ArticleIEEE transactions on pattern analysis and machine intelligence2020
Hierarchical Fully Convolutional Network for Joint Atrophy Localization and Alzheimer's Disease Diagnosis Using Structural MRI.
Article in IEEE transactions on pattern analysis and machine intelligence, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 128 papers, 5 of them syntheses that pooled it.
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Who cites it
128 citing papers in PubMed, 5 syntheses or guidelines pooled it, 541 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
- Multimodal AI for Alzheimer Disease Diagnosis: Systematic Review of Datasets, Models, and Modalities.Journal of medical Internet research · 2026Pooled it
- Language comprehension in mild cognitive impairment: a bibliometric study of research trends and thematic structures.Frontiers in psychology · 2026Pooled it
- Predictive modelling of brain disorders with magnetic resonance imaging: A systematic review of modelling practices, transparency, and interpretability in the use of convolutional neural networks.Human brain mapping · 2023Pooled it
- Interpretable machine learning for dementia: A systematic review.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2023Pooled it
- Vaginal Canal Segmentation with nnU-Net Algorithm from MRI Defecography for Biomechanical Analysis.International urogynecology journal · 2026Article
- FLAME: Federated Learning and Aggregated Multi-Model Ensemble for Multi-Class Alzheimer's Disease Stage Classification from Structured Clinical Data.Diagnostics (Basel, Switzerland) · 2026Article
- Diagnosis and Prediction of Alzheimer's Disease via a High-Level Convolutional Block Attention Module-Residual Network.Interdisciplinary sciences, computational life sciences · 2026Article
- AlzhiNet: Traversing from 2D-CNN to 3D-CNN, Towards Early Detection and Diagnosis of Alzheimer's Disease.Interdisciplinary sciences, computational life sciences · 2026Article
- Deep learning models identify brain changes during the progression of Alzheimer's disease.NPJ systems biology and applications · 2026Article
- MRI neuroimaging-based Alzheimer's disease stage classification using deep neural network with convolutional block attention module and GAN-style noise injection.Scientific reports · 2026Article
- A token efficient vision framework using patch residual transformer for Alzheimer's disease diagnosis.Scientific reports · 2026Article
- Toward neuroimaging-based diagnostic support: a deep learning approach with a closed-loop system for psychiatric classification.iScience · 2025Article
- ViViMZheimer a slice based end to end model for Alzheimer's disease diagnosis from 3D MRI.Scientific reports · 2025Article
- A vision-language foundation model for Alzheimer's disease diagnosis using MRI and clinical data.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2025Article
- AlzFormer: Video-based space-time attention model for early diagnosis of Alzheimer's disease.Neuroscience · 2025Article
- Brain Anatomy Prior Modeling to Forecast Clinical Progression of Cognitive Impairment with Structural MRI.Pattern recognition · 2025Article
- Mind the Gap: Does Brain Age Improve Alzheimer's Disease Prediction?Human brain mapping · 2025Article
- A novel neuroimaging based early detection framework for alzheimer disease using deep learning.Scientific reports · 2025Article
- Applying a multi-task and multi-instance framework to predict axillary lymph node metastases in breast cancer.NPJ precision oncology · 2025Article
68 more citing papers are in PubMed but not listed here.
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Authors and funding
4 authors at 1 institution in 2 countries.
Funding
Abstract
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. Recently, a few deep learning methods (e.g., convolutional neural networks, CNNs) have been proposed to learn task-oriented features from sMRI for AD diagnosis, and achieved superior performance than the conventional learning-based methods using hand-crafted features. However, these existing CNN-based methods still require the pre-determination of informative locations in sMRI. That is, the stage of discriminative atrophy localization is isolated to the latter stages of feature extraction and classifier construction. In this paper, we propose a hierarchical fully convolutional network (H-FCN) to automatically identify discriminative local patches and regions in the whole brain sMRI, upon which multi-scale feature representations are then jointly learned and fused to construct hierarchical classification models for AD diagnosis. Our proposed H-FCN method was evaluated on a large cohort of subjects from two independent datasets (i.e., ADNI-1 and ADNI-2), demonstrating good performance on joint discriminative atrophy localization and brain disease diagnosis.
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Registered trials
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.