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ICAM-Reg: Interpretable Classification and Regression With Feature Attribution for Mapping Neurological Phenotypes in Individual Scans

IEEE Transactions on Medical Imaging(2022)10.1109/tmi.2022.3221890Source: DataRank Database

ICAM-Reg: Interpretable Classification and Regression With Feature Attribution for Mapping Neurological Phenotypes in Individual Scans is a research paper published in IEEE Transactions on Medical Imaging (2022). On theSindex it has a DataRank of 0.546. It has been cited 37 times.

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0.546DataRank · unranked

Scored on demand from live citation data

Open Access37 citations

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›Methodology & internals

datarank_citation_only_1hop_v6· scope data_onlyMethodology

Pipeline:MetadataData-paper checkEnrichmentCitation networkScoring

Enrichment:Funding (13 grants)Impact (FWCI 3.50)OA: hybridTopics (45)IDs (PubMed, OpenAlex)SDGs (2)

FAIR Checklist

Context only (not used in score)

Findable (1/2)

Has DOI

Accessible (1/2)

Open Access

Interoperable (0/2)

Reusable (0/3)

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DataRank Breakdown

Base Score 100%Citation Network 0%

Base Score Contribution

0.546

From this paper's citation signal

Citation Network Contribution

Citation network not refreshed for this result

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Authors (11)

Mariana da Silva ORCID,Carole H. Sudre ORCID,Logan Z. J. Williams ORCID,Helena S. Sousa,Petru-Daniel Tudosiu ORCID

Live enrichment

PubMed:36374873 OpenAlex:https://openalex.org/W3133653501

Alzheimer’s Disease Neuroimaging Initiative (ADNI), National Institutes of Health

Grant: U01 AG024904

Department of Defense (DOD), ADNI

Grant: W81XWH-12-2-0012

Academy of Medical Sciences/the British Heart Foundation/the Government Department of Business, Energy and Industrial Strategy/the Wellcome Trust Springboard

Grant: SBF003/1116

Wellcome Collaborative

Grant: 215573/Z/19/Z

Engineering and Physical Sciences Research Council (EPSRC) Centre for Doctoral Training in Smart Medical Imaging

Grant: EP/S022104/1

EPSRC Centre for Doctoral Training in Smart Medical Imaging at King's College London and Imperial College London

EPSRC Doctoral Training Programme

Grant: EP/R513064/1

DTP 2018-19 Kings College London

Wellcome Trust

Grant: 215573

Integrative imaging of brain structure and function in populations and individuals

National Institutes of Health

Grant: 1U01AG024904-01

Alzheimers Disease Neuroimaging Initiative

Medical Research Council

Grant: MC_QA137853

Medical Research Council

Grant: MC_PC_17228

NIMH NIH HHS

Grant: R01 MH060974

British Heart Foundation

Wellcome Trust

FWCI

3.50

Citation Percentile

0.9%

Citation Trend

2021

2022

2023

2024

2025

2026

hybridLicense: cc-by

journalhttps://ieeexplore.ieee.org/ielx7/42/10091712/09947339.pdf

publisherhttps://ieeexplore.ieee.org/ielx7/42/10091712/09947339.pdf

publisherhttp://xplorestaging.ieee.org/ielx7/42/10091712/09947339.pdf?arnumber=9947339

journalhttps://doi.org/10.1109/tmi.2022.3221890

repositoryhttps://pubmed.ncbi.nlm.nih.gov/36374873

Fields of Study

Explainable Artificial Intelligence (XAI)Machine Learning in HealthcareCell Image Analysis Techniques02 engineering and technology0202 electrical engineering, electronic engineering, information engineeringHumansNeuroimagingBrainRadionuclide ImagingConnectome

MeSH Terms

BrainHumansRadionuclide ImagingNeuroimagingConnectome

Keywords

Artificial intelligencePattern recognition (psychology)Feature (linguistics)Feature extractionRegressionAttributionComputer sciencePhenotypeNeuroimagingStatisticsMathematicsNeurosciencePsychologyBiologyGeneticsGeneBrain ImagingDeep generative modelsBrainDiseasesAlzheimer's diseaseArticleImagingFeature AttributionBiomedical imagingConnectomeTrainingHumansImage-to-image translationRadionuclide Imaging

Sustainable Development Goals

SDG 3: 3. Good healthSDG 0: Reduced inequalities