ArticleNature communications2021
Deep learning encodes robust discriminative neuroimaging representations to outperform standard machine learning.
Article in Nature communications, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 102 papers, 1 of them a synthesis that pooled it.
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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.
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Who cites it
102 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Artificial Intelligence for Predicting Treatment Response in Patients With Anxiety Disorders After Cognitive Behavioral Therapy: Systematic Review and Meta-Analysis.Journal of medical Internet research · 2026Pooled it
- DEEP LEARNING OF SEMI-COMPETING RISK DATA VIA A NEW NEURAL EXPECTATION-MAXIMIZATION ALGORITHM.The annals of applied statistics · 2026Article
- Generalizable and explainable deep learning for brain MRI: a multi-cohort evaluation of 3D architectures for age and sex prediction.Brain informatics · 2026Article
- Deep learning-based MRI analysis reveals Lewy body co-pathology accelerates brain aging in Alzheimer's disease.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2026Article
- In silico models in oncology, neurology, and epidemiology: systems-level and multiscale perspectives.NPJ systems biology and applications · 2026Review
- Neural embedding of frailty in cognitively unimpaired aging and dementia across Latin America.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2026Article
- Anomaly Detection for Structural and Functional Connectivity in Glioma Patients.NMR in biomedicine · 2026Article
- Article
- Conditioned Graph Reconstruction of Brain Functional Network Connectivity Reveals Interpretable Latent Axes of Sex and Fluid Intelligence.bioRxiv : the preprint server for biology · 2026Article
- Disease detection and classification in temporal lobe epilepsy: step-wise versus simultaneous AI decision models in a multisite neuroimaging study.Brain communications · 2026Article
- An interpretable TimeMIL framework for fNIRS: differential diagnosis between schizophrenia and bipolar disorder.Frontiers in psychiatry · 2026Article
- SliceMap: a binary classification-driven 2D pipeline for detecting discriminative candidate regions in brain MRI.Frontiers in neuroimaging · 2026Article
- Multimodal Fusion of Structural and Diffusion MRI for Intelligence Prediction.Proceedings. IEEE Southwest Symposium on Image Analysis and Interpretation · 2026Article
- Deep learning-based classification of benign and malignant breast microcalcifications in mammography.Scientific reports · 2025Article
- Comparative analysis of multiple deep learning models with mitigation-driven approaches for enhanced Alzheimer's disease classification.Scientific reports · 2025Article
- Cardiovascular risk factors and the allostatic interoceptive network in dementia.Cardiovascular research · 2025Article
- MRI-based Intra- and Peritumoral Heterogeneity in Hepatocellular Carcinoma for Microvascular Invasion Prediction and Prognostic Risk Stratification.Radiology. Imaging cancer · 2025Article
- Generating MRI-Derived CSF Proxy-Markers to Predict and Visualize Alzheimer's Disease Progression.Human brain mapping · 2025Article
- Interpretable Disorder Signatures: Probing Neural Latent Spaces for Schizophrenia, Alzheimer's, and Autism Stratification.Brain sciences · 2025Article
- Statistical variability in comparing accuracy of neuroimaging based classification models via cross validation.Scientific reports · 2025Article
42 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
Recent critical commentaries unfavorably compare deep learning (DL) with standard machine learning (SML) approaches for brain imaging data analysis. However, their conclusions are often based on pre-engineered features depriving DL of its main advantage - representation learning. We conduct a large-scale systematic comparison profiled in multiple classification and regression tasks on structural MRI images and show the importance of representation learning for DL. Results show that if trained following prevalent DL practices, DL methods have the potential to scale particularly well and substantially improve compared to SML methods, while also presenting a lower asymptotic complexity in relative computational time, despite being more complex. We also demonstrate that DL embeddings span comprehensible task-specific projection spectra and that DL consistently localizes task-discriminative brain biomarkers. Our findings highlight the presence of nonlinearities in neuroimaging data that DL can exploit to generate superior task-discriminative representations for characterizing the human brain.
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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.