ArticleMedical image analysis2017
Deep ensemble learning of sparse regression models for brain disease diagnosis.
Article in Medical image analysis, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 78 papers, 3 of them syntheses that pooled it.
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Who cites it
78 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- 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
- Machine learning methods for predicting progression from mild cognitive impairment to Alzheimer's disease dementia: a systematic review.Alzheimer's research & therapy · 2021Pooled it
- A Systematic Review and Implementation of IoT-Based Pervasive Sensor-Enabled Tracking System for Dementia Patients.Journal of medical systems · 2019Pooled it
- A longitudinal and explainable 2.5D deep learning framework for Alzheimer's disease progression using ADNI MRI.Neuroimage. Reports · 2026Article
- Artificial intelligence and multiomics integration for Parkinson's disease drug development.Molecules and cells · 2026Review
- Alzheimer's related dementia severity classification from magnetic resonance imaging using derivative-free optimization of convolutional neural network.Scientific reports · 2026Article
- Progression-Aware and Explainable CNN-Transformer Framework for Multiclass Alzheimer's Disease Staging Using MRI.Diagnostics (Basel, Switzerland) · 2026Article
- The role of multimodality in clinical disease diagnosis: advances, challenges, and opportunities.Frontiers in public health · 2026Review
- Ensemble based in transfer learning for cytological classification in pleural fluid.Frontiers in digital health · 2026Article
- Preoperative MVI prediction in intrahepatic cholangiocarcinoma via deep learning analysis of intratumoral and peritumoral features on multi-sequence MRI.BMC medical imaging · 2025Article
- AlzFormer: Video-based space-time attention model for early diagnosis of Alzheimer's disease.Neuroscience · 2025Article
- High-Performance Open-Source AI for Breast Cancer Detection and Localization in MRI.Radiology. Artificial intelligence · 2025Article
- Diagnosis of Alzheimer's disease using brain [Formula: see text]-FDG PET imaging based on a state space model.Scientific reports · 2025Article
- A fine-tuned convolutional neural network model for accurate Alzheimer's disease classification.Scientific reports · 2025Article
- Machine-learning models for Alzheimer's disease diagnosis using neuroimaging data: survey, reproducibility, and generalizability evaluation.Brain informatics · 2025Review
- Alzheimer's disease image classification based on enhanced residual attention network.PloS one · 2025Article
- Comprehensive evaluation and clinical implications of kernel extreme learning machine long short term memory transformer framework.American journal of translational research · 2025Article
- A Multi-Label Deep Learning Model for Detailed Classification of Alzheimer's Disease.Actas espanolas de psiquiatria · 2025Article
- Feature-Based Classification of Mild Cognitive Impairment and Alzheimer's Disease Based on Optical Coherence Tomographic Angiographic Image.Sensors (Basel, Switzerland) · 2024Article
- Deep Learning for Alzheimer's Disease Prediction: A Comprehensive Review.Diagnostics (Basel, Switzerland) · 2024Review
18 more citing papers are in PubMed but not listed here.
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4 authors.
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Abstract
Recent studies on brain imaging analysis witnessed the core roles of machine learning techniques in computer-assisted intervention for brain disease diagnosis. Of various machine-learning techniques, sparse regression models have proved their effectiveness in handling high-dimensional data but with a small number of training samples, especially in medical problems. In the meantime, deep learning methods have been making great successes by outperforming the state-of-the-art performances in various applications. In this paper, we propose a novel framework that combines the two conceptually different methods of sparse regression and deep learning for Alzheimer's disease/mild cognitive impairment diagnosis and prognosis. Specifically, we first train multiple sparse regression models, each of which is trained with different values of a regularization control parameter. Thus, our multiple sparse regression models potentially select different feature subsets from the original feature set; thereby they have different powers to predict the response values, i.e., clinical label and clinical scores in our work. By regarding the response values from our sparse regression models as target-level representations, we then build a deep convolutional neural network for clinical decision making, which thus we call 'Deep Ensemble Sparse Regression Network.' To our best knowledge, this is the first work that combines sparse regression models with deep neural network. In our experiments with the ADNI cohort, we validated the effectiveness of the proposed method by achieving the highest diagnostic accuracies in three classification tasks. We also rigorously analyzed our results and compared with the previous studies on the ADNI cohort in the literature.
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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.