Professor

Contact InformationHamid Tizhoosh

Phone: 519-888-4567 x46751
Location: E7 6314

Website

Biography Summary

Dr. Hamid R. Tizhoosh is a Professor in the Faculty of Engineering at the University of Waterloo since 2001 where he leads the KIMIA Lab (Laboratory for Knowledge Inference in Medical Image Analysis). Before he joined the University of Waterloo, he was a research associate at the Knowledge and Intelligence Systems Laboratory at the University of Toronto where he worked on AI methods such as reinforcement learning. His research activities encompass artificial intelligence, computer vision, and medical imaging. He has developed algorithms for medical image filtering, segmentation, and search. As well, he has introduced the concept of "Opposition-based Learning". Dr. Tizhoosh has received more than $6.0 M in funding since 2001 for his research and commercialization activities through NSERC, OCE, FedDev, MITACS, MaRS, HTX, IRAP, ORF-RE and industry partners. He is the author of two books, 14 book chapters, more than 150 journal and conference papers, and multiple patents. He has more than three decades of industry experience and has worked with numerous companies such as Management of Intelligent Technologies GmbH (Aachen, Germany), Image Processing Systems Inc. (Photon Dynamics Inc. San Jose, CA), Semacode (Waterloo, ON), and Medipattern Corporation (Toronto). Additionally, Dr. Tizhoosh has more than 10 years of experience in commercialization and start-ups. In 2007, he started Segasist Technologies, a start-up that developed image segmentation software for radiation oncology. The company raised more than $2M under his leadership, both as CEO and CTO. He also planned and successfully managed an FDA 510k submission for computer-aided segmentation for prostate cancer with the help of Sunnybrook Hospital and the London Regional Cancer Centre. Presently, he is the AI Advisor of Huron Digital Pathology, St. Jacobs, ON, Canada. As well, he is a faculty affiliate to the Vector Institute, Toronto, where he leads a pathfinder project on using AI in radiology.

Research Interests

  • Artificial Intelligence
  • Medical Imaging
  • Computer Vision
  • Biomedical

Education

  • 2000, Doctorate, Technical Computer Science - Medical Imaging, University of Magdeburg
  • 1996, Master's, Electrical Engineering - Technical Computer Science, University of Technology Aachen

Courses*

  • SYDE 201 - Seminar
    • Taught in 2017
  • SYDE 223 - Data Structures and Algorithms
    • Taught in 2016, 2017, 2018
  • SYDE 522 - Machine Intelligence
    • Taught in 2016, 2017, 2018, 2019
  • SYDE 677 - Medical Imaging
    • Taught in 2015, 2016, 2017, 2018, 2019
  • SYDE 720 - Selected Topics in Computation
    • Taught in 2019
* Only courses taught in the past 5 years are displayed.

Selected/Recent Publications

  • Pan-cancer diagnostic consensus through searching archival histopathology images using artificial intelligence Nature npj Digital Medicine, 2020 Kalra, Shivam; Tizhoosh, HR; Shah, Sultaan; Choi, Charles; Damaskinos, Savvas; Safarpoor, Amir; Shafiei, Sobhan; Babaie, Morteza; Diamandis, Phedias; Campbell, Clinton JV (Accepted in 2020)
  • Heterogeneity-aware local binary patterns for retrieval of histopathology images Erfankhah, Hamed; Yazdi, Mehran; Babaie, Morteza; Tizhoosh, Hamid R; IEEE Access, 2019 (Accepted in 2019)
  • Artificial Intelligence and Digital Pathology: Challenges and Opportunities Tizhoosh, HR; Pantanowitz, L; Journal of Pathology Informatics, 2018 (Accepted in 2018)
  • Representing medical images with encoded local projections Tizhoosh, Hamid R; Babaie, Morteza; IEEE Transactions on Biomedical Engineering, 2018 (Accepted in 2018)
  • Sze-To, Antonio and Tizhoosh, Hamid R and Wong, Andrew KC, Binary Codes for Tagging X-Ray Images via Deep De-Noising Autoencoders, arXiv preprint arXiv:1604.07060, 2016
  • Liu, Xinran and Tizhoosh, Hamid R and Kofman, Jonathan, Generating Binary Tags for Fast Medical Image Retrieval Based on Convolutional Nets and Radon Transform, arXiv preprint arXiv:1604.04676, 2016
  • Zhu, Shujin and Tizhoosh, HR, Radon Features and Barcodes for Medical Image Retrieval via SVM, arXiv preprint arXiv:1604.04675, 2016
  • Mahootchi, M and Ponnambalam, K and Tizhoosh, HR, Response to the comments by Joodavi, A., and Mozafari, M. on “Operations optimization of multireservoir systems using storage moments equations” by M. Mahootchi, K. Ponnambalam, HR Tizhoosh [Adv. Water Resour. 33 (2010) 1150--1163], Advances in Water Resources, 2016
  • Tizhoosh, Hamid R and Gangeh, Mehrdad J and Tadayyon, Hadi and Czarnota, Gregory J, Tumour ROI Estimation in Ultrasound Images via Radon Barcodes in Patients with Locally Advanced Breast Cancer, arXiv preprint arXiv:1602.02586, 2016
  • Khalvati, Farzad and Salmanpour, Aryan and Rahnamayan, Shahryar and Haider, Masoom A and Tizhoosh, HR, Sequential Registration-Based Segmentation of the Prostate Gland in MR Image Volumes, Journal of digital imaging, 29(2), 2016, 254 - 263
  • Al-Qunaieer, Fares and Tizhoosh, Hamid R and Rahnamayan, Shahryar, Automated Resolution Selection for Image Segmentation, arXiv preprint arXiv:1605.06820, 2016
  • Nouredanesh, Mina and Tizhoosh, Hamid R and Banijamali, Ershad, Gabor Barcodes for Medical Image Retrieval, arXiv preprint arXiv:1605.04478, 2016
  • Tizhoosh, Hamid R and Rahnamayan, Shahryar, Evolutionary Projection Selection for Radon Barcodes, arXiv preprint arXiv:1604.04673, 2016
  • Salehinejad, Hojjat and Rahnamayan, Shahryar and Tizhoosh, Hamid R, Micro-Differential Evolution: Diversity Enhancement and Comparative Study, arXiv preprint arXiv:1512.07980, 2015
  • Projectron-A Shallow and Interpretable Network for Classifying Medical Images Sriram, Aditya; Kalra, Shivam; Tizhoosh, Hamid R; International Joint Conference on Neural Networks (IJCNN), 2019 (Accepted in 2019)
  • Deep Features for Tissue-Fold Detection in Histopathology Images Babaie, Morteza; Tizhoosh, Hamid R; European Congress on Digital Pathology, 2019 (Accepted in 2019)
  • Convolutional neural networks for histopathology image classification: Training vs. using pre-trained networks Kieffer, Brady; Babaie, Morteza; Kalra, Shivam; Tizhoosh, Hamid R; Seventh International Conference on Image Processing Theory, Tools and Applications (IPTA), 2017 (Accepted in 2017)
  • Barcode annotations for medical image retrieval: A preliminary investigation Tizhoosh, Hamid R; IEEE International Conference on Image Processing (ICIP), 2015 (Accepted in 2015)
  • Opposition-based learning: a new scheme for machine intelligence Tizhoosh, Hamid R; International Conference on Computational Intelligence for Modelling, Control and Automation (CIMCA), 2005 (Accepted in 2005)

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