Zongyuan Ge

Dr

Accepting PhD Students

20142020

Research output per year

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Personal profile

Biography

ZongYuan Ge, is currently a Senior Research Fellow and Adjunct Senior Lecturer at Monash Faculty of Engineering and also serve as a Deep Learning Specialist at NVIDIA AI Technology Centre. He is also the group leader of Monash Medical AI Group (www.mmai.group).

Before he joined Monash Zongyuan was a research scientist at IBM Research Australia doing research in medical AI during 2016-2018.

He has been awarded science accomplishment award and manager choice of the year inside IBM for his excellent contributions to those projects. In 2017, Zongyuan was selected as one of the 200 Most Qualified Young Researchers in Computer and Mathematics by the Scientific Committee of the Heidelberg Laureate Forum Foundation in 2017.

He has a strong background in statistical analysis, machine learning and computer vision research. So far, he has published more than 30 peer-reviewed publications and patents. He has led and contributed to six international projects in the areas of dermatology, ophthalmology and radiology with major industry companies like IBM Watson Health, medical AI unicorn startup Airdoc and medical service provider Molemap. These findings have been translated into health products and services that foster effective infectious diseases interventions in Asia-Pacific. 

Formerly Zongyuan was a PhD candidate with Australian Centre for Robotics Vision at the Queensland University of Tech and worked with Prof. Peter Corke, Dr Chris McCool and Dr Conrad Sanderson. He has enthusiasm for AI, computer vision, medical image, robotics and deep learning research.

Two PhD scholarships are available in Zongyuan's medical AI team in 2019, please contact him if you are interested in computer vision, machine learning or medical AI. 

Community service

Professional Memberships and Service

  • Transactions on Medical Imaging (TMI)
  • Computer Vision and Pattern Recognition (CVPR)
  • Medical Imaging Computing and Computer-Assisted Intervention (MICCAI)
  • IEEE International Conference on Robotics and Automation (ICRA)
  • International Joint Conference on Artificial Intelligence (IJCAI)
  • IEEE Winter Conference on Applications of Computer Vision (WACV)
  • IEEE International Symposium on Biomedical Imaging (ISBI)
  • International Conference on Digital Image Computing: Techniques and Applications (DICTA)
  • IEEE Transactions on Image Processing (TIP)
  • IEEE Transaction on Multimedia (ToM)
  • ELSEVIER Computers & Electrical Engineering
  • International Journal of Applied Mathematics and Computer Science (AMCS)
  • IBM Journal of Research and Development
  • Journal of Biomedical and Health Informatics

Consulting

Zongyuan’s dermatology work is a joint collaboration between industry company Molemap Ltd and research institute. This work has been awarded for IBM Scientific Research Accomplishment Award and IBM Manager Choice Award. These findings have pushed forward the AI implementation in the field of skin cancer screening service and generated over one million revenue from the product. This research led to 4 technical PIC conference papers (2 as lead-author), an abstract presented in Dermatology World Congress 2017, one clinical journal (under preparation for submission to JAMA Dermatology) and 2 patent filed.    
 
Zongyuans recent AI work on ophthalmology has lead a team of 12 research engineers and built a fundus based eye disease screening system. The system is able to provide diagnosis suggestion over more than 36 fundus diseases including diabetic retinopathy (DR), age-related macular degeneration and glaucoma. This system is trained over 5 million images labelled by over 100 ophthalmologists for 3 years. In 2018, this system has provided eye care service for more than 8 million people in major health checking institutes and major hospitals. 

Research area keywords

  • Deep Learning
  • Medical Imaging
  • Artificial Intelligence
  • Computer Vision
  • Robotics

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Projects

Research Output

Computational prediction and interpretation of both general and specific types of promoters in Escherichia coli by exploiting a stacked ensemble-learning framework

Li, F., Chen, J., Ge, Z., Wen, Y., Yue, Y., Hayashida, M., Baggag, A., Bensmail, H. & Song, J., 4 May 2020, (Accepted/In press) In : Briefings in Bioinformatics. 15 p., bbaa049.

Research output: Contribution to journalArticleResearchpeer-review

Epileptic Seizure Detection Using Convolutional Neural Network: A Multi-Biosignal study

Liu, Y., Sivathamboo, S., Goodin, P., Bonnington, P., Kwan, P., Kuhlmann, L., O'Brien, T., Perucca, P. & Ge, Z., 4 Feb 2020, Proceedings of the Australasian Computer Science Week Multiconference 2020, ACSW 2020. Forkan, A. (ed.). New York NY USA: Association for Computing Machinery (ACM), 8 p. 37

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearch

PeNGaRoo, a combined gradient boosting and ensemble learning framework for predicting non-classical secreted proteins

Zhang, Y., Yu, S., Xie, R., Li, J., Leier, A., Marquez-Lago, T. T., Akutsu, T., Smith, A. I., Ge, Z., Wang, J., Lithgow, T. & Song, J., 1 Feb 2020, In : Bioinformatics. 36, 3, p. 704-712 9 p.

Research output: Contribution to journalArticleResearchpeer-review

2 Citations (Scopus)

Training data independent image registration using generative adversarial networks and domain adaptation

Mahapatra, D. & Ge, Z., Apr 2020, In : Pattern Recognition. 100, 10 p., 107109.

Research output: Contribution to journalArticleResearchpeer-review

1 Citation (Scopus)

Adversarial discriminative sim-to-real transfer of visuo-motor policies

Zhang, F., Leitner, J., Ge, Z., Milford, M. & Corke, P., Sep 2019, In : International Journal of Robotics Research. 38, 10-11, p. 1229-1245 17 p.

Research output: Contribution to journalArticleResearchpeer-review

2 Citations (Scopus)