​Welcome!

I am currently a Research Associate with Department of Radiology in JHU, dedicated to empower accurate medical diagnosis using advanced AI technologies to assist radiologists. I obtained my Ph.D. in CS at JHU, before which I spent one wonderful year in UCLA and four memorable years in HUST.

My research interests are in Medical Image Analysis.

zhuotun(at)gmail(dot)com
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Curriculum Vitae

Recent News

Aug. 2021. One paper got accepted to ICCV workshop on CVAMD. 
May. 2021. Two abstract papers got accepted to the ASTRO 2021.
Mar. 2021. I began my new position as a  Research Associate  with Department of Radiology in JHMI.
Dec. 2020. I passed my PhD dissertation. Thanks all people I met along this journey!!!
Sep. 2020. I was granted the MICCAI 2020 NIH Awards. See you virtually in the online MICCAI conference this year!
Aug. 2020. One abstract paper on the lymph node detection got accepted to the RSNA 2020.
May. 2020. Two co-author papers on the lymph node detection got early accepted to the MICCAI 2020.
Feb. 2020. One co-author paper on Head-and-Neck Cancer got accepted to the CVPR 2020.

Educations

Sep. 2016 ~ Mar. 2021                         Ph.D. in Computer Science
                                                              Johns Hopkins University (JHU), Baltimore, MD, USA
                                                              Adviser: Prof. Alan L. Yuille
                                                              Bloomberg Distinguished Professor 

Sep. 2015 ~ Sep. 2016                         M.S. in Statistics
                                                              University of California, Los Angeles (UCLA), CA, USA
                                                              Adviser: Prof. Alan L. Yuille
                                                              Thesis: ImageNet Classification with Complementary Networks (PDF)
​
Sep. 2011 ~ Jun. 2015                         B.Eng. in Electronics and Information Engineering.
                                                             Huazhong Univ. of Sci. & Tech. (HUST), Wuhan, P.R.China
                                                             Adviser: Prof. Xiang Bai and Prof. Xinggang Wang
                                                             Thesis: 3D Shape Recognition via Deep Learning. (PDF in Chinese)

Internships

Jun. 2019 ~ May. 2020                       Very worth working with Dr. Le Lu and Dr. Dakai Jin ​on the challenging lymph
Bethesda, Maryland                           node segmentation and detection yet critical medical problem at PAII.

Jun. 2018 ~ Aug. 2018                       Glad to work with Dr. Daguang Xu and Dr. Dong Yang on the medical image Bethesda, Maryland                           segmentation at NVIDIA.

Jun. 2016 ~ Aug. 2016                       Fortunate to work with Dr. Waoyuan Wang@MSR on Blur Estimation, Microsoft Research, Redmond          where I met so many nice and professional people, as well as learned a lot                                                              ​    about the Caffe framework.

Research

Preprints:​

  1. Zhuotun Zhu, Ke Yan*, Dakai Jin*, Jinzheng Cai, Tsung-Ying Ho, Adam P Harrison, Dazhou Guo, Chun Hung Chao, Xianghua Ye, Jing Xiao, Alan Yuille, Le Lu. Detecting Scatteredly-Distributed, Small, and Critically Important Objects in 3D Oncology Imaging via Decision Stratification. In submission. ​​
Publications:
  1. Zhuotun Zhu, Yongyi Lu, Wei Shen, Elliot Fishman, Alan Yuille. Segmentation for Classification of Screening Pancreatic Neuroendocrine Tumors. In ICCV Workshop on CVAMD 2021. ​​(Arxiv)
  2. Zhuotun Zhu, Dakai Jin, Ke Yan, Tsung-Ying Ho, Xianghua Ye, Dazhou Guo, Chun Hung Chao, Jing Xiao, Alan Yuille, Le Lu. Lymph Node Gross Tumor Volume Detection and Segmentation via Distance-based Gating using 3D CT/PET Imaging in Radiotherapy. Early Accepted to MICCAI 2020. (Arxiv)(Poster)(Video)(MICCAI 2020 NIH Award)
  3. Chun Hung Chao, Zhuotun Zhu*, Dazhou Guo*, Ke Yan*, Tsung-Ying Ho, Jinzheng Cai, Adam Harrison, Xianghua Ye, Jing Xiao, Alan Yuille, Min Sun, Le Lu, Dakai Jin. Lymph Node Gross Tumor Volume Detection in Oncology Imaging via Relationship Learning Using Graph Neural Network. Early Accepted to MICCAI 2020.
  4. Dazhou Guo, Dakai Jin, Zhuotun Zhu, Tsung-Ying Ho, Adam Harrison, Chun-Hung Chao, Jing Xiao, Alan L. Yuille, Chen-kan Tseng, Le Lu. Organ at Risk Segmentation for Head and Neck Cancer using Stratified Learning and Neural Architecture Search. CVPR 2020, Seattle.​​
  5. Zhuotun Zhu, Chenxi Liu, Dong Yang, Alan L. Yuille, Daguang Xu. V-NAS: Neural Architecture Search for Volumetric Medical Image Segmentation. 3DV 2019, Quebec City. (ArXiv)(Poster)(Spotlight Video)
  6. ​​Zhuotun Zhu*, Yingda Xia*, Lingxi Xie, Elliot K. Fishman, Alan L. Yuille. Multi-Scale Coarse-to-Fine Segmentation for Screening Pancreatic Ductal Adenocarcinoma. MICCAI 2019, Shenzhen. (PDF)(Poster)(Early Acceptance)
  7. ​Zhuotun Zhu, Yingda Xia, Wei Shen, Elliot K. Fishman, Alan L. Yuille. A 3D Coarse-to-Fine Framework for Volumetric Medical Image Segmentation. 3DV 2018, Verona. (PDF)(Slides)(Poster)(Spotlight Video)(Media)
  8. Yingda Xia, Lingxi Xie, Fengze Liu, Zhuotun Zhu, Elliot K. Fishman and Alan L. Yuille. Bridging the Gap Between 2D and 3D Organ Segmentation with Volumetric Fusion Net. MICCAI 2018, Granada. (PDF)
  9. Zhuotun Zhu, Lingxi Xie, Alan L. Yuille. Object Recognition with and without Objects. IJCAI 2017, Melbourne. (PDF)(Slides)(Poster)(CODE)
  10. Zhuotun Zhu, Xinggang Wang, Song Bai, Cong Yao, and Xiang Bai. Deep learning representation using autoencoder for 3D shape retrieval. Neurocomputing 204 (2016): 41-50. (PDF)
  11. Xinggang Wang*, Zhuotun Zhu*, Cong Yao, Xiang Bai. Relaxed Multiple-Instance SVM with Application to Object Discovery. ICCV 2015. (* equal contribution) (PDF)(CODE)
  12. Xiang Bai, Song Bai, Zhuotun Zhu, Longin Jan Latecki. 3D Shape Matching via Two Layer Coding. TPAMI, 37(12):2361–2373, 2015. (PDF)
  13. Zhuotun Zhu, Xinggang Wang, Song Bai, Cong Yao and Xiang Bai. Deep Learning Representation using Autoencoder for 3D Shape Retrieval. IEEE ICSPAC, 2014, Wuhan. (PDF) (Oral Presentation).

​Teaching

​EN.601.482/682: Machine Learning: Deep Learning                               2018 Fall
​EN.601.382: Deep Learning Lab                                                              2018 Fall

​ACADEMIC ACTIVITIES

Invited Reviewer for the following conferences and journals:
  • CVPR (2021, 2020, 2019), ECCV 2020, ICCV (2021, 2019), ICLR 2021, ICML (2021, 2020), MICCAI (2021, 2020, 2019),  AAAI (2021, 2020, 2017), WACV (2021, 2020)
  • TPAMI, IJCV, TMM, TMI, PR, PRL, Neurocomputing
Student volunteer for the IJCAI 2017.

Presentation

Aug 20. 2014                            "Deep learning representation using autoencoder for 3D shape retrieval"
                                                  Beijing, Microsoft Research Asia (MSRA)

Selected Awards

Excellent Graduate Award                                         Jun. 2015
Young Microsoft Fellowship(1%)                               Jun.  2014
The MediaTek Scholarship(1%)                                Apr. 2014
Outstanding Undergraduate Award(2%)                   Nov. 2013
National Scholarship(1.5%)                                       Sep. 2013 & Sep. 2012
Last updated on  Oct. 2020