2023
Speech Audio Synthesis from Tagged MRI and Non-negative Matrix Factorization via Plastic Transformer
Liu X, Xing F, Stone M, Zhuo J, Fels S, Prince J, El Fakhri G, Woo J. Speech Audio Synthesis from Tagged MRI and Non-negative Matrix Factorization via Plastic Transformer. Lecture Notes In Computer Science 2023, 14226: 435-445. PMID: 38651032, PMCID: PMC11034915, DOI: 10.1007/978-3-031-43990-2_41.Peer-Reviewed Original ResearchWeight mapAudio waveformEnd-to-end deep learning frameworkMatrix factorization-based approachesFactorization-based approachDeep learning frameworkNon-negative matrix factorizationEnd-to-endAdversarial trainingProcess of speech productionTwo-dimensional spectrogramConventional convolutionLearning frameworkMotion featuresTraining samplesAudio synthesisDimension expansionMatrix inputMatrix factorizationTagged MRISpeech productionTransformation modelExperimental resultsSpectrogramPlastic transformation
2022
Tagged-MRI Sequence to Audio Synthesis via Self Residual Attention Guided Heterogeneous Translator
Liu X, Xing F, Prince J, Zhuo J, Stone M, El Fakhri G, Woo J. Tagged-MRI Sequence to Audio Synthesis via Self Residual Attention Guided Heterogeneous Translator. Lecture Notes In Computer Science 2022, 13436: 376-386. PMID: 36820764, PMCID: PMC9942274, DOI: 10.1007/978-3-031-16446-0_36.Peer-Reviewed Original ResearchAudio waveformEnd-to-end deep learning frameworkAdversarial training approachDeep learning frameworkEnd-to-endTwo-dimensional spectrogramAdversarial networkIntermediate representationLearning frameworkResidual attentionDisentanglement strategyAudio synthesisDataset sizeImprove realismHeterogeneous representationsHeterogeneous translationAttentional strategiesTraining approachExperimental resultsMuscle deformationIntelligible speechMotor control theoriesTagged-MRIRelated-disordersSpeech acoustics
2020
High-performance rapid MR parameter mapping using model-based deep adversarial learning
Liu F, Kijowski R, Feng L, El Fakhri G. High-performance rapid MR parameter mapping using model-based deep adversarial learning. Magnetic Resonance Imaging 2020, 74: 152-160. PMID: 32980503, PMCID: PMC7669737, DOI: 10.1016/j.mri.2020.09.021.Peer-Reviewed Original ResearchConceptsConvolutional neural networkMR parameter mappingAdversarial learningState-of-the-art reconstruction methodsEnd-to-end convolutional neural networkUndersampled k-space dataConvolutional neural network approachAdversarial learning approachState-of-the-artStructural similarity indexImage reconstruction frameworkEnd-to-endImage sharpnessData consistencyConventional reconstruction approachesReconstruction approachK-space dataImprove image sharpnessImage reconstruction approachEstimated parameter mapsImage sparsityTexture restorationNetwork trainingImage datasetsReconstruction performance
2018
End-to-End Lung Nodule Detection in Computed Tomography
Wu D, Kim K, Dong B, Fakhri G, Li Q. End-to-End Lung Nodule Detection in Computed Tomography. Lecture Notes In Computer Science 2018, 11046: 37-45. DOI: 10.1007/978-3-030-00919-9_5.Peer-Reviewed Original ResearchDeep reconstruction networkLung nodule detectionReconstruction networkEnd-to-end detectorMedical imagesLung Image Database Consortium image collectionNodule detectionEfficient network trainingReconstructed imagesConvolutional neural networkEnd-to-endSuperior detection performanceRaw dataComputer visionCAD systemCNN detectorNetwork trainingImage collectionNeural networkDetection performanceImage spaceDetection taskDetection systemModern medical imagingFanbeam projections