Bin Zhou, MS

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 Research

  This study investigates burnout in U.S. hematology-oncology physicians specializing in sickle cell disease, showing higher burnout rates linked to institutional and workplace challenges, reduced recreation, and lower job pride.

  Full Summary

• 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 Research

• Generation 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 Research

• FedSKD: 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 Research

  Citations

• Sparser2Sparse: 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 Research

• Dose-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 Research

  Citations

• HiFusion: 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 Research

  Citations

• AMA-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 Research

  Citations

• Parametric 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 Research

  This study investigates dynamic 18F-flutemetamol PET imaging for measuring amyloid burden in transthyretin cardiac amyloidosis, showing significant reductions after 6 months of tafamidis treatment.

  Full Summary

• 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 Research