alzhub.org
Get help now — free helplines

You can call any of these for free. You do not need a diagnosis, an appointment, or a crisis to call — questions and plain exhaustion are reasons enough.

  • Alzheimer's Association 24/7 Helpline 800-272-3900 24 hours a day, 365 days a year Free, confidential support and information for people with dementia, caregivers, families and the public. Master's-level clinicians. Also Spanish.
  • 988 Suicide & Crisis Lifeline 988 24/7 Call or text 988, or chat at 988lifeline.org. For anyone in emotional distress or a mental-health crisis, including caregivers.
  • Alzheimer's Foundation of America (AFA) Helpline 866-232-8484 9:00 am - 9:00 pm ET, seven days a week Licensed social workers providing support, info and referrals. Also text message and web chat. Text: 646-586-5283

In a medical emergency, call 911. More numbers, and what each line does, on the help page.

Home › Research › Topics › convolutional

convolutional

205 studies tagged convolutional, dated 2006-12-06 to 2026-10-30. Bibliography: BibTeX (.bib) · RIS (.ris)

Newest in this topic

  1. Tau-CTFP: A connectome-informed continuous-time first-passage model for forecasting the onset of regional tau PET abnormality 2026-10-30
  2. A Wasserstein Weisfeiler-Lehman Kernel Framework for Multimodal Brain Connectome Analysis 2026-10-29
  3. Current gaps in downstream applications of fMRI foundation models: lessons learned from BrainLM & BrainHarmonix on Prevent-AD 2026-10-26

Studies

205 studies carry this tag.

Date range

2006 to 2026

Top journals

  • Research Square 22
  • medRxiv 22
  • NeuroImage 14
  • bioRxiv 14
  • Preprints.org 7

87 journals in all.

Newest first, then by address. Each study has a usefulness score out of 100 (rubric version 4.0); tap a score to see what makes it up. Tags were assigned automatically from each study's title and summary. The same studies as a filter in the library.

  1. Interpretable classification of Alzheimer's disease pathologies with a convolutional neural network pipeline

    ✓ Peer-reviewed alzhub usefulness score 45 out of 100. Nature communications 2019-05-15 Digital health tools

    Neuropathologists assess vast brain areas to identify diverse and subtly-differentiated morphologies.

  2. Global Evolution of Research in Artificial Intelligence in Health and Medicine: A Bibliometric Study

    ✓ Peer-reviewed alzhub usefulness score 57 out of 100. Journal of clinical medicine 2019-03-14 Risk factors

    The increasing application of Artificial Intelligence (AI) in health and medicine has attracted a great deal of research interest in recent decades.

  3. Machine learning in mental health: a scoping review of methods and applications

    ✓ Peer-reviewed alzhub usefulness score 38 out of 100. Psychological medicine 2019-02-12 Digital health tools

    Overall, the application of ML to mental health has demonstrated a range of benefits across the areas of diagnosis, treatment and support, research, and clinical administration.

  4. A Review on a Deep Learning Perspective in Brain Cancer Classification

    ✓ Peer-reviewed alzhub usefulness score 41 out of 100. Cancers 2019-01-18 Digital health tools

    A World Health Organization (WHO) Feb 2018 report has recently shown that mortality rate due to brain or central nervous system (CNS) cancer is the highest in the Asian continent.

  5. Early-Stage Identification and Pathological Development of Alzheimer's Disease Using Multimodal MRI

    ✓ Peer-reviewed alzhub usefulness score 43 out of 100. Journal of Alzheimer's disease : JAD 2019 Brain scans and imaging

    Alzheimer's disease (AD) is one of the most common progressive and irreversible neurodegenerative diseases.

  6. Hierarchical Fully Convolutional Network for Joint Atrophy Localization and Alzheimer's Disease Diagnosis Using Structural MRI

    ✓ Peer-reviewed alzhub usefulness score 41 out of 100. IEEE transactions on pattern analysis and machine intelligence 2018-12-21 Digital health tools

    Structural magnetic resonance imaging (sMRI) has been widely used for computer-aided diagnosis of neurodegenerative disorders, e.g., Alzheimer's disease (AD), due to its sensitivity to morphological changes caused by brain atrophy.

  7. Automated classification of Alzheimer's disease and mild cognitive impairment using a single MRI and deep neural networks

    ✓ Peer-reviewed alzhub usefulness score 48 out of 100. NeuroImage. Clinical 2018-12-18 Digital health tools

    We built and validated a deep learning algorithm predicting the individual diagnosis of Alzheimer's disease (AD) and mild cognitive impairment who will convert to AD (c-MCI) based on a single cross-sectional brain structural MRI scan.

  8. Disease progression timeline estimation for Alzheimer's disease using discriminative event based modeling

    ✓ Peer-reviewed alzhub usefulness score 45 out of 100. NeuroImage 2018-11-22 Digital health tools

    Alzheimer's Disease (AD) is characterized by a cascade of biomarkers becoming abnormal, the pathophysiology of which is very complex and largely unknown.

  9. Retinal image analytics detects white matter hyperintensities in healthy adults

    ✓ Peer-reviewed alzhub usefulness score 57 out of 100. Annals of clinical and translational neurology 2018-11-15 Treatments

    We developed a robust algorithm to automatically evaluate retinal fundus image that can identify subjects with high WMH burden.

  10. A Deep Learning Model to Predict a Diagnosis of Alzheimer Disease by Using 18 F-FDG PET of the Brain

    ✓ Peer-reviewed alzhub usefulness score 48 out of 100. Radiology 2018-11-06 Digital health tools

    By using fluorine 18 fluorodeoxyglucose PET of the brain, a deep learning algorithm developed for early prediction of Alzheimer disease achieved 82% specificity at 100% sensitivity, an average of 75.8 months prior to the final diagnosis. © RSNA, 2018 Online supplemental material is available for this article.

  11. Convolutional Neural Networks-Based MRI Image Analysis for the Alzheimer's Disease Prediction From Mild Cognitive Impairment

    ✓ Peer-reviewed alzhub usefulness score 45 out of 100. Frontiers in neuroscience 2018-11-05 Digital health tools

    Mild cognitive impairment (MCI) is the prodromal stage of Alzheimer's disease (AD).

  12. Multi-Modality Cascaded Convolutional Neural Networks for Alzheimer's Disease Diagnosis

    ✓ Peer-reviewed alzhub usefulness score 50 out of 100. Neuroinformatics 2018-10-01 Digital health tools

    Accurate and early diagnosis of Alzheimer's disease (AD) plays important role for patient care and development of future treatment.

  13. Joint Classification and Regression via Deep Multi-Task Multi-Channel Learning for Alzheimer's Disease Diagnosis

    ✓ Peer-reviewed alzhub usefulness score 48 out of 100. IEEE transactions on bio-medical engineering 2018-09-12 Digital health tools

    In the field of computer-aided Alzheimer's disease (AD) diagnosis, jointly identifying brain diseases and predicting clinical scores using magnetic resonance imaging (MRI) have attracted increasing attention since these two tasks are highly correlated.

  14. Structural neuroimaging as clinical predictor: A review of machine learning applications

    ✓ Peer-reviewed alzhub usefulness score 41 out of 100. NeuroImage. Clinical 2018-08-10 Digital health tools

    In this paper, we provide an extensive overview of machine learning techniques applied to structural magnetic resonance imaging (MRI) data to obtain clinical classifiers.

  15. Classification of Alzheimer's Disease by Combination of Convolutional and Recurrent Neural Networks Using FDG-PET Images

    ✓ Peer-reviewed alzhub usefulness score 53 out of 100. Frontiers in neuroinformatics 2018-06-19 Digital health tools

    Alzheimer's disease (AD) is an irreversible brain degenerative disorder affecting people aged older than 65 years.

  16. Disease prediction using graph convolutional networks: Application to Autism Spectrum Disorder and Alzheimer's disease

    ✓ Peer-reviewed alzhub usefulness score 41 out of 100. Medical image analysis 2018-06-02 Long-term cohort studies

    Graphs are widely used as a natural framework that captures interactions between individual elements represented as nodes in a graph.

  17. Brain MRI analysis for Alzheimer's disease diagnosis using an ensemble system of deep convolutional neural networks

    ✓ Peer-reviewed alzhub usefulness score 41 out of 100. Brain informatics 2018-05-31 Digital health tools

    Alzheimer's disease is an incurable, progressive neurological brain disorder.

  18. Adaptive template generation for amyloid PET using a deep learning approach

    ✓ Peer-reviewed alzhub usefulness score 39 out of 100. Human brain mapping 2018-05-11 Brain scans and imaging

    Accurate spatial normalization (SN) of amyloid positron emission tomography (PET) images for Alzheimer's disease assessment without coregistered anatomical magnetic resonance imaging (MRI) of the same individual is technically challenging.

  19. Multimodal and Multiscale Deep Neural Networks for the Early Diagnosis of Alzheimer's Disease using structural MR and FDG-PET images

    ✓ Peer-reviewed alzhub usefulness score 45 out of 100. Scientific reports 2018-04-09 Digital health tools

    Alzheimer's Disease (AD) is a progressive neurodegenerative disease where biomarkers for disease based on pathophysiology may be able to provide objective measures for disease diagnosis and staging.

  20. Classification of Alzheimer's Disease Based on Eight-Layer Convolutional Neural Network with Leaky Rectified Linear Unit and Max Pooling

    ✓ Peer-reviewed alzhub usefulness score 41 out of 100. Journal of medical systems 2018-03-26 Digital health tools

    Alzheimer's disease (AD) is a progressive brain disease.

  21. Predicting cognitive decline with deep learning of brain metabolism and amyloid imaging

    ✓ Peer-reviewed alzhub usefulness score 66 out of 100. Behavioural brain research 2018-02-14 Digital health tools

    For effective treatment of Alzheimer's disease (AD), it is important to identify subjects who are most likely to exhibit rapid cognitive decline.

  22. Multi-Domain Transfer Learning for Early Diagnosis of Alzheimer's Disease

    ✓ Peer-reviewed alzhub usefulness score 48 out of 100. Neuroinformatics 2017-04 Prevention trials

    Recently, transfer learning has been successfully applied in early diagnosis of Alzheimer's Disease (AD) based on multi-domain data.

  23. Predicting conversion from MCI to AD using resting-state fMRI, graph theoretical approach and SVM

    ✓ Peer-reviewed alzhub usefulness score 41 out of 100. Journal of neuroscience methods 2017-03-09 Digital health tools

    Using subset of optimal features in SVM, we classified MCI-C from MCI-NC with an accuracy, sensitivity, specificity, and the area under the receiver operating characteristic (ROC) curve of 91.4%, 83.24%, 90.1%, and 0.95, respectively.

  24. Deep ensemble learning of sparse regression models for brain disease diagnosis

    ✓ Peer-reviewed alzhub usefulness score 41 out of 100. Medical image analysis 2017-01-24 Digital health tools

    Recent studies on brain imaging analysis witnessed the core roles of machine learning techniques in computer-assisted intervention for brain disease diagnosis.

  25. Multimodal Neuroimaging Feature Learning With Multimodal Stacked Deep Polynomial Networks for Diagnosis of Alzheimer's Disease

    ✓ Peer-reviewed alzhub usefulness score 38 out of 100. IEEE journal of biomedical and health informatics 2017-01-19 Digital health tools

    The accurate diagnosis of Alzheimer's disease (AD) and its early stage, i.e., mild cognitive impairment, is essential for timely treatment and possible delay of AD.

  26. Classification of CT brain images based on deep learning networks

    ✓ Peer-reviewed alzhub usefulness score 50 out of 100. Computer methods and programs in biomedicine 2016-10-20 Digital health tools

    While computerised tomography (CT) may have been the first imaging tool to study human brain, it has not yet been implemented into clinical decision making process for diagnosis of Alzheimer's disease (AD).

  27. Ensembles of Deep Learning Architectures for the Early Diagnosis of the Alzheimer's Disease

    ✓ Peer-reviewed alzhub usefulness score 36 out of 100. International journal of neural systems 2016-04-04 Digital health tools

    Computer Aided Diagnosis (CAD) constitutes an important tool for the early diagnosis of Alzheimer's Disease (AD), which, in turn, allows the application of treatments that can be simpler and more likely to be effective.

  28. Single subject prediction of brain disorders in neuroimaging: Promises and pitfalls

    ✓ Peer-reviewed alzhub usefulness score 38 out of 100. NeuroImage 2016-03-21 Digital health tools

    Neuroimaging-based single subject prediction of brain disorders has gained increasing attention in recent years.

  29. Linguistic Features Identify Alzheimer's Disease in Narrative Speech

    ✓ Peer-reviewed alzhub usefulness score 45 out of 100. Journal of Alzheimer's disease : JAD 2016-01-01 Digital health tools

    Modern machine learning and linguistic analysis will be increasingly useful in assessment and clustering of suspected AD.

  30. Analysis of entropies based on empirical mode decomposition in amnesic mild cognitive impairment of diabetes mellitus

    ✓ Peer-reviewed alzhub usefulness score 39 out of 100. Journal of Innovative Optical Health Sciences 2015-08-21 Risk factors

    EEG characteristics that correlate with the cognitive functions are important in detecting mild cognitive impairment (MCI) in T2DM.

  31. Gaussian process classification of Alzheimer's disease and mild cognitive impairment from resting-state fMRI

    ✓ Peer-reviewed alzhub usefulness score 43 out of 100. NeuroImage 2015-02-28 Digital health tools

    Multivariate pattern analysis and statistical machine learning techniques are attracting increasing interest from the neuroimaging community.

  32. Multimodal analysis of functional and structural disconnection in Alzheimer's disease using multiple kernel SVM

    ✓ Peer-reviewed alzhub usefulness score 43 out of 100. Human brain mapping 2015-02-09 Digital health tools

    Alzheimer's disease (AD) patients exhibit alterations in the functional connectivity between spatially segregated brain regions which may be related to both local gray matter (GM) atrophy as well as a decline in the fiber integrity of the underlying white matter tracts.

  33. Multimodal neuroimaging feature learning for multiclass diagnosis of Alzheimer's disease

    ✓ Peer-reviewed alzhub usefulness score 36 out of 100. IEEE transactions on bio-medical engineering 2014-11-20 Digital health tools

    The accurate diagnosis of Alzheimer's disease (AD) is essential for patient care and will be increasingly important as disease modifying agents become available, early in the course of the disease.

  34. Machine learning framework for early MRI-based Alzheimer's conversion prediction in MCI subjects

    ✓ Peer-reviewed alzhub usefulness score 36 out of 100. NeuroImage 2014-10-12 Digital health tools

    Mild cognitive impairment (MCI) is a transitional stage between age-related cognitive decline and Alzheimer's disease (AD).

  35. Hierarchical feature representation and multimodal fusion with deep learning for AD/MCI diagnosis

    ✓ Peer-reviewed alzhub usefulness score 40 out of 100. NeuroImage 2014-07-18 Brain scans and imaging

    For the last decade, it has been shown that neuroimaging can be a potential tool for the diagnosis of Alzheimer's Disease (AD) and its prodromal stage, Mild Cognitive Impairment (MCI), and also fusion of different modalities can further provide the complementary information to enhance diagnostic accuracy.

  36. Fast parallel image registration on CPU and GPU for diagnostic classification of Alzheimer's disease

    ✓ Peer-reviewed alzhub usefulness score 48 out of 100. Frontiers in neuroinformatics 2014-01-16 Long-term cohort studies

    Nonrigid image registration is an important, but time-consuming task in medical image analysis.

  37. Deep learning based imaging data completion for improved brain disease diagnosis

    ✓ Peer-reviewed alzhub usefulness score 62 out of 100. Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention 2014-01-01 Digital health tools

    Combining multi-modality brain data for disease diagnosis commonly leads to improved performance.

  38. Multivariate data analysis and machine learning in Alzheimer's disease with a focus on structural magnetic resonance imaging

    ✓ Peer-reviewed alzhub usefulness score 40 out of 100. Journal of Alzheimer's disease : JAD 2014-01-01 Digital health tools

    Machine learning algorithms and multivariate data analysis methods have been widely utilized in the field of Alzheimer's disease (AD) research in recent years.

  39. Latent feature representation with stacked auto-encoder for AD/MCI diagnosis

    ✓ Peer-reviewed alzhub usefulness score 36 out of 100. Brain structure & function 2013-12-22 Digital health tools

    Recently, there have been great interests for computer-aided diagnosis of Alzheimer's disease (AD) and its prodromal stage, mild cognitive impairment (MCI).

  40. Accurate multimodal probabilistic prediction of conversion to Alzheimer's disease in patients with mild cognitive impairment

    ✓ Peer-reviewed alzhub usefulness score 40 out of 100. NeuroImage. Clinical 2013-05-19 Digital health tools

    Accurately identifying the patients that have mild cognitive impairment (MCI) who will go on to develop Alzheimer's disease (AD) will become essential as new treatments will require identification of AD patients at earlier stages in the disease process.

  41. Deep learning-based feature representation for AD/MCI classification

    ✓ Peer-reviewed alzhub usefulness score 40 out of 100. Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention 2013-01-01 Digital health tools

    In recent years, there has been a great interest in computer-aided diagnosis of Alzheimer's Disease (AD) and its prodromal stage, Mild Cognitive Impairment (MCI).

  42. Random forest-based similarity measures for multi-modal classification of Alzheimer's disease

    ✓ Peer-reviewed alzhub usefulness score 36 out of 100. NeuroImage 2012-10-04 Digital health tools

    Neurodegenerative disorders, such as Alzheimer's disease, are associated with changes in multiple neuroimaging and biological measures.

  43. Using Support Vector Machine to identify imaging biomarkers of neurological and psychiatric disease: a critical review

    ✓ Peer-reviewed alzhub usefulness score 36 out of 100. Neuroscience and biobehavioral reviews 2012-01-28 Risk factors

    Standard univariate analysis of neuroimaging data has revealed a host of neuroanatomical and functional differences between healthy individuals and patients suffering a wide range of neurological and psychiatric disorders.

  44. Does feature selection improve classification accuracy? Impact of sample size and feature selection on classification using anatomical magnetic resonance images

    ✓ Peer-reviewed alzhub usefulness score 36 out of 100. NeuroImage 2011-12-01 Digital health tools

    There are growing numbers of studies using machine learning approaches to characterize patterns of anatomical difference discernible from neuroimaging data.

  45. Multi-modal multi-task learning for joint prediction of multiple regression and classification variables in Alzheimer's disease

    ✓ Peer-reviewed alzhub usefulness score 36 out of 100. NeuroImage 2011-10-04 Brain scans and imaging

    Many machine learning and pattern classification methods have been applied to the diagnosis of Alzheimer's disease (AD) and its prodromal stage, i.e., mild cognitive impairment (MCI).

  46. BEaST: brain extraction based on nonlocal segmentation technique

    ✓ Peer-reviewed alzhub usefulness score 36 out of 100. NeuroImage 2011-09-16 Long-term cohort studies

    Brain extraction is an important step in the analysis of brain images.

  47. Multimodal classification of Alzheimer's disease and mild cognitive impairment

    ✓ Peer-reviewed alzhub usefulness score 40 out of 100. NeuroImage 2011-01-12 Tau biomarkers

    However, most existing research focuses on only a single modality of biomarkers for diagnosis of AD and MCI, although recent studies have shown that different biomarkers may provide complementary information for the diagnosis of AD and MCI.

  48. Diagnosis of Alzheimer's disease from EEG signals: where are we standing?

    ✓ Peer-reviewed alzhub usefulness score 36 out of 100. Current Alzheimer research 2010-09-01 Brain scans and imaging

    This paper reviews recent progress in the diagnosis of Alzheimer's disease (AD) from electroencephalograms (EEG).

  49. New diagnostic EEG markers of the Alzheimer's disease using visibility graph

    ✓ Peer-reviewed alzhub usefulness score 40 out of 100. Journal of neural transmission (Vienna, Austria : 1996) 2010-08-17 Digital health tools

    A new chaos-wavelet approach is presented for electroencephalogram (EEG)-based diagnosis of Alzheimer's disease (AD) employing a recently developed concept in graph theory, visibility graph (VG).

  50. Automatic classification of patients with Alzheimer's disease from structural MRI: a comparison of ten methods using the ADNI database

    ✓ Peer-reviewed alzhub usefulness score 45 out of 100. NeuroImage 2010-06-11 Brain scans and imaging

    Recently, several high dimensional classification methods have been proposed to automatically discriminate between patients with Alzheimer's disease (AD) or mild cognitive impairment (MCI) and elderly controls (CN) based on T1-weighted MRI.

From across this tag

7 of the 205 studies in this tag, picked by a fixed rule (a hash of each study's address), not by score or date; each page of this list shows a different 7. The same 7 appear here every time.

All topic tags with their study counts