ArticleScientific reports2023
Effects of MRI scanner manufacturers in classification tasks with deep learning models.
Article in Scientific reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 33 papers.
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.
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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.
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Who cites it
33 citing papers in PubMed.
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- Letter to the Editor: Beyond external validation: Is TED-Net ready for real-world generalization?European radiology · 2026Article
- Article
- Evaluation of Image-Level Harmonization Methods for Multi-Center MR Neuroimaging.Journal of magnetic resonance imaging : JMRI · 2026Article
- Toward trustworthy clinical AI for obsessive-compulsive disorder: reliability, generalizability, and interpretability of a transformer model across the ENIGMA-OCD consortium.medRxiv : the preprint server for health sciences · 2026Article
- A Multiple Sclerosis MRI Dataset with Tri-Mask Annotations for Lesion Segmentation.Scientific data · 2026Article
- A generalizable foundation model for analysis of human brain MRI.Nature neuroscience · 2026Article
- NeuroFM: Toward Precision Neuroimaging with Foundation Models for Individualized Brain Health Estimation.medRxiv : the preprint server for health sciences · 2026Article
- Decoding the Glioblastoma Microenvironment: AI-Driven Analysis of Cellular MRI Signatures for Targeted Therapy.Cellular and molecular neurobiology · 2026Review
- From promise to practice: a scoping review of AI applications in abdominal radiology.Abdominal radiology (New York) · 2026Article
- Neurodevelopmental disorders in children: the role of MRI in early detection and intervention planning.Frontiers in neuroscience · 2026Review
- Multimodal neuroimaging and AI integration in cognitive disorders: advances, challenges, and future directions for precision medicine.Psychoradiology · 2026Review
- Skull-stripping induces shortcut learning in MRI-based Alzheimer's disease classification.Insights into imaging · 2025Article
- Explainable 3D CNNs link regional and network level disruption in early Parkinson's MRIs to symptom progression.bioRxiv : the preprint server for biology · 2025Article
- Medical machine learning operations: a framework to facilitate clinical AI development and deployment in radiology.European radiology · 2025Review
- Improving Clinically Significant Prostate Cancer Detection with a Multimodal Machine Learning Approach: A Large-Scale Multicenter Study.Radiology. Imaging cancer · 2025Article
- Rethinking MRI as a measurement device through modular and portable pipelines.Magma (New York, N.Y.) · 2025Review
- Functional Connectivity Patterns Following Mild Traumatic Brain Injury and the Association With Longitudinal Cognitive Function.Human brain mapping · 2025Article
- Decoding dynamic brain networks in Parkinson's disease with temporal attention.Scientific reports · 2025Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
Deep learning has become a leading subset of machine learning and has been successfully employed in diverse areas, ranging from natural language processing to medical image analysis. In medical imaging, researchers have progressively turned towards multi-center neuroimaging studies to address complex questions in neuroscience, leveraging larger sample sizes and aiming to enhance the accuracy of deep learning models. However, variations in image pixel/voxel characteristics can arise between centers due to factors including differences in magnetic resonance imaging scanners. Such variations create challenges, particularly inconsistent performance in machine learning-based approaches, often referred to as domain shift, where the trained models fail to achieve satisfactory or improved results when confronted with dissimilar test data. This study analyzes the performance of multiple disease classification tasks using multi-center MRI data obtained from three widely used scanner manufacturers (GE, Philips, and Siemens) across several deep learning-based networks. Furthermore, we investigate the efficacy of mitigating scanner vendor effects using ComBat-based harmonization techniques when applied to multi-center datasets of 3D structural MR images. Our experimental results reveal a substantial decline in classification performance when models trained on one type of scanner manufacturer are tested with data from different manufacturers. Moreover, despite applying ComBat-based harmonization, the harmonized images do not demonstrate any noticeable performance enhancement for disease classification tasks.
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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.