ArticleNeuroImage2016
Domain adaptation for Alzheimer's disease diagnostics.
Article in NeuroImage, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 41 papers.
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Who cites it
41 citing papers in PubMed.
- Multimodal multi-instance learning for cardiopulmonary exercise testing performance prediction.NPJ digital medicine · 2026Article
- Diagnostic classifications based on cognitive, CSF, and PET biomarkers across ADNI cohorts: cross-sectional and longitudinal variability.Brain communications · 2026Article
- Source-Free Active Learning for Adapting Alzheimer's Diagnostic Deep Learning Models Across Neuroimaging Cohorts.... IEEE-EMBS International Conference on Biomedical and Health Informatics. IEEE-EMBS International Conference on Biomedical and Health Informatics · 2025Article
- Domain Adaptation-enhanced searchlight: enabling classification of brain states from visual perception to mental imagery.Brain informatics · 2025Article
- Development and validation of an intratumoral-peritumoral deep transfer learning fusion model for differentiating BI-RADS 3-4 breast nodules.Gland surgery · 2025Article
- Addressing cross-population domain shift in chest X-ray classification through supervised adversarial domain adaptation.Scientific reports · 2025Article
- VAE deep learning model with domain adaptation, transfer learning and harmonization for diagnostic classification from multi-site neuroimaging data.Frontiers in neuroinformatics · 2025Article
- Artificial Intelligence-Based Methodologies for Early Diagnostic Precision and Personalized Therapeutic Strategies in Neuro-Ophthalmic and Neurodegenerative Pathologies.Brain sciences · 2024Review
- Domain adaptation using AdaBN and AdaIN for high-resolution IVD mesh reconstruction from clinical MRI.International journal of computer assisted radiology and surgery · 2024Article
- Federated learning for medical image analysis: A survey.Pattern recognition · 2024Article
- Correlation enhanced distribution adaptation for prediction of fall risk.Scientific reports · 2024Article
- Joint multi-site domain adaptation and multi-modality feature selection for the diagnosis of psychiatric disorders.NeuroImage. Clinical · 2024Article
- The Advent of Domain Adaptation into Artificial Intelligence for Gastrointestinal Endoscopy and Medical Imaging.Diagnostics (Basel, Switzerland) · 2023Review
- Attention-Guided Autoencoder for Automated Progression Prediction of Subjective Cognitive Decline With Structural MRI.IEEE journal of biomedical and health informatics · 2023Article
- The Usefulness of Machine Learning-Based Evaluation of Clinical and Pretreatment [Molecular imaging and biology · 2023Article
- Machine Learning for Brain MRI Data Harmonisation: A Systematic Review.Bioengineering (Basel, Switzerland) · 2023Review
- Multi-auxiliary domain transfer learning for diagnosis of MCI conversion.Neurological sciences : official journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology · 2022Article
- Domain Adaptation for Medical Image Analysis: A Survey.IEEE transactions on bio-medical engineering · 2022Review
- Accounting for symptom heterogeneity can improve neuroimaging models of antidepressant response after electroconvulsive therapy.Human brain mapping · 2021Article
- Augmented Multicenter Graph Convolutional Network for COVID-19 Diagnosis.IEEE transactions on industrial informatics · 2021Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
With the increasing prevalence of Alzheimer's disease, research focuses on the early computer-aided diagnosis of dementia with the goal to understand the disease process, determine risk and preserving factors, and explore preventive therapies. By now, large amounts of data from multi-site studies have been made available for developing, training, and evaluating automated classifiers. Yet, their translation to the clinic remains challenging, in part due to their limited generalizability across different datasets. In this work, we describe a compact classification approach that mitigates overfitting by regularizing the multinomial regression with the mixed ℓ1/ℓ2 norm. We combine volume, thickness, and anatomical shape features from MRI scans to characterize neuroanatomy for the three-class classification of Alzheimer's disease, mild cognitive impairment and healthy controls. We demonstrate high classification accuracy via independent evaluation within the scope of the CADDementia challenge. We, furthermore, demonstrate that variations between source and target datasets can substantially influence classification accuracy. The main contribution of this work addresses this problem by proposing an approach for supervised domain adaptation based on instance weighting. Integration of this method into our classifier allows us to assess different strategies for domain adaptation. Our results demonstrate (i) that training on only the target training set yields better results than the naïve combination (union) of source and target training sets, and (ii) that domain adaptation with instance weighting yields the best classification results, especially if only a small training component of the target dataset is available. These insights imply that successful deployment of systems for computer-aided diagnostics to the clinic depends not only on accurate classifiers that avoid overfitting, but also on a dedicated domain adaptation strategy.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.