ArticleMachine learning in medical imaging. MLMI (Workshop)2018
End-To-End Alzheimer's Disease Diagnosis and Biomarker Identification.
Article in Machine learning in medical imaging. MLMI (Workshop), 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers.
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Who cites it
28 citing papers in PubMed.
- Flexible Multimodal Neuroimaging Fusion for Alzheimer's Disease Progression Prediction.Applications of medical artificial intelligence. AMAI (Workshop) (4th : 2024 : Taejon-si, Korea) · 2026Article
- IProceedings of machine learning research · 2025Article
- Machine-learning models for Alzheimer's disease diagnosis using neuroimaging data: survey, reproducibility, and generalizability evaluation.Brain informatics · 2025Review
- An MRI based histogram oriented gradient and deep learning approach for accurate classification of mild cognitive impairment and Alzheimer's disease.Frontiers in medicine · 2025Article
- A multi-slice attention fusion and multi-view personalized fusion lightweight network for Alzheimer's disease diagnosis.BMC medical imaging · 2024Article
- Joint transformer architecture in brain 3D MRI classification: its application in Alzheimer's disease classification.Scientific reports · 2024Article
- Detecting and Characterizing Inferior Vena Cava Filters on Abdominal Computed Tomography with Data-Driven Computational Frameworks.Journal of digital imaging · 2023Article
- CAD-ALZ: A Blockwise Fine-Tuning Strategy on Convolutional Model and Random Forest Classifier for Recognition of Multistage Alzheimer's Disease.Diagnostics (Basel, Switzerland) · 2023Article
- Assessing an AI-based Smart Imagery Framing and Truthing (SIFT) system to assist radiologists annotating lung abnormalities on chest X-ray images for development of deep learning models.Proceedings of SPIE--the International Society for Optical Engineering · 2023Article
- Ensemble learning using traditional machine learning and deep neural network for diagnosis of Alzheimer's disease.IBRO neuroscience reports · 2022Article
- Wavelet-Based Fractal Analysis of rs-fMRI for Classification of Alzheimer's Disease.Sensors (Basel, Switzerland) · 2022Article
- A new classification network for diagnosing Alzheimer's disease in class-imbalance MRI datasets.Frontiers in neuroscience · 2022Article
- Objective speech intelligibility prediction using a deep learning model with continuous speech-evoked cortical auditory responses.Frontiers in neuroscience · 2022Article
- Patch individual filter layers in CNNs to harness the spatial homogeneity of neuroimaging data.Scientific reports · 2021Article
- Training Strategies for Radiology Deep Learning Models in Data-limited Scenarios.Radiology. Artificial intelligence · 2021Review
- Analysis of Features of Alzheimer's Disease: Detection of Early Stage from Functional Brain Changes in Magnetic Resonance Images Using a Finetuned ResNet18 Network.Diagnostics (Basel, Switzerland) · 2021Article
- Longitudinal Pooling & Consistency Regularization to Model Disease Progression From MRIs.IEEE journal of biomedical and health informatics · 2021Article
- Convolutional neural networks for Alzheimer's disease detection on MRI images.Journal of medical imaging (Bellingham, Wash.) · 2021Article
- Diagnosis of Alzheimer's Disease Severity with fMRI Images Using Robust Multitask Feature Extraction Method and Convolutional Neural Network (CNN).Computational and mathematical methods in medicine · 2021Article
- Influence of medical domain knowledge on deep learning for Alzheimer's disease prediction.Computer methods and programs in biomedicine · 2020Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
As shown in computer vision, the power of deep learning lies in automatically learning relevant and powerful features for any perdition task, which is made possible through end-to-end architectures. However, deep learning approaches applied for classifying medical images do not adhere to this architecture as they rely on several pre- and post-processing steps. This shortcoming can be explained by the relatively small number of available labeled subjects, the high dimensionality of neuroimaging data, and difficulties in interpreting the results of deep learning methods. In this paper, we propose a simple 3D Convolutional Neural Networks and exploit its model parameters to tailor the end-to-end architecture for the diagnosis of Alzheimer's disease (AD). Our model can diagnose AD with an accuracy of 94.1% on the popular ADNI dataset using only MRI data, which outperforms the previous state-of-the-art. Based on the learned model, we identify the disease biomarkers, the results of which were in accordance with the literature. We further transfer the learned model to diagnose mild cognitive impairment (MCI), the prodromal stage of AD, which yield better results compared to other methods.
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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.