ReviewFrontiers in neuroinformatics2021
Deep Learning in Large and Multi-Site Structural Brain MR Imaging Datasets.
Review in Frontiers in neuroinformatics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers, 3 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
26 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Machine learning-based models for preoperative prediction of pituitary adenoma consistency: a systematic review and meta-analysis.Acta neurochirurgica · 2026Pooled it
- Machine learning-based methods in diagnosing cardiac amyloidosis: a meta-analysis.Frontiers in cardiovascular medicine · 2026Pooled it
- Integrating Neuroimaging and Machine Learning to Predict Mental Disorder Outcomes: A Systematic Review.Advances in experimental medicine and biology · 2026Pooled it
- Photoacoustic device fingerprints induce bias in deep learning models.Scientific reports · 2026Article
- A Multimodal Dataset to Investigate Task-Evoked Negative BOLD Response and Neurodegeneration.Scientific data · 2026Article
- Knowledge attitudes and practices regarding MRI safety among healthcare providers and patients/family members in China.Scientific reports · 2026Article
- Construction and application of high-quality development model for public hospitals oriented to regional medical center: an integrated advanced imaging platform for computed tomography, magnetic resonance imaging, and positron emission tomography-computed tomography services.Journal of applied clinical medical physics · 2026Article
- Deep Learning for fODF Estimation in Infant Brains: Model Comparison, Ground-Truth Impact, and Domain Shift Mitigation.Human brain mapping · 2025Article
- Optimization of the Non-Local Means Algorithm for Breast Diffusion-Weighted Magnetic Resonance Imaging Using a 3D-Printed Breast-Mimicking Phantom.Life (Basel, Switzerland) · 2025Article
- Investigation of ComBat Harmonization on Radiomic and Deep Features from Multi-Center Abdominal MRI Data.Journal of imaging informatics in medicine · 2025Article
- Multi-Center Fetal Brain Tissue Annotation (FeTA) Challenge 2022 Results.IEEE transactions on medical imaging · 2025Article
- Deep learning based automatic detection and dipole estimation of epileptic discharges in MEG: a multi-center study.Scientific reports · 2024Article
- Article
- A General Primer for Data Harmonization.Scientific data · 2024Article
- Brain MRI sequence and view plane identification using deep learning.Frontiers in neuroinformatics · 2024Article
- Diffusion deep learning for brain age prediction and longitudinal tracking in children through adulthood.Imaging neuroscience (Cambridge, Mass.) · 2024Article
- Fully Automated Skull Stripping from Brain Magnetic Resonance Images Using Mask RCNN-Based Deep Learning Neural Networks.Brain sciences · 2023Article
- Image harmonization: A review of statistical and deep learning methods for removing batch effects and evaluation metrics for effective harmonization.NeuroImage · 2023Review
- Eye Recognition by YOLO for Inner Canthus Temperature Detection in the Elderly Using a Transfer Learning Approach.Sensors (Basel, Switzerland) · 2023Article
- Deep Learning for Image Enhancement and Correction in Magnetic Resonance Imaging-State-of-the-Art and Challenges.Journal of digital imaging · 2023Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Large, multi-site, heterogeneous brain imaging datasets are increasingly required for the training, validation, and testing of advanced deep learning (DL)-based automated tools, including structural magnetic resonance (MR) image-based diagnostic and treatment monitoring approaches. When assembling a number of smaller datasets to form a larger dataset, understanding the underlying variability between different acquisition and processing protocols across the aggregated dataset (termed "batch effects") is critical. The presence of variation in the training dataset is important as it more closely reflects the true underlying data distribution and, thus, may enhance the overall generalizability of the tool. However, the impact of batch effects must be carefully evaluated in order to avoid undesirable effects that, for example, may reduce performance measures. Batch effects can result from many sources, including differences in acquisition equipment, imaging technique and parameters, as well as applied processing methodologies. Their impact, both beneficial and adversarial, must be considered when developing tools to ensure that their outputs are related to the proposed clinical or research question (
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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.