Evidence map›Paper›PMID 39281201›Full record

ArticleMachine learning in clinical neuroimaging : 6th international workshop, MLCN 2023, held in conjunction with MICCAI 2023, Vancouver, BC, Canada, October 8, 2023, proceedings. MLCN (Workshop) (6th : 2023 : Vancouver, B.C.)2023

Learning Sequential Information in Task-based fMRI for Synthetic Data Augmentation.

Jiyao Wang, Nicha C Dvornek, Lawrence H Staib, James S Duncan

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Machine learning in clinical neuroimaging : 6th international workshop, MLCN 2023, held in conjunction with MICCAI 2023, Vancouver, BC, Canada, October 8, 2023, proceedings. MLCN (Workshop) (6th : 2023 : Vancouver, B.C.), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from 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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Jiyao WangBiomedical Engineering, Yale University, New Haven, CT 06511, USA.
Nicha C DvornekBiomedical Engineering, Yale University, New Haven, CT 06511, USA.
Lawrence H StaibBiomedical Engineering, Yale University, New Haven, CT 06511, USA.
James S DuncanBiomedical Engineering, Yale University, New Haven, CT 06511, USA.

Funding

Subnetwork-based Quantitative Imaging Biomarkers for Therapy Assessment in AutismR01NS035193 · NINDS · YALE UNIVERSITY · PI JAMES S DUNCAN, LAWRENCE H. STAIB · 1996 to 2026
$9.2M
NINDS NIH HHS R01 NS035193
6 · The paper itself

Abstract

Insufficiency of training data is a persistent issue in medical image analysis, especially for task-based functional magnetic resonance images (fMRI) with spatio-temporal imaging data acquired using specific cognitive tasks. In this paper, we propose an approach for generating synthetic fMRI sequences that can then be used to create augmented training datasets in downstream learning tasks. To synthesize high-resolution task-specific fMRI, we adapt the

Indexed as

Data augmentationFunctional MRIImage synthesisMachine learningMedical imaging

Identifiers

PMID39281201
PMCPMC11395879

What OpenQuestion holds

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Registered trials

None linked

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.