Fuyao Chen
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About
Biography
I am an MD-PhD student focusing on medical image analysis. My interests encompass developing and applying cutting-edge technological tools to enhance disease diagnosis, treatment planning, and patient outcomes. I am inspired by interdisciplinary collaborations that drive translational development and bring innovation into real-world clinical settings. Additionally, I am passionate about leveraging advanced technologies in medical education and address implementation challenges in underserved and under-resourced healthcare environments.
Last Updated on May 29, 2025.
Education & Training
- BE
- Vanderbilt University, Biomedical Engineering (2017)
Research
Overview
Medical Research Interests
Biomedical Engineering
ORCID
0000-0002-9609-5731
Publications
2025
Conditional Convolution of Clinical Data Embeddings for Multimodal Prostate Cancer Classification
Zhong J, Chen F, Chen L, Shung D, Onofrey J. Conditional Convolution of Clinical Data Embeddings for Multimodal Prostate Cancer Classification. 2025, 00: 1-5. DOI: 10.1109/isbi60581.2025.10981307.Peer-Reviewed Original ResearchConceptsConvolutional neural networkGleason scoreProstate cancerClinical dataMultiparametric magnetic resonance imagingPredicting Gleason scoreClinical informationCurrent deep learning approachesPatient clinical dataMagnetic resonance imagingDeep learning approachNon-invasive diagnosisAccurate risk predictionData embeddingCNN kernelsMRI scansConditional convolutionPublic datasetsResonance imagingNeural networkProstate cancer classificationData modalitiesLearning approachBaseline modelGS prediction accuracyComparative Performance of Machine Learning Models in Reducing Unnecessary Targeted Prostate Biopsies
Chen F, Esmaili R, Khajir G, Zeevi T, Gross M, Leapman M, Sprenkle P, Justice A, Arora S, Weinreb J, Spektor M, Huber S, Humphrey P, Levi A, Staib L, Venkataraman R, Martin D, Onofrey J. Comparative Performance of Machine Learning Models in Reducing Unnecessary Targeted Prostate Biopsies. European Urology Oncology 2025 PMID: 39924390, PMCID: PMC12332026, DOI: 10.1016/j.euo.2025.01.005.Peer-Reviewed Original ResearchCitationsAltmetricConceptsProstate cancerPrediction of clinically significant prostate cancerClinical dataProstate-specific antigen levelClinically Significant Prostate CancerProstate magnetic resonance imagingSeverity of prostate cancerCombination of clinical featuresPrediction of csPCaSignificant prostate cancerProstate Imaging-ReportingCore needle biopsyRetrospective analysis of dataDecision curve analysisReducing unnecessary biopsiesProstate cancer diagnosisReceiver operating characteristic curveArea under the receiver operating characteristic curveFalse-negative rateMagnetic resonance imagingPersonalized risk assessmentAntigen levelsNeedle biopsyPatient ageUnnecessary biopsies
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 ResearchCitationsConceptsDeep 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, PMCID: PMC11755669, 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, PMCID: PMC11737863, 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 Research
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The Anlyan Center
300 Cedar Street
New Haven, CT 06519