Xiaofeng Liu
Assistant Professor of Radiology and Biomedical Imaging and of Biomedical Informatics and Data ScienceDownloadHi-Res Photo
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Assistant Professor of Radiology and Biomedical Imaging and of Biomedical Informatics and Data Science
Biography
Dr. Liu joined Yale on 03/2024 as an Assistant Professor in traditional tenure track, and an Associate Member at the Broad Institute of MIT and Harvard (forward related email to: liuxiaof@broadinstitute.org). He is also an affiliate faculty member of the Center for Biomedical Data Science (CBDS), Yale Institutes for Foundations of Data Science (FDS), and Global Health (YIGH).
Prior to this, he was an Assistant Professor (2023-24), Instructor (2021-23), and Post/Pre-Doctorial Fellow (2019-21) at Harvard Medical School (MGH/BIDMC).
His research interests are centered around the convergence of trustworthy AI/deep learning, medical imaging, and data science to advance the diagnosis, prognosis, and treatment monitoring of various diseases.
Appointments
Radiology & Biomedical Imaging
Assistant ProfessorPrimaryBiomedical Informatics & Data Science
Assistant ProfessorSecondary
Other Departments & Organizations
- Bioimaging Sciences
- Biomedical Informatics & Data Science
- Center for Biomedical Data Science
- Center for Brain & Mind Health
- Computational Biology and Biomedical Informatics
- Magnetic Resonance Research Center
- Positron Emission Tomography (PET)
- Radiology & Biomedical Imaging
- Xiaofeng Liu Lab
- Yale Combined Program in the Biological and Biomedical Sciences (BBS)
- Yale Institute for Global Health
Education & Training
- Assistant Professor
- Harvard Medical School (2024)
- Affiliate Faculty
- Harvard Data Science Initiative, Kempner Institute for Natural and Artificial Intelligence, Dana-Farber/Harvard Cancer Center . (2024)
- Research Staff
- Massachusetts General Hospital (2024)
- Instructor
- Harvard Medical School (2023)
- Post-doctoral Research Fellow (Radiology)
- Massachusetts General Hospital/Harvard Medical School (2021)
- Post-doctoral Research Fellow (Neurology)
- Beth Israel Deaconess Medical Center/Harvard Medical School (2020)
- PhD
- University of Chinese Academy of Sciences, Mechatronics
- Pre-doctoral Research Fellow
- Beth Israel Deaconess Medical Center (2019)
- Non Degree Program
- Carnegie Mellon University, Electrical and Computer Engineering (2018)
- BA
- University of Science and Technology of China, Communication (2014)
- BE
- University of Science and Technology of China, Automation [Wang-Daheng Elite Class] (2014)
- Non Degree Program
- University of Science and Technology of China, Life Sciences/Biology (2011)
Research
Overview
Medical Subject Headings (MeSH)
Artificial Intelligence; Deep Learning; Image Processing, Computer-Assisted; Information Science; Neural Networks, Computer
ORCID
0000-0002-4514-2016- View Lab Website
XLiu Lab
Research at a Glance
Yale Co-Authors
Frequent collaborators of Xiaofeng Liu's published research.
Research Interests
Research topics Xiaofeng Liu is interested in exploring.
Georges El Fakhri, PhD, DABR
Jinsong Ouyang, PhD
Thibault Marin, PhD
Chao Ma, PhD
Ruth Lim, MD
Maeva Dhaynaut
Neural Networks, Computer
Image Processing, Computer-Assisted
Deep Learning
Publications
Featured Publications
Mutual Information Regularized Feature-Level Frankenstein for Discriminative Recognition.
Liu X, Yang C, You J, Kuo CJ, Kumar BVKV. Mutual Information Regularized Feature-Level Frankenstein for Discriminative Recognition. IEEE Trans Pattern Anal Mach Intell 2022, 44: 5243-5260. PMID: 33945470, DOI: 10.1109/TPAMI.2021.3077397.Peer-Reviewed Original ResearchOrdinal Unsupervised Domain Adaptation With Recursively Conditional Gaussian Imposed Variational Disentanglement.
Liu X, Li S, Ge Y, Ye P, You J, Lu J. Ordinal Unsupervised Domain Adaptation With Recursively Conditional Gaussian Imposed Variational Disentanglement. IEEE Trans Pattern Anal Mach Intell 2022, PP PMID: 35704544, DOI: 10.1109/TPAMI.2022.3183115.Peer-Reviewed Original ResearchSubtype-Aware Dynamic Unsupervised Domain Adaptation
Liu X, Xing F, You J, Lu J, Kuo C, Fakhri G, Woo J. Subtype-Aware Dynamic Unsupervised Domain Adaptation. IEEE Transactions On Neural Networks And Learning Systems 2024, 35: 2820-2834. PMID: 35895653, DOI: 10.1109/tnnls.2022.3192315.Peer-Reviewed Original ResearchCitationsConceptsTarget domainSource domain to target domainUnsupervised domain adaptationWithin-class compactnessHeart disease dataPseudo-labelsDomain adaptationClass centersLatent spaceCluster centroidsConditional alignmentLabel shiftTransfer knowledgeQueueing frameworkLocal proximityAlternative processing schemesSubtype labelsExperimental resultsProcessing schemeSubtype structureDomainNetVisDADisease dataDomainLabelingAttentive continuous generative self-training for unsupervised domain adaptive medical image translation
Liu X, Prince J, Xing F, Zhuo J, Reese T, Stone M, El Fakhri G, Woo J. Attentive continuous generative self-training for unsupervised domain adaptive medical image translation. Medical Image Analysis 2023, 88: 102851. PMID: 37329854, PMCID: PMC10527936, DOI: 10.1016/j.media.2023.102851.Peer-Reviewed Original ResearchCitationsMeSH Keywords and ConceptsConceptsUnsupervised domain adaptationImage translationProblem of domain shiftSelf-trainingImage modality translationLabeled source domainTarget domain dataSelf-attention schemeAlternating optimization schemeHeterogeneous target domainContinuous value predictionPseudo-labelsDomain adaptationUDA methodsDomain shiftSoftmax probabilitiesSource domainTarget domainVariational BayesBackground regionsTranslation tasksTraining processDomain dataGeneration taskOptimization schemeMemory consistent unsupervised off-the-shelf model adaptation for source-relaxed medical image segmentation
Liu X, Xing F, El Fakhri G, Woo J. Memory consistent unsupervised off-the-shelf model adaptation for source-relaxed medical image segmentation. Medical Image Analysis 2022, 83: 102641. PMID: 36265264, PMCID: PMC10016738, DOI: 10.1016/j.media.2022.102641.Peer-Reviewed Original ResearchCitationsMeSH Keywords and ConceptsConceptsUnsupervised domain adaptationUnsupervised domain adaptation methodsSource domain dataBN statisticsTarget domainLabeled source domain dataDomain dataLabeled source domainSelf-training strategyPatient data privacyHeterogeneous target domainBrain tumor segmentationPseudo-labelsDomain adaptationUnsupervised adaptationData privacySegmentation taskSource domainImage segmentationVital protocolAdaptation frameworkDecay strategyBoost performanceModel adaptationTumor segmentationAssessment of Transcatheter or Surgical Closure of Atrial Septal Defect using Interpretable Deep Keypoint Stadiometry.
Wang J, Xie W, Cheng M, Wu Q, Wang F, Li P, Fan B, Zhang X, Wang B, Liu X. Assessment of Transcatheter or Surgical Closure of Atrial Septal Defect using Interpretable Deep Keypoint Stadiometry. Research (Wash D C) 2022, 2022: 9790653. PMID: 36340508, DOI: 10.34133/2022/9790653.Peer-Reviewed Original ResearchVicinal Feature Statistics Augmentation for Federated 3D Medical Volume Segmentation
Huang Y, Xie W, Li M, Cheng M, Wu J, Wang W, You J, Liu X. Vicinal Feature Statistics Augmentation for Federated 3D Medical Volume Segmentation. InInternational Conference on Information Processing in Medical Imaging 2023 Jun 8 (pp. 360-371). Cham: Springer Nature Switzerland.Peer-Reviewed Original ResearchAUTO3D: Novel view synthesis through unsupervisely learned variational viewpoint and global 3D representation
Liu X, Che T, Lu Y, Yang C, Li S, You J. Auto3d: Novel view synthesis through unsupervisely learned variational viewpoint and global 3d representation. InEuropean Conference on Computer Vision 2020 Aug 23 (pp. 52-71). Cham: Springer International Publishing.Peer-Reviewed Original ResearchSeverity-aware semantic segmentation with reinforced wasserstein training
Liu X, Ji W, You J, Fakhri GE, Woo J. Severity-aware semantic segmentation with reinforced wasserstein training. InProceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition 2020 (pp. 12566-12575).Peer-Reviewed Original ResearchImportance-Aware Semantic Segmentation in Self-Driving with Discrete Wasserstein Training
Liu X, Han Y, Bai S, Ge Y, Wang T, Han X, Li S, You J, Lu J. Importance-aware semantic segmentation in self-driving with discrete wasserstein training. InProceedings of the AAAI Conference on Artificial Intelligence 2020 Apr 3 (Vol. 34, No. 07, pp. 11629-11636).Peer-Reviewed Original Research
Academic Achievements & Community Involvement
activity IEEE Transactions on Neural Networks and Learning Systems (TNNLS)
Journal ServiceAssociate EditorDetails2023 - Presentactivity Journal of Medical Internet Research (JMIR)
Journal ServiceAssociate EditorDetails2024 - Presenthonor Trailblazer R21 Award
National AwardNIBIB/NIHDetails05/23/2024United Stateshonor Google Cloud Research Credits
National AwardGoogleDetails05/01/2024United Stateshonor National Artificial Intelligence Research Resource Pilot Award
National AwardU.S. National Science Foundation (NSF)Details04/26/2024
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Academic Office
100 Church Street South, Wing E, Rm 169
New Haven, CT 06519