ArticleFrontiers in psychiatry2022
Deep Learning in Neuroimaging: Overcoming Challenges With Emerging Approaches.
Article in Frontiers in psychiatry, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
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Who cites it
20 citing papers in PubMed, 38 citations in OpenAlex.
- Causal graph neural networks for healthcare.Nature biomedical engineering · 2026Review
- Decision processes in 3D structural MRI schizophrenia classification evaluated with saliency maps.Scientific reports · 2026Article
- Diffusion models for brain imaging computing: a survey of frameworks and applications.Brain informatics · 2026Review
- On the importance of white-box machine learning models in psychiatry: An example predicting suicidality in depression.Psychiatry research · 2026Article
- Deep learning in fetal, infant, and toddler neuroimaging research.Developmental cognitive neuroscience · 2026Review
- Functional and Clinical: An Explainable Deep Learning Model for Multimodal Alzheimer's Disease Classification.Brain and behavior · 2026Article
- SliceMap: a binary classification-driven 2D pipeline for detecting discriminative candidate regions in brain MRI.Frontiers in neuroimaging · 2026Article
- From Lesion to Decision: AI for ARIA Detection and Predictive Imaging in Alzheimer's Disease.Biomedicines · 2025Review
- Machine Learning-Based Clinical Prediction Models in Psychopathology: Can Transfer Learning Fix the "Illusory Generalizability" Problem?Biological psychiatry. Cognitive neuroscience and neuroimaging · 2025Article
- Review
- Explainable Artificial Intelligence in Neuroimaging of Alzheimer's Disease.Diagnostics (Basel, Switzerland) · 2025Review
- Towards Clinical Diagnoses: Classifying Alzheimer's Disease Using Single fMRI, Small Datasets, and Transfer Learning.Brain and behavior · 2025Article
- Information Geometry and Manifold Learning: A Novel Framework for Analyzing Alzheimer's Disease MRI Data.Diagnostics (Basel, Switzerland) · 2025Article
- Predicting alcohol use disorder risk in firefighters using a multimodal deep learning model: a cross-sectional study.Frontiers in psychiatry · 2025Article
- Applications and advances of combined fMRI-fNIRs techniques in brain functional research.Frontiers in neurology · 2025Review
- Predicting Clinical Improvement in Early Psychosis Using Circuit-Based Resting-State Functional Magnetic Resonance Imaging.Schizophrenia bulletin · 2024Article
- Explainable fNIRS-based pain decoding under pharmacological conditions via deep transfer learning approach.Neurophotonics · 2024Article
- Contrastive learning for neural fingerprinting from limited neuroimaging data.Frontiers in nuclear medicine · 2024Article
- Predicting conversion to psychosis using machine learning: response to Cannon.Frontiers in psychiatry · 2024Article
- Deep learning interpretability in neuroimaging: A comprehensive survey and methodological recommendations.Imaging neuroscience (Cambridge, Mass.)Article
Corrections and comments
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Authors and funding
3 authors at 1 institution in 1 country.
Funding
Abstract
Deep learning (DL) is of great interest in psychiatry due its potential yet largely untapped ability to utilize multidimensional datasets (such as fMRI data) to predict clinical outcomes. Typical DL methods, however, have strong assumptions, such as large datasets and underlying model opaqueness, that are suitable for natural image prediction problems but not medical imaging. Here we describe three relatively novel DL approaches that may help accelerate its incorporation into mainstream psychiatry research and ultimately bring it into the clinic as a prognostic tool. We first introduce two methods that can reduce the amount of training data required to develop accurate models. These may prove invaluable for fMRI-based DL given the time and monetary expense required to acquire neuroimaging data. These methods are (1)
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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.