2023
Disentangled Representations in Local-Global Contexts for Arabic Dialect Identification
Alhakeem Z, Jang S, Kang H. Disentangled Representations in Local-Global Contexts for Arabic Dialect Identification. IEEE/ACM Transactions On Audio Speech And Language Processing 2023, 32: 879-890. DOI: 10.1109/taslp.2023.3341006.Peer-Reviewed Original ResearchArabic dialect identificationDisentanglement networkLow-resolution feature mapsIntra-class variationsInter-class variationsDialect identificationEnhanced identification performanceDisentangled representationsFeature representationFeature mapsBottleneck TransformerLatent spaceLocal-global contextClustering lossLocal informationSource utteranceBackbone moduleIdentification performanceIrrelevant informationCompetitive solutionNetworkLocal-globalRepresentationQuantitative evaluationDisentanglementAttenuation correction for PET imaging using conditional denoising diffusion probabilistic model
Dong Y, Jang S, Han P, Johnson K, Ma C, Fakhri G, Li Q, Gong K. Attenuation correction for PET imaging using conditional denoising diffusion probabilistic model. 2023, 00: 1-1. DOI: 10.1109/nssmicrtsd49126.2023.10338188.Peer-Reviewed Original ResearchDiffusion probabilistic modelGenerative adversarial networkConditional encodingAttenuation correctionDenoising diffusion probabilistic modelLow-level featuresProbabilistic modelAttenuation coefficientAdversarial networkExtract featuresPET/MR systemsEncodingPET acquisitionNovel methodDiffusion encodingMagnetic resonanceImagesPET imagingCorrectionMR imagingUNetAttenuationNetworkFeaturesResonance
2022
Investigation of Network Architecture for Multimodal Head-and-Neck Tumor Segmentation
Li Y, Chen J, Jang S, Gong K, Li Q. Investigation of Network Architecture for Multimodal Head-and-Neck Tumor Segmentation. 2022, 00: 1-3. DOI: 10.1109/nss/mic44845.2022.10399293.Peer-Reviewed Original ResearchNetwork architectureTransformer-based network architectureModel long-range dependenciesTransformer-based networkNatural language processingU-Net architectureMedical imaging communitySuccess of transformationVision transformerComputer visionSegmentation networkComputational resourcesLanguage processingLong range dependenceTraining datasetTumor segmentationMedical tasksNnU-NetImaging communityArchitectureNetworkTaskVisionSegmentsDatasetA Noise-Level-Aware Framework for PET Image Denoising
Li Y, Cui J, Chen J, Zeng G, Wollenweber S, Jansen F, Jang S, Kim K, Gong K, Li Q. A Noise-Level-Aware Framework for PET Image Denoising. Lecture Notes In Computer Science 2022, 13587: 75-83. DOI: 10.1007/978-3-031-17247-2_8.Peer-Reviewed Original ResearchDeep convolutional neural networkPET image denoisingImage denoisingConvolutional neural networkDenoising frameworkDenoising operationBaseline methodsDenoising needsLocal noise levelBackbone networkPatient PET imagesNeural networkDenoisingNoise levelScanner sensitivityPET/CT systemNetworkPET imagingNoise-levelEmbeddingImage acquisition durationAcquisition durationAdministered activityImagesNoise