Fuyao Chen
DownloadHi-Res Photo
About
Titles
MD-PhD Student, Biomedical Engineering
Biography
Born and raised in Shanghai, China, I came to the United States at the age of nineteen and attended Vanderbilt University, where I majored in Biomedical Engineering. In my spare time, I enjoy learning different languages and traditional Chinese art forms, such as Beijing Opera and crosstalk.
Fun fact about me: I joined the marching band in my college with no prior knowledge of football. It was a wild experience but a lot of fun!
Education & Training
- BE
- Vanderbilt University, Biomedical Engineering (2017)
Research
Overview
Medical Subject Headings (MeSH)
Biomedical Engineering
ORCID
0000-0002-9609-5731
Research at a Glance
Yale Co-Authors
Frequent collaborators of Fuyao Chen's published research.
Publications Timeline
A big-picture view of Fuyao Chen's research output by year.
John Onofrey, PhD
Mika Naganawa, PhD
Tianyi Zeng, PhD
Takuya Toyonaga, MD, PhD
24Publications
99Citations
Publications
2023
Fast Reconstruction Enhances Deep Learning PET Head Motion Correction
Zeng T, Chen F, Zhang J, Lieffrig E, Cai Z, Naganawa M, You C, Lu Y, Onofrey J. Fast Reconstruction Enhances Deep Learning PET Head Motion Correction. 2023, 00: 1-1. DOI: 10.1109/nssmicrtsd49126.2023.10338189.Peer-Reviewed Original ResearchTeacher’s PET: Semi-supervised Deep Learning for PET Head Motion Correction
Zeng T, You C, Cai Z, Lieffrig E, Zhang J, Chen F, Lu Y, Onofrey J. Teacher’s PET: Semi-supervised Deep Learning for PET Head Motion Correction. 2023, 00: 1-1. DOI: 10.1109/nssmicrtsd49126.2023.10337834.Peer-Reviewed Original ResearchConceptsMotion tracking methodHead motion correctionMotion trackingExtra hardwareMotion estimatesTracking methodSemi-supervised deep learningSupervised deep learning methodsQuality training dataDeep learning methodsMean teacher modelSemi-supervised mannerMotion correctionMotion detectionHead motionCorrection networkDeep learningInaccurate quantitative resultsTraining dataLearning methodsBetter generalizationMotionLow resolutionCorrection resultsPerformanceCross-Attention for Improved Motion Correction in Brain PET
Cai Z, Zeng T, Lieffrig E, Zhang J, Chen F, Toyonaga T, You C, Xin J, Zheng N, Lu Y, Duncan J, Onofrey J. Cross-Attention for Improved Motion Correction in Brain PET. Lecture Notes In Computer Science 2023, 14312: 34-45. PMID: 38174216, PMCID: PMC10758996, DOI: 10.1007/978-3-031-44858-4_4.Peer-Reviewed Original ResearchConceptsDeep learning networkCross-attention mechanismDeep learning benchmarksMotion correctionTraining data domainPET list-mode dataPET image reconstructionQuality of reconstructionData domainCross attentionLearning networkSupervised mannerLearning benchmarksReference imageMotion trackingInherent informationList-mode dataImage reconstructionBrain PET dataPrediction resultsDifferent scannersHead motionImproved motion correctionNetworkSpatial correspondenceFast Reconstruction for Deep Learning PET Head Motion Correction
Zeng T, Zhang J, Lieffrig E, Cai Z, Chen F, You C, Naganawa M, Lu Y, Onofrey J. Fast Reconstruction for Deep Learning PET Head Motion Correction. Lecture Notes In Computer Science 2023, 14229: 710-719. PMID: 38174207, PMCID: PMC10758999, DOI: 10.1007/978-3-031-43999-5_67.Peer-Reviewed Original ResearchCitationsInterrater reproducibility of the Myoton and durometer devices to quantify sclerotic chronic graft-versus-host disease
Ghosh S, Baker L, Chen F, Khera Z, Vain A, Zhang K, Hood A, Smith H, Chen H, Jagasia M, Tkaczyk E. Interrater reproducibility of the Myoton and durometer devices to quantify sclerotic chronic graft-versus-host disease. Archives Of Dermatological Research 2023, 315: 2545-2554. PMID: 37227518, DOI: 10.1007/s00403-023-02626-1.Peer-Reviewed Original ResearchCitationsMeSH Keywords and ConceptsConceptsSkin sclerosisChronic graftHost diseaseAnatomic sitesAllogeneic hematopoietic stem cell transplantationHematopoietic stem cell transplantationSclerotic chronic graftLong-term survivorsStem cell transplantationConfidence intervalsCGVHD treatmentSclerotic cGvHDSevere complicationsSkin scoreCurrent gold standardCell transplantationTherapeutic responseClinical reproducibilityCGVHDDorsal forearmMyotonSclerosisVolar forearmHealthy participantsMean pairwise differenceHyperspectral imaging to accurately segment skin erythema and hyperpigmentation in cutaneous chronic graft‐versus‐host disease
Saknite I, Kwun S, Zhang K, Hood A, Chen F, Kangas L, Kortteisto P, Kukkonen A, Spigulis J, Tkaczyk E. Hyperspectral imaging to accurately segment skin erythema and hyperpigmentation in cutaneous chronic graft‐versus‐host disease. Journal Of Biophotonics 2023, 16: e202300009. PMID: 36942511, DOI: 10.1002/jbio.202300009.Peer-Reviewed Original ResearchCitationsAltmetricMeSH Keywords and ConceptsEnhanced characterization of breast cancer phenotypes using Raman micro-spectroscopy on stainless steel substrate
Thomas G, Fitzgerald S, Gautam R, Chen F, Haugen E, Rasiah P, Adams W, Mahadevan-Jansen A. Enhanced characterization of breast cancer phenotypes using Raman micro-spectroscopy on stainless steel substrate. Analytical Methods 2023, 15: 1188-1205. PMID: 36799369, DOI: 10.1039/d2ay01764d.Peer-Reviewed Original ResearchCitationsMeSH Keywords and ConceptsConceptsStainless steel substratesStainless steelSteel substrateRaman spectroscopySteelRaman signalRaman signal intensityCalcium fluoride substratesLaser powerFluoride substratesSignal qualityFundamental understandingStrong Raman signalNoise ratioSubstrateCost-effective alternativeEnhanced characterizationRaman spectraSignals
2022
805 Segmentation of cutaneous chronic graft-versus-host disease by a deep learning neural network
McNeil A, Parks K, Liu X, Saknite I, Chen F, Reasat T, Wheless L, Dawant B, Tkaczyk E. 805 Segmentation of cutaneous chronic graft-versus-host disease by a deep learning neural network. Journal Of Investigative Dermatology 2022, 142: s140. DOI: 10.1016/j.jid.2022.05.819.Peer-Reviewed Original ResearchArtificial intelligence recognition of cutaneous chronic graft‐versus‐host disease by a deep learning neural network
McNeil A, Parks K, Liu X, Saknite I, Chen F, Reasat T, Cronin A, Wheless L, Dawant B, Tkaczyk E. Artificial intelligence recognition of cutaneous chronic graft‐versus‐host disease by a deep learning neural network. British Journal Of Haematology 2022, 197: e69-e72. PMID: 35322873, PMCID: PMC9197858, DOI: 10.1111/bjh.18141.Peer-Reviewed Original ResearchCitationsAltmetricMeSH Keywords and Concepts
2021
Optimal Biomechanical Parameters for Measuring Sclerotic Chronic Graft-Versus-Host Disease
Baker L, Chen F, Cronin A, Chen H, Vain A, Jagasia M, Tkaczyk E. Optimal Biomechanical Parameters for Measuring Sclerotic Chronic Graft-Versus-Host Disease. JID Innovations 2021, 1: 100037. PMID: 34790906, PMCID: PMC8594905, DOI: 10.1016/j.xjidi.2021.100037.Peer-Reviewed Original ResearchCitationsAltmetricConceptsCGVHD patientsBiomechanical parametersChronic Graft-VersusSclerotic chronic graftHost disease patientsBackward stepwise selectionChronic graftGraft-VersusSclerotic cGvHDHost diseaseUnivariable analysisTransplant controlsDisease patientsAnatomic sitesPatientsDisease severityLogistic regressionSclerotic diseaseBiomechanical studyDiscriminatory abilityLASSO regressionCharacteristic curveMyotonometerStepwise selectionDisease
Links & Media
Get In Touch
Contacts
Email
Locations
N207
Lab
The Anlyan Center
300 Cedar Street
New Haven, CT 06519