Trial reportMedical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention2014
Deep learning based imaging data completion for improved brain disease diagnosis.
Trial report in Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 129 papers, 5 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
129 citing papers in PubMed, 5 syntheses or guidelines pooled it.
- An Exploration: Alzheimer's Disease Classification Based on Convolutional Neural Network.BioMed research international · 2022Pooled it
- Transfer Learning for Alzheimer's Disease through Neuroimaging Biomarkers: A Systematic Review.Sensors (Basel, Switzerland) · 2021Pooled it
- Spectral-Domain OCT Measurements in Alzheimer's Disease: A Systematic Review and Meta-analysis.Ophthalmology · 2019Pooled it
- Deep Learning in Alzheimer's Disease: Diagnostic Classification and Prognostic Prediction Using Neuroimaging Data.Frontiers in aging neuroscience · 2019Pooled it
- Advancing Alzheimer's research: A review of big data promises.International journal of medical informatics · 2017Pooled it
- Centralized Review of Alzheimer's Disease and Related Dementias Biomedical Repositories and Computational Methods.Bioengineering (Basel, Switzerland) · 2026Review
- A longitudinal and explainable 2.5D deep learning framework for Alzheimer's disease progression using ADNI MRI.Neuroimage. Reports · 2026Article
- Comparative Analysis of Deep Learning Techniques in Alzheimer's Disease Diagnosis: Trends, Challenges, and Future Directions.MethodsX · 2026Review
- Alzheimer's related dementia severity classification from magnetic resonance imaging using derivative-free optimization of convolutional neural network.Scientific reports · 2026Article
- Combined Use of Microwave Sensing Technologies and Artificial Intelligence for Biomedical Monitoring and Imaging.Biosensors · 2026Review
- Score-based generative diffusion models to synthesize full-dose FDG brain PET from MRI in epilepsy patients.Frontiers in artificial intelligence · 2026Article
- Multimodal neuroimaging and AI integration in cognitive disorders: advances, challenges, and future directions for precision medicine.Psychoradiology · 2026Review
- Article
- Applications of machine learning for computer-aided diagnosis of Parkinson's disease: progress and benchmark case study.Artificial intelligence review · 2025Article
- Generation of synthetic TSPO PET maps from structural MRI images.Frontiers in neuroinformatics · 2025Article
- EEG-based schizophrenia diagnosis using deep learning with multi-scale and adaptive feature selection.PeerJ. Computer science · 2025Article
- Removing Adversarial Noise in X-ray Images via Total Variation Minimization and Patch-Based Regularization for Robust Deep Learning-based Diagnosis.Journal of imaging informatics in medicine · 2024Article
- Employing Informatics Strategies in Alzheimer's Disease Research: A Review from Genetics, Multiomics, and Biomarkers to Clinical Outcomes.Annual review of biomedical data science · 2024Review
- Determining Protein Secondary Structures in Heterogeneous Medium-Resolution Cryo-EM Images Using CryoSSESeg.ACS omega · 2024Article
- Turning brain MRI into diagnostic PET:Medical image analysis · 2024Article
69 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
Abstract
Combining multi-modality brain data for disease diagnosis commonly leads to improved performance. A challenge in using multimodality data is that the data are commonly incomplete; namely, some modality might be missing for some subjects. In this work, we proposed a deep learning based framework for estimating multi-modality imaging data. Our method takes the form of convolutional neural networks, where the input and output are two volumetric modalities. The network contains a large number of trainable parameters that capture the relationship between input and output modalities. When trained on subjects with all modalities, the network can estimate the output modality given the input modality. We evaluated our method on the Alzheimer's Disease Neuroimaging Initiative (ADNI) database, where the input and output modalities are MRI and PET images, respectively. Results showed that our method significantly outperformed prior methods.
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What OpenQuestion holds
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.