H.R. Tizhoosh
University of Waterloo

Vision : Images : Brain


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The 2017 IEEE Symposium Series on Computational Intelligence

IEEE SSCI 2017 will be held in Honolulu, Hawaii, USA from Nov. 27 to Dec 1, 2017.




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:: University of Waterloo

:: KIMIA Lab

:: Centre for Bioengineering and Biotechnology (CBB)


  Home Research Teaching KIMIA Lab  

Basic research is a major driving force in our society. Scientific observation and problem solving, as striking aspects of human consciousness, are crucial for progress and overcoming challenges. Looking at problems and attempting to find a solution is a very thrilling part of working at a university.

Hamid Tizhoosh

Selected Recent Publications (2015-2016)

  • arXiv:1604.07060
    Binary Codes for Tagging X-Ray Images via Deep De-Noising Autoencoders
    Antonio Sze-To, Hamid R. Tizhoosh, Andrew K.C. Wong

  • arXiv:1604.04678
    Anatomy-Aware Measurement of Segmentation Accuracy
    Hamid R. Tizhoosh, Ahmed A. Othman

  • arXiv:1604.04676
    Generating Binary Tags for Fast Medical Image Retrieval Based on Convolutional Nets and Radon Transform
    Xinran Liu, Hamid R. Tizhoosh, Jonathan Kofman

  • arXiv:1604.04675
    Radon Features and Barcodes for Medical Image Retrieval via SVM
    Shujin Zhu, H.R.Tizhoosh

  • arXiv:1604.04673
    Evolutionary Projection Selection for Radon Barcodes
    Hamid R. Tizhoosh, Shahryar Rahnamayan

  • arXiv:1602.02586
    Tumour ROI Estimation in Ultrasound Images via Radon Barcodes in Patients with Locally Advanced Breast Cancer
    Hamid R. Tizhoosh, Mehrdad J. Gangeh, Hadi Tadayyon, Gregory J. Czarnota

  • arXiv:1505.05212
    Barcode Annotations for Medical Image Retrieval: A Preliminary Investigation
    Hamid R. Tizhoosh

  • arXiv:1504.06266
    Evolving Fuzzy Image Segmentation with Self-Configuration
    Ahmed Othman, Hamid R. Tizhoosh, Farzad Khalvati

  • arXiv:1504.05619
    Learning Opposites with Evolving Rules
    Hamid R. Tizhoosh, Shahryar Rahnamayan


Talk: BIG Data, Medical Imaging and Machine Intelligence

With big data we mean any enormous and multifaceted collection of data that cannot be analyzed by ordinary computing devices and algorithms. Big data, due to their sheer volume and inherent variety, are extremely challenging to manage and hence difficult to understand. 

:: Read more

Big Data Medical Imaging Talk


© H.R. Tizhoosh, Faculty of Engineering, University of Waterloo, Canada, 2001-2017