Evidence map›Paper›PMID 39247791›Full record

ArticleMedical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention2023

MRIS: A Multi-modal Retrieval Approach for Image Synthesis on Diverse Modalities.

Boqi Chen, Marc Niethammer

Abstract read
In one paragraph

Article in Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

2 authors.

Boqi ChenDepartment of Computer Science, University of North Carolina at Chapel Hill.
Marc NiethammerDepartment of Computer Science, University of North Carolina at Chapel Hill.

Funding

Large-scale automatic analysis of the OAI magnetic resonance image datasetR01AR072013 · NIAMS · UNIV OF NORTH CAROLINA CHAPEL HILL · PI NIETHAMMER, MARC · 2017 to 2022
$2.2M
A comprehensive imaging genetics framework for osteoarthritis researchR01AR082684 · NIAMS · UNIV OF NORTH CAROLINA CHAPEL HILL · PI NIETHAMMER, MARC, ZHU, HONGTU · 2024 to 2025
$1.4M
NIAMS NIH HHS R01 AR072013NIAMS NIH HHS R01 AR082684
6 · The paper itself

Abstract

Multiple imaging modalities are often used for disease diagnosis, prediction, or population-based analyses. However, not all modalities might be available due to cost, different study designs, or changes in imaging technology. If the differences between the types of imaging are small, data harmonization approaches can be used; for larger changes, direct image synthesis approaches have been explored. In this paper, we develop an approach based on multi-modal metric learning to synthesize images of diverse modalities. We use metric learning via multi-modal image retrieval, resulting in embeddings that can relate images of different modalities. Given a large image database, the learned image embeddings allow us to use k-nearest neighbor (

Identifiers

PMID39247791
PMCPMC11378323

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Registered trials

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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.