ArticleHuman brain mapping2022
The impact of transfer learning on 3D deep learning convolutional neural network segmentation of the hippocampus in mild cognitive impairment and Alzheimer disease subjects.
Article in Human brain mapping, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers, 1 of them a synthesis that pooled it.
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Who cites it
11 citing papers in PubMed, 1 synthesis or guideline pooled it, 28 citations in OpenAlex.
- From Single-Modal to Multi-Modal Artificial Intelligence in Alzheimer's Disease: A Systematic Review of Databases, Modalities, Diagnostic Performance, and Clinical Translation Challenges.Sensors (Basel, Switzerland) · 2026Pooled it
- Deep learning for Alzheimer's disease: advances in classification, segmentation, subtyping, and explainability.Biomedical engineering online · 2025Review
- Recent Advancements in Neuroimaging-Based Alzheimer's Disease Prediction Using Deep Learning Approaches in e-Health: A Systematic Review.Health science reports · 2025Review
- Deep learning techniques for Alzheimer's disease detection in 3D imaging: A systematic review.Health science reports · 2024Review
- Revolutionizing anemia detection: integrative machine learning models and advanced attention mechanisms.Visual computing for industry, biomedicine, and art · 2024Article
- AI in imaging: the regulatory landscape.The British journal of radiology · 2024Article
- Machine Learning for Brain MRI Data Harmonisation: A Systematic Review.Bioengineering (Basel, Switzerland) · 2023Review
- Toward hippocampal volume measures on ultra-high field magnetic resonance imaging: a comprehensive comparison study between deep learning and conventional approaches.Frontiers in neuroscience · 2023Article
- The impact of transfer learning on 3D deep learning convolutional neural network segmentation of the hippocampus in mild cognitive impairment and Alzheimer disease subjects.Human brain mapping · 2022Article
- Deep learning in neuroimaging data analysis: Applications, challenges, and solutions.Frontiers in neuroimaging · 2022Review
- Adapting UK Biobank imaging for use in a routine memory clinic setting: The Oxford Brain Health Clinic.NeuroImage. Clinical · 2022Article
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Authors and funding
11 authors at 5 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Research on segmentation of the hippocampus in magnetic resonance images through deep learning convolutional neural networks (CNNs) shows promising results, suggesting that these methods can identify small structural abnormalities of the hippocampus, which are among the earliest and most frequent brain changes associated with Alzheimer disease (AD). However, CNNs typically achieve the highest accuracy on datasets acquired from the same domain as the training dataset. Transfer learning allows domain adaptation through further training on a limited dataset. In this study, we applied transfer learning on a network called spatial warping network segmentation (SWANS), developed and trained in a previous study. We used MR images of patients with clinical diagnoses of mild cognitive impairment (MCI) and AD, segmented by two different raters. By using transfer learning techniques, we developed four new models, using different training methods. Testing was performed using 26% of the original dataset, which was excluded from training as a hold-out test set. In addition, 10% of the overall training dataset was used as a hold-out validation set. Results showed that all the new models achieved better hippocampal segmentation quality than the baseline SWANS model (p
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