Article... IEEE International Conference on Computer Vision workshops. IEEE International Conference on Computer Vision2021
Multi-scanner Harmonization of Paired Neuroimaging Data via Structure Preserving Embedding Learning.
Article in ... IEEE International Conference on Computer Vision workshops. IEEE International Conference on Computer Vision, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 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.
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
10 citing papers in PubMed.
- VISGAB: Virtual staining-driven GAN benchmarking for optimizing skin tissue histology.Scientific reports · 2025Article
- WMH Contributions to Cognitive Impairment: Rationale and Design of the Diverse VCID Study.Stroke · 2025Review
- 3D histological mapping of hippocampal subfields: a comparative study in patients with schizophrenia and healthy controls.Frontiers in psychiatry · 2025Article
- Quantitative MRI biomarker for classification of clinically significant prostate cancer: Calibration for reproducibility across echo times.Journal of applied clinical medical physics · 2024Article
- Deep learning for the harmonization of structural MRI scans: a survey.Biomedical engineering online · 2024Review
- Image harmonization improves consistency of intra-rater delineations of MS lesions in heterogeneous MRI.Neuroimage. Reports · 2024Article
- MISPEL: A supervised deep learning harmonization method for multi-scanner neuroimaging data.Medical image analysis · 2023Article
- Machine Learning for Brain MRI Data Harmonisation: A Systematic Review.Bioengineering (Basel, Switzerland) · 2023Review
- ComBat Harmonization: Empirical Bayes versus fully Bayes approaches.NeuroImage. Clinical · 2023Article
- A guide for researchers seeking training in retrospective data harmonization for population neuroscience studies of Alzheimer's disease and related dementias.Frontiers in neuroimaging · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
Abstract
Combining datasets from multiple sites/scanners has been becoming increasingly more prevalent in modern neuroimaging studies. Despite numerous benefits from the growth in sample size, substantial technical variability associated with site/scanner-related effects exists which may inadvertently bias subsequent downstream analyses. Such a challenge calls for a data harmonization procedure which reduces the scanner effects and allows the scans to be combined for pooled analyses. In this work, we present MISPEL (Multi-scanner Image harmonization via Structure Preserving Embedding Learning), a multi-scanner harmonization framework. Unlike existing techniques, MISPEL does not assume a perfect coregistration across the scans, and the framework is naturally extendable to more than two scanners. Importantly, we incorporate our multi-scanner dataset where each subject is scanned on four different scanners. This unique paired dataset allows us to define and aim for an ideal harmonization (e.g., each subject with identical brain tissue volumes on all scanners). We extensively view scanner effects under varying metrics and demonstrate how MISPEL significantly improves them.
Identifiers
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.