Bin Zhou, MS
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Research
Publications
2026
Burnout in sickle cell disease–focused hematology-oncology–trained physicians: a national cross-sectional study
Restrepo-Espinosa V, Marshall A, Feder K, Afranie-Sakyi J, Carrithers B, Jones-Wonni B, Mensah C, Zhou B, Mistry R, McGann P, Azar S, De Castro L, Calhoun C, Boozang K, Brown T, Lee A, Van Doren L. Burnout in sickle cell disease–focused hematology-oncology–trained physicians: a national cross-sectional study. Blood Advances 2026, 10: 3653-3663. PMID: 41817318, PMCID: PMC13207547, DOI: 10.1182/bloodadvances.2025018338.Peer-Reviewed Original ResearchThis study investigates burnout in U.S. hematology-oncology physicians specializing in sickle cell disease, showing higher burnout rates linked to systemic factors, reduced recreation, and lower job pride.Dual-Domain Multipath Self-Supervised Diffusion Model for Accelerated MRI Reconstruction
Zhang Y, Hao J, Zhou B. Dual-Domain Multipath Self-Supervised Diffusion Model for Accelerated MRI Reconstruction. IEEE Transactions On Neural Networks And Learning Systems 2026, PP: 1-13. PMID: 42113659, DOI: 10.1109/tnnls.2026.3688183.Peer-Reviewed Original ResearchDual domainSelf-supervised baselinesHybrid attention networkAccelerated MRI reconstructionMagnetic resonance image reconstructionModel training schemeHigher acceleration factorsSource codeDeep learningMRI reconstructionReconstruction errorModel trainingReconstruction accuracyInference strategyTraining schemeComputational costAttention networkMRI datasetsFavorable performanceExplainabilityAcceleration factorAcquisition timeTrainingDatasetNetworkGeneration of brain PET synaptic density imaging from MRI and FDG-PET using a 3D Multi-stage Residual U-Net
Zheng X, Worhunsky P, Toyonaga T, Liu Q, Guo X, Zhou Y, Chen X, Zhou B, Mecca A, Chen M, O'Dell R, Van Dyck C, Carson R, Radhakrishnan R, Liu C. Generation of brain PET synaptic density imaging from MRI and FDG-PET using a 3D Multi-stage Residual U-Net. IEEE Transactions On Radiation And Plasma Medical Sciences 2026, PP: 1-1. DOI: 10.1109/trpms.2026.3690934.Peer-Reviewed Original ResearchFedSKD: Aggregation-Free Model-Heterogeneous Federated Learning via Multidimensional Similarity Knowledge Distillation for Medical Image Classification
Weng Z, Cai W, Zhou B. FedSKD: Aggregation-Free Model-Heterogeneous Federated Learning via Multidimensional Similarity Knowledge Distillation for Medical Image Classification. IEEE Transactions On Neural Networks And Learning Systems 2026, PP: 1-15. PMID: 42019059, DOI: 10.1109/tnnls.2026.3684321.Peer-Reviewed Original ResearchMedical image classificationFederated LearningKnowledge distillationImage classificationCurrent peer-to-peerCollaborative model trainingHeterogeneous model architecturesCentralized aggregatorState-of-the-artSkin lesion classificationModel driftPeer-to-peerFL baselinesFL frameworkCatastrophic forgettingDistribution alignmentServer-dependentModel architectureModel trainingHeterogeneous architecturesData sharingRound-robinIdentical architectureLesion classificationRobust solutionSparser2Sparse: Single-shot Sparser-to-Sparse Learning for Spatial Transcriptomics Imputation with Natural Image Co-learning
Fang Y, Qian J, Wang X, Cooper L, Zhou B. Sparser2Sparse: Single-shot Sparser-to-Sparse Learning for Spatial Transcriptomics Imputation with Natural Image Co-learning. IEEE Journal Of Biomedical And Health Informatics 2026, PP: 1-14. PMID: 41915529, DOI: 10.1109/jbhi.2026.3679252.Peer-Reviewed Original ResearchNatural imagesState-of-the-art approachesState-of-the-artCo-trainingSparse inputData fidelityImputation networkCo-learningHigh-resolution gene expression profilesSparse regionsLearning strategiesSpatial transcriptomicsDiverse tissue typesGene expression profilesTranscriptome imputationImputation accuracyFrameworkLow costImagesBiomedical researchST datasetsDatasetExpression profilesIntrinsic spatial patternsNetworkDose-aware diffusion model for 3D PET image denoising: Multi-institutional validation with reader study and real low-dose data
Xie H, Gan W, Bayerlein R, Zhou B, Chen M, Kulon M, Boustani A, Ko K, Wang D, Spencer B, Ji W, Chen X, Liu Q, Guo X, Xia M, Zhou Y, Liu H, Guo L, An H, Kamilov U, Wang H, Li B, Rominger A, Shi K, Wang G, Badawi R, Liu C. Dose-aware diffusion model for 3D PET image denoising: Multi-institutional validation with reader study and real low-dose data. Medical Image Analysis 2026, 111: 104039. PMID: 41930496, PMCID: PMC13332693, DOI: 10.1016/j.media.2026.104039.Peer-Reviewed Original ResearchPET image denoisingImage denoisingDeep learningLow-dose PETState-of-the-art generative modelsImage qualityMedical imaging tasksState-of-the-artLow-dose PET imagesFull-dose imagesDenoised imageSegmentation networkImage noise levelLow-dose dataLow-dose scansGenerative modelTraining modelLow-dose levelsImaging tasksNoise levelPET imagingHigh-quality samplesDenoisingDiffusion networksBoard-certified nuclear medicine physiciansHiFusion: Hierarchical Intra-Spot Alignment and Regional Context Fusion for Spatial Gene Expression Prediction from Histopathology
Weng Z, Fang Y, Qian J, Wang X, Cooper L, Cai W, Zhou B. HiFusion: Hierarchical Intra-Spot Alignment and Regional Context Fusion for Spatial Gene Expression Prediction from Histopathology. Proceedings Of The AAAI Conference On Artificial Intelligence 2026, 40: 10630-10637. DOI: 10.1609/aaai.v40i13.38036.Peer-Reviewed Original ResearchGene expression predictionState-of-the-art performanceWhole-slide imagesState-of-the-artDeep learning frameworkGene expressionExpression predictionSpatial transcriptomicsContext fusionCross-attentionFusion moduleAlignment lossSemantic consistencyLearning frameworkScalable solutionContextual informationRepresentational capacityAdoption barriersMicroenvironmental cuesST datasetsCross-validationMorphological representationH&E-stained whole-slide imagesExpressionComputational methodsAMA-SAM: Adversarial multi-Domain alignment of segment anything model for high-Fidelity histology nuclei segmentation
Qian J, Fang Y, Hao J, Zhou B. AMA-SAM: Adversarial multi-Domain alignment of segment anything model for high-Fidelity histology nuclei segmentation. Medical Image Analysis 2026, 111: 104011. PMID: 41812368, DOI: 10.1016/j.media.2026.104011.Peer-Reviewed Original ResearchDomain shiftDomain-invariant representation learningState-of-the-art approachesAccurate segmentationGradient reversal layerState-of-the-artMulti-domain settingLow resolution outputNuclei segmentation methodRepresentation learningAlignment moduleCell nuclei segmentation methodSegmentation mapDiverse domainsHistopathological imagesPerformance dropSegmentation methodAuxiliary datasetsReversal layerNuclei segmentationMultiple datasetsDatasetNegative transferSegmentation of cell nucleiPrimary datasetParametric Cardiac Imaging with 18F-Flutemetamol PET to Evaluate the Impact of Tafamidis in Patients with Transthyretin Cardiac Amyloidosis.
Liu Q, Shi T, Gravel P, Sharma A, De Freitas C, Fazzone-Chettiar R, Van Laere K, Baldick A, Kattan C, Guo X, Guo L, Xie H, Chen X, Zhou B, Liu Y, Carson R, Liu C, Miller E. Parametric Cardiac Imaging with 18F-Flutemetamol PET to Evaluate the Impact of Tafamidis in Patients with Transthyretin Cardiac Amyloidosis. Journal Of Nuclear Medicine 2026, 67: 780-787. PMID: 41748295, DOI: 10.2967/jnumed.125.270003.Peer-Reviewed Original ResearchThis study investigates dynamic 18F-flutemetamol PET imaging for measuring amyloid burden in transthyretin cardiac amyloidosis, showing significant reductions after 6 months of tafamidis treatment.DOSTA-Net: Domain-Shuffle Temporal Attention Network for Vessel Extraction in X-Ray Coronary Angiography Using Synthetic Data
Hao J, Cantrell D, Abdalla R, Ansari S, Zhou B. DOSTA-Net: Domain-Shuffle Temporal Attention Network for Vessel Extraction in X-Ray Coronary Angiography Using Synthetic Data. IEEE Transactions On Medical Imaging 2026, 45: 2614-2627. PMID: 41615973, DOI: 10.1109/tmi.2026.3659754.Peer-Reviewed Original ResearchTemporal attention networkState-of-the-art methodsLack of large-scale datasetsAttention networkDeep learning-based methodsTraining deep learning modelsUnlabeled real dataTemporal feature learningState-of-the-artImage quality assessmentVessel segmentation performanceLearning-based methodsLarge-scale datasetsHigh-quality annotationsDevelopment of deep learning-based methodsDeep learning modelsReal dataDomain gapPseudo-labelsDomain discrepancyFeature learningSynthetic dataHuman annotatorsSegmentation performanceLoss function
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