implementing computer-aided diagnostic models for neuroimages in a clinical setting
Leming, M., Bron, E. E., Bruffaerts, R., Ou, Y., Iglesias, J. E., Gollub, R. L., & Im, H. (2023) · npj Digital Medicine · doi:10.1038/s41746-023-00868-x · Cited by 54 · see citing works
Zhou, T., Thung, K., Liu, M., & Shen, D. G. (2018) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2018.2824725 · Cited by 52 · see citing works
Self-citationA review of multimodal deep learning methods for genomic-enabled prediction in plant breeding
Montesinos-López, O. A., Chavira-Flores, M., Kiasmiantini, Crespo-Herrera, L., Saint Piere, C., Li, H., Fritsche-Neto, R., Al-Nowibet, K., Montesinos-López, A., & Crossa, J. (2024) · GENETICS · doi:10.1093/genetics/iyae161 · Cited by 47 · see citing works
Liu, F., Yuan, S., Li, W., Xu, Q. J., & Sheng, B. (2022) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2022.104400 · Cited by 45 · see citing works
Artificial intelligence for dementia prevention
Newby, D., Orgeta, V., Marshall, C. R., Lourida, I., Albertyn, C. P., Tamburin, S., Raymont, V., Veldsman, M., Koychev, I., Bauermeister, S., et al. (2023) · Alzheimer's & Dementia · doi:10.1002/alz.13463 · Cited by 42 · see citing works
Multimodal missing data in healthcare: A comprehensive review and future directions
Le, L. P., Nguyen, T., Riegler, M., Halvorsen, P., & Nguyen, B. T. (2025) · Computer Science Review · doi:10.1016/j.cosrev.2024.100720 · Cited by 39 · see citing works
Cheng, C.-Y., Tseng, W., Chang, C.-F., Chang, C.-H., & Gau, S. (2020) · Frontiers in Psychiatry · doi:10.3389/fpsyt.2020.00673 · Cited by 27 · see citing works
Kwak, M. G., Mao, L., Zheng, Z., Su, Y., Lure, F. Y. M., Li, J., & Initiative, A. D. N. (2025) · IEEE Transactions on Automation Science and Engineering · doi:10.1109/tase.2025.3556290 · Cited by 31 · see citing works
Brenes, D., Barberan, C., Hunt, B., Parra, S. G., Salcedo, M. P., Resende, J. C. P., Cremer, M., Castle, P. E., Fregnani, J. H. T. G., Maza, M., et al. (2022) · Computerized Medical Imaging and Graphics · doi:10.1016/j.compmedimag.2022.102052 · Cited by 31 · see citing works
Multimodal Machine Learning Approaches in Predictive Healthcare Analytics: A Comprehensive Survey
Vavekanand, R., Kumar, T., Kumar, S., Kumar, G., & Laghari, A. A. (2026) · Archives of Computational Methods in Engineering · doi:10.1007/s11831-026-10560-4 · Cited by 29 · see citing works
VAE-Driven Multimodal Fusion for Early Cardiac Disease Detection
Jun-xin, W., Li, J., Wang, R., & Zhou, X. (2024) · IEEE Access · doi:10.1109/access.2024.3420444 · Cited by 23 · see citing works
Multi input–Multi output 3D CNN for dementia severity assessment with incomplete multimodal data
Gravina, M., Garcia-Pedrero, A. M., Gonzalo‐Martín, C., Sansone, C., & Soda, P. (2024) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2024.102774 · Cited by 21 · see citing works
Robust multimodal fusion network using adversarial learning for brain tumor grading
Jeong, S.-W., Cho, H.-H., Lee, S.-H., & Park, H. (2022) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2022.107165 · Cited by 19 · see citing works
Ke, F., & Yang, R. (2020) · IEEE Access · doi:10.1109/access.2020.3038479 · Cited by 17 · see citing works
Zhou, J., Hu, L., Jiang, Y. L., & Liu, L. (2021) · BioMed Research International · doi:10.1155/2021/8890513 · Cited by 17 · see citing works
RetractedJabason, E., Ahmad, M. O., & Swamy, M. N. S. (2018) · IEEE Biomedical Circuits and Systems Conference (BioCAS) · doi:10.1109/biocas.2018.8584844 · Cited by 16 · see citing works
Transformer-based personalized attention mechanism for medical images with clinical records
Takagi, Y., Hashimoto, N., Masuda, H., Miyoshi, H., Ohshima, K., Hontani, H., & Takeuchi, I. (2023) · Journal of Pathology Informatics · doi:10.1016/j.jpi.2022.100185 · Cited by 14 · see citing works
Lin, I., Chang, S.-C., Huang, Y., Kuo, T. B. J., & Chiu, H. (2023) · Frontiers in Psychology · doi:10.3389/fpsyg.2022.1067771 · Cited by 13 · see citing works
Li, X., Qiu, Y., Zhou, J. H., & Xie, Z. (2021) · Current Genomics · doi:10.2174/1389202923666211216163049 · Cited by 11 · see citing works
Single-sample image-fusion upsampling of fluorescence lifetime images
Kapitány, V., Fatima, A., Žičkus, V., Whitelaw, J., McGhee, E. J., Insall, R. H., Machesky, L. M., & Faccio, D. (2024) · Science Advances · doi:10.1126/sciadv.adn0139 · Cited by 10 · see citing works
Wei, S., Tian, X., Zuo, E., Zhang, X., Lü, C., Gu, J., Chen, C., Lv, X., & Chen, C. (2024) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2024.103053 · Cited by 8 · see citing works
Multi-task Deep Learning in Medical Image Processing: A Systematic Review
Kamiri, J., Wambugu, G. M., & Oirere, A. M. (2025) · International Journal of Computing Sciences Research · doi:10.25147/ijcsr.2017.001.1.226 · Cited by 8 · see citing works
Jiang, L., & Wu, C. (2019) · International Journal of Wireless Information Networks · doi:10.1007/s10776-019-00447-6 · Cited by 5 · see citing works
Multimodal biomarker AI techniques for early neurocognitive disorder diagnosis: A systematic review
Catino, F., Castellana, F., Zupo, R., Giannoccaro, V., Lampignano, L., Petrosillo, A. M., Addabbo, F., Sborgia, G., Colacicco, G., Santoro, C., et al. (2026) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2026.103389 · Cited by 3 · see citing works
Lin, Y. J., & Komperda, I. (2026) · SN Comprehensive Clinical Medicine · doi:10.1007/s42399-026-02330-x · Cited by 3 · see citing works
Henderson, N., Min, W., Rowe, J., & Lester, J. C. (2020) · INTERNATIONAL CONFERENCE ON MULTIMODAL INTERACTION · doi:10.1145/3382507.3418892 · Cited by 3 · see citing works
Merging Well-Trained Deep CNN Models for Efficient Inference
Wu, C.-E. R., Lee, J., Wan, T. S. T., Chan, Y.-M., & Chen, C. (2020) · Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC) · Cited by 3 · see citing works
Hou, L. (2021) · Information Systems and e-Business Management · doi:10.1007/s10257-021-00507-6 · Cited by 3 · see citing works
RetractedHarshitha, Chamarajan, G., & Charishma, Y. (2021) · International Journal of Artificial Intelligence · doi:10.36079/lamintang.ijai-0801.220 · Cited by 3 · see citing works
Mishra, S. D., & Dutta, M. (2023) · Soft Computing · doi:10.1007/s00500-023-08108-w · Cited by 2 · see citing works
RetractedFTNet-HiLa: An adaptive multimodal histopathological image categorization network
Yin, S., Zhang, D., Zhang, Y., Zhao, X. Y., & Zhao, X. (2024) · Ain Shams Engineering Journal · doi:10.1016/j.asej.2024.103211 · Cited by 1 · see citing works
de Gevigney, V. D., Nicastro, N., Garibotto, V., & Schmid, J. (2026) · Journal of Imaging Informatics in Medicine · doi:10.1007/s10278-025-01831-w
Multi-Branch and Multi-Task Learning Framework for Autism Diagnosis with Modality Incomplete Data
Wu, C., Liang, C., Adalı, T., Jiang, R., Zhang, D., Calhoun, V. D., & Qi, S. (2026) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi61048.2026.11515423
Multi-task Deep Learning for the Multi-label Prediction of Neurodegenerative Diseases
Borsani, T., Nicosia, G., & Fatta, G. D. (2026) · IEEE Conference on Artificial Intelligence (CAI) · doi:10.1109/cai68641.2026.11536260
Rodriguez-Idiazabal, L., Quintana, J. M., Ser, J. D., Echevarria‐Uraga, J. J., Legarreta, M. J., Larrea, N., García-García, F., & Barrio, I. (2026) · Expert Systems · doi:10.1111/exsy.70440
A Review of MRI-PET Fusion for Alzheimer’s Disease Diagnosis
秦, 傲. (2026) · Journal of Image and Signal Processing · doi:10.12677/jisp.2026.154042
- Authors
- Thung, K.-H., Yap, P.-T., & Shen, D.
- Affiliations
- University of North Carolina at Chapel Hill (US)
- Corresponding author
- Shen, Dinggang
- Venue
- Lecture Notes in Computer Science
- Publisher
- Springer International Publishing
- Published
- 2017
- Volume
- 10553
- Pages
- 160-168
- ISSN
- 0302-9743 (ISSN-L)
- 1611-3349
- PMC
- PMC5666687
- OpenAlex
- W2755657284
- Semantic Scholar
- a453c0f1992913dd3d3f87f076d2838429b3bb28
- Cited by
- 63
- References
- 15
- Citations by year
- 2026: 8 · 2025: 2 · 2024: 10 · 2023: 7 · 2022: 6 · 2021: 11 · 2020: 8 · 2019: 7 · 2018: 4
- FWCI
- 15.06
- Type
- conference paper
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Lecture notes in computer science (publishedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- Funders
- National Institute on Aging (R01AG042599, R01AG041721)
- National Institute of Mental Health (R01MH100217)
- National Institute of Neurological Disorders and Stroke (R01NS093842)
- National Institute of Biomedical Imaging and Bioengineering (R01EB022880, R01EB008374, R01EB009634, R01EB006733)
- Topics
- Health Sciences › Medicine › Psychiatry and Mental health › Dementia and Cognitive Impairment Research
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Multimodal Machine Learning Applications
- Keywords
- Alzheimer's disease diagnosis, multimodal data, missing data, multi-task deep learning, multi-input multi-output neural network, neurodegenerative diseases, ADNI dataset
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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