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Scale-Aware Transformers for Diagnosing Melanocytic Lesions

IEEE Access(2021)10.1109/access.2021.3132958Source: DataRank Database

Scale-Aware Transformers for Diagnosing Melanocytic Lesions is a research paper published in IEEE Access (2021). On theSindex it has a DataRank of 0. It has been cited 35 times.

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0DataRank · unranked

Scored on demand from live citation data

Open Access35 citations

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›Methodology & internals

datarank_citation_only_1hop_v6· scope data_onlyMethodology

Pipeline:MetadataData-paper checkEnrichmentCitation networkScoring

Enrichment:Funding (7 grants)Impact (FWCI 1.95)OA: goldTopics (46)IDs (PubMed, PMC, 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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Authors (8)

Sachin Mehta ORCID,Shima Nofallah,Stevan R. Knezevich,Caitlin May ORCID,Oliver Chang ORCID

Live enrichment

PubMed:35211363 PMC:PMC8865389 OpenAlex:https://openalex.org/W4206671419

National Cancer Institute of the National Institutes of Health

Grant: R01CA200690

National Cancer Institute of the National Institutes of Health

Grant: U01CA231782

Melanoma Research Alliance

Grant: 622600

U.S. Department of Defense

Grant: W81XWH-20-1-0798

National Institutes of Health

Grant: 1R01CA151306-01A1

Accuracy in the Diagnosis of Melanoma & the Impact of Double Reading

National Institutes of Health

Grant: 1U01CA231782-01

A Unified Machine Learning Package for Cancer Diagnosis

National Institutes of Health

Grant: 5R01CA200690-02

Improving Melanoma Pathology Accuracy through Computer Vision Techniques - the IMPACT Study

FWCI

1.95

Citation Percentile

0.9%

Citation Trend

2022

2023

2024

2025

2026

goldLicense: cc-by

journalhttps://doi.org/10.1109/access.2021.3132958

publisherhttps://doi.org/10.1109/access.2021.3132958

publisherhttps://ieeexplore.ieee.org/ielam/6287639/9312710/9638602-aam.pdf

publisherhttp://xplorestaging.ieee.org/ielx7/6287639/9312710/09638602.pdf?arnumber=9638602

repositoryhttps://pubmed.ncbi.nlm.nih.gov/35211363

Fields of Study

Cutaneous Melanoma Detection and ManagementAI in cancer detectionDigital Imaging for Blood Diseases02 engineering and technology03 medical and health sciences0302 clinical medicine0202 electrical engineering, electronic engineering, information engineering

Keywords

Computer scienceArtificial intelligenceInferenceMargin (machine learning)Pattern recognition (psychology)Context (archaeology)Digital pathologyComputer visionMachine learningMelanomaMulti-scaleConvolutional Neural NetworkTransformersSkin Cancer DiagnosisHistopathological ImagesMelanocytic Risk LesionsWhole-Slide Image ClassificationTechnologyBiopsy61008 Information and Computing Sciences (for)Article46 Information and Computing Sciences (for-2020)EngineeringInformation and Computing Sciences4611 Machine Learning (for-2020)CancerSkinImage segmentationBioengineering (rcdc)40 Engineering (for-2020)004TK1-99714.2 Evaluation of markers and technologies (hrcs-rac)09 Engineering (for)Feature extractionConvolutional neural networksElectrical engineering. Electronics. Nuclear engineering10 Technology (for)

Sustainable Development Goals

SDG 3: 3. Good healthSDG 0: Reduced inequalities