World's Best Scientists 2026 revealed!

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Computer Science

D-Index
83
Citations
20334
World Ranking
917
National Ranking
496

Overview

Steve B Jiang is affiliated with The University of Texas Southwestern Medical Center in the United States. Their research spans significant areas within medicine and physics, with a focus on radiology, radiation, pulmonary and respiratory medicine, biomedical engineering, and artificial intelligence.

Their primary fields of study include Medicine and Physics and Astronomy. Within these, subfields frequently addressed are Radiology, Nuclear Medicine and Imaging; Radiation; Pulmonary and Respiratory Medicine; Biomedical Engineering; and Artificial Intelligence.

Steve B Jiang's work encompasses various key topics including Advanced Radiotherapy Techniques, Medical Imaging Techniques and Applications, Radiomics and Machine Learning in Medical Imaging, Radiation Therapy and Dosimetry, Advanced X-ray and CT Imaging, Lung Cancer Diagnosis and Treatment, and Artificial Intelligence in Healthcare and Education.

The scientist has authored numerous publications in established venues. Frequent publication venues include:

  • arXiv (Cornell University)
  • Medical Physics
  • International Journal of Radiation Oncology*Biology*Physics
  • Physics in Medicine and Biology
  • Radiotherapy and Oncology

Frequent co-authors with whom Steve B Jiang has published multiple works include Dan Nguyen, Mu-Han Lin, Ti Bai, Michael Dohopolski, and Robert Timmerman.

Selected recent papers by Steve B Jiang demonstrate their focus on integrating deep learning and artificial intelligence models with medical imaging and radiotherapy:

  • ChatDoctor: A Medical Chat Model Fine-Tuned on a Large Language Model Meta-AI (LLaMA) Using Medical Domain Knowledge, 2023, Cureus
  • An introduction to deep learning in medical physics: advantages, potential, and challenges, 2020, Physics in Medicine and Biology
  • Operating a treatment planning system using a deep-reinforcement learning-based virtual treatment planner for prostate cancer intensity-modulated radiation therapy treatment planning, 2020, Medical Physics
  • Dose prediction with deep learning for prostate cancer radiation therapy: Model adaptation to different treatment planning practices, 2020, Radiotherapy and Oncology
  • A deep learning-based framework for segmenting invisible clinical target volumes with estimated uncertainties for post-operative prostate cancer radiotherapy, 2021, Medical Image Analysis

Best Publications

  • The management of imaging dose during image-guided radiotherapy: Report of the AAPM Task Group 75

    Martin J. Murphy;James Balter;Stephen Balter;Jose A. Bencomo

  • Effects of intra-fraction motion on IMRT dose delivery: statistical analysis and simulation.

    Thomas Bortfeld;Kimmo Jokivarsi;Michael Goitein;Jong Kung

  • Prediction of respiratory tumour motion for real-time image-guided radiotherapy

    Gregory C Sharp;Steve B Jiang;Shinichi Shimizu;Hiroki Shirato

  • Low-dose CT reconstruction via edge-preserving total variation regularization

    Zhen Tian;Xun Jia;Kehong Yuan;Tinsu Pan

  • Three-Dimensional Radiotherapy Dose Prediction on Head and Neck Cancer Patients with a Hierarchically Densely Connected U-net Deep Learning Architecture.

    Dan Nguyen;Xun Jia;David J. Sher;Mu-Han Lin

  • Generating synthesized computed tomography (CT) from cone-beam computed tomography (CBCT) using CycleGAN for adaptive radiation therapy.

    Xiao Liang;Liyuan Chen;Dan Nguyen;Zhiguo Zhou

  • Synchronized moving aperture radiation therapy (SMART): average tumour trajectory for lung patients.

    Toni Neicu;Hiroki Shirato;Yvette Seppenwoolde;Steve B Jiang

  • Implementation and evaluation of various demons deformable image registration algorithms on a GPU

    Xuejun Gu;Hubert Pan;Yun Liang;Richard Castillo

  • 3D radiotherapy dose prediction on head and neck cancer patients with a hierarchically densely connected U-net deep learning architecture.

    Dan Nguyen;Xun Jia;David Sher;Mu Han Lin

  • GPU-based fast cone beam CT reconstruction from undersampled and noisy projection data via total variation

    Xun Jia;Yifei Lou;Ruijiang Li;William Y. Song

  • GPU-based Low Dose CT Reconstruction via Edge-preserving Total Variation Regularization

    Zhen Tian;Xun Jia;Kehong Yuan;Tinsu Pan

  • Implementation and evaluation of various demons deformable image registration algorithms on GPU

    Xuejun Gu;Hubert Pan;Yun Liang;Richard Castillo

  • Internal-external correlation investigations of respiratory induced motion of lung tumors.

    Dan Ionascu;Steve B. Jiang;Seiko Nishioka;Hiroki Shirato

  • Monte Carlo modelling of electron beams from medical accelerators

    Chang Ming Ma;Steve B. Jiang

  • GPU-based iterative cone-beam CT reconstruction using tight frame regularization

    Xun Jia;Bin Dong;Yifei Lou;Steve B. Jiang

  • A Monte Carlo dose calculation tool for radiotherapy treatment planning.

    C. M. Ma;J. S. Li;J. S. Li;T. Pawlicki;Steve B Jiang

  • Radiotherapy of mobile tumors.

    Steve B. Jiang

  • GPU-based fast Monte Carlo simulation for radiotherapy dose calculation

    Xun Jia;Xuejun Gu;Yan Jiang Graves;Michael Folkerts

  • Monte Carlo verification of IMRT dose distributions from a commercial treatment planning optimization system

    C. M. Ma;T. Pawlicki;S. B. Jiang;J. S. Li

  • 4D-CT lung motion estimation with deformable registration: quantification of motion nonlinearity and hysteresis.

    Vlad Boldea;Gregory C. Sharp;Steve B. Jiang;David Sarrut

  • Integrated radiotherapy imaging system (IRIS): design considerations of tumour tracking with linac gantry-mounted diagnostic x-ray systems with flat-panel detectors

    Ross I Berbeco;Steve B Jiang;Gregory C Sharp;George T Y Chen

Frequent Co-Authors

Gregory C. Sharp
Gregory C. Sharp Harvard University
Yifei Lou
Yifei Lou University of North Carolina at Chapel Hill
H. Edwin Romeijn
H. Edwin Romeijn Georgia Institute of Technology
Wotao Yin
Wotao Yin Alibaba Group (China)
Jennifer G. Dy
Jennifer G. Dy Northeastern University
Changzhi Li
Changzhi Li Texas Tech University

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