At Intelligent Medical Image Computing Systems (IMICS) Lab at The Hospital for Sick Children and University of Toronto we investigate and develop Artificial Intelligence solutions for precision medicine using medical imaging. Our goal is to design and develop AI-based diagnostic and prognostic tools to improve the quality of care for patients.
Dr. Farzad Khalvati, Principal Investigator: Farzad Khalvati, PhD, is a Scientist and Endowed Chair in Medical Imaging and Artificial Intelligence at The Hospital for Sick Children (SickKids) and University of Toronto. He is an Associate Professor in the Department of Medical Imaging and Institute of Medical Science, with cross appointments to the Department of Computer Science and Department of Mechanical and Industrial Engineering at the University of Toronto. He is also a Faculty Affiliate at Vector Institute. He is actively engaged in bridging the gap between AI researchers and clinicians to design and implement AI solutions that can be integrated into clinical workflows with a tangible impact on the quality of patient care. One important aspect of Dr. Khalvati’s research is to design and implement joint clinician-AI decision making solutions, which will significantly advance our understanding of how AI algorithms findings can be translated from mere information to practical meaning, and how that meaning can be properly communicated to clinicians in order to significantly improve the accuracy of final diagnoses, prognoses, and treatment planning with a direct impact on the quality of care given to patients.
Dr. Khalvati has published over 125 peer-reviewed publications as journal papers (45), patents (3), book chapter (1), and conference full papers (39) and abstracts (39). He has also co-authored a book. He has received ~$2M in peer-reviewed research grants (~$1.2M as PI). Dr. Khalvati has supervised over 50 trainees as Primary Supervisor at different levels, including 32 undergraduate students, 13 graduate students, 2 postdoctoral fellows, and 4 Research Associates.
Dr. Khalvati received his PhD in Electrical and Computer Engineering from University of Waterloo and before joining University of Toronto, he worked as an imaging and AI scientist in biomedical industry and then as a Postdoctoral Research Associate at Sunnybrook Research Institute. Dr. Khalvati is a recipient of several awards from NSERC, OCE, and CIHR and his dissertation was selected and patented by the University of Waterloo Commercialization Office.
Dr. Khalvati’s research is funded by:
- Canadian Cancer Society
- Brain Canada Foundation
- AMS Healthcare
- Chair in Medical Imaging and Artificial Intelligence, a joint Hospital-University Chair between the University of Toronto, The Hospital for Sick Children, and the SickKids Foundation
- Huawei Technologies Canada
- Astellas Pharma
- Cancer Care Ontario
- Department of Medical Imaging, University of Toronto
Ernest (Khashayar) Namdar, PhD candidate, Institute of Medical Science, University of Toronto
Sara Ketabi, PhD Candidate (with Prof. Greg A. Jamieson), Department of Mechanical and Industrial Engineering, University of Toronto
Kareem Kudus (with Dr. Birgit Ertl-Wagner), PhD Candidate, Institute of Medical Science, University of Toronto
Hamed Zakeri, MASc Candidate, Department of Mechanical and Industrial Engineering, University of Toronto
Sajith Rajapaska, MSc Candidate, Institute of Medical Science, University of Toronto
Thesis: Weakly Supervised Perturbation Based Method for 3D Brain Tumour Segmentation
Simon (Meng) Zhou, MSc Candidate, Department of Computer Science, University of Toronto
Jay Yoo, MSc Candidate, Institute of Medical Science, University of Toronto
Yujie Wu, MEng Student, Department of Mechanical and Industrial Engineering, University of Toronto
Vicky Chan, MEng Student, Department of Mechanical and Industrial Engineering, University of Toronto
Chen Zhao, MEng Student, Department of Mechanical and Industrial Engineering, University of Toronto
Yann Shaw, MEng Student, Department of Mechanical and Industrial Engineering, University of Toronto
Katarina Chiam, Undergraduate Thesis Student, Engineering Science, University of Toronto
Arsh Kadakia, Undergraduate Thesis Student, Engineering Science, University of Toronto
Capstone Projects, Department of Mechanical and Industrial Engineering
3D Slicer brain tumor segmentation add-on. Nada Al Aker, Justin Semelhago, Michael Simone, Rishik Kumar, 2022-2023.
Zilun Zhang (2021-2022) MEng
MEng Project: Introducing Vision Transformer for Alzheimer’s disease classification task with 3D input.
Yue Tong Leung (2021-2022) MEng (Now Full-Stack Developer at Continual Energy Inc.)
MEng Project: Exploring COVID-19–related stressors: Topic modeling study
Partoo Vafaeikia (2020-2022) MSc (Now Data Scientist at RBC)
Thesis: Deep Learning Methods for Pediatric Brain Tumour Diagnosis
Saman Motamed (2019-2021) MSc (Now Machine Learning Researcher at Carnegie Mellon University)
Thesis: A Semi-supervised Pipeline for Detection of Anomalies in Medical Images
Ruqian Hao (2019-2020) Visiting PhD Scholar (Now PhD Candidate at School of Optoelectronic Science and Engineering, University of Electronic Science and Technology of China, Chengdu, Sichuan, China)
- A Transfer Learning Based Active Learning Framework for Brain Tumor Classification
- A Comprehensive Study of Data Augmentation Strategies for Prostate Cancer Detection in Diffusion Weighted MRI using Convolutional Neural Networks
Yucheng Zhang (with Dr. Masoom Haider) (2017-2019), MSc, (Now Medical Student at University of Toronto)
Thesis: Deep Radiomics Analytics Pipeline for Prognosis of Pancreatic Ductal Adenocarcinoma
Junjie Zhang (with Dr. Masoom Haider) (2015-2017), Postdoctoral Fellow (Now Research Data Scientist at RBC Capital Markets)
Capstone Projects, Department of Mechanical and Industrial Engineering
- Artificial Intelligence-based pediatric-specific solution for touchless interaction using voice control during image guided surgeries. Christina Seo, Bayaan Shalaby, Sheree Zhang, Litong Zheng, 2020-2021
- A front-end graphical user interface module design and implementation for Radiomics analytics pipeline in medical imaging using PyQt. Ines Gomes, Samuel McCulloch, Gabriella Tolnai, Lora Tyufekchieva, 2020-2021.
Undergraduate Research Assistants and Thesis Students
Justin Xu (2022). BSc Thesis, Engineering Science, University of Toronto. Thesis: Radiomics and residual neural networks for pretherapeutic MRI Differentiation of BRAF status in pediatric low-grade gliomas.
Haiyue (Kevin) Jin (2021-2022). Engineering Science, University of Toronto. Thesis: An educational graphical user interface to construct convolutional neural networks for teaching artificial intelligence in radiology.
Akino Watanabe (2021-2022). Engineering Science, University of Toronto. Thesis: Improving disease classification performance and explainability of deep learning models in Radiology with heatmap generators.
Martyn Wei (2021-2022). Engineering Science, University of Toronto. Thesis: Investigating visual explanations of CNN models for chest X-rays.
Pranav Agnihotri (2021-2022). Engineering Science, University of Toronto. Using multi-modal data for improving generalizability and explainability of disease classification in Radiology
Chaojun (Vicky) Chang (2021-2022). Engineering Science, University of Toronto. Division of Engineering Science, University of Toronto. Scoliosis assessment: Automating cobb angle measurement using segmentation method.
Kevin Wang (2020-2021), BSc Thesis, Engineering Science, University of Toronto. Thesis: Variational Autoencoders for Dimension Reduction of Radiomic Data (Now MSE student at Johns Hopkins University)
Julia Le (2018) (Now MSc student at University of Toronto)
Saman Motamed (2018-2019) (Now Machine Learning Researcher at Carnegie Mellon University)
Jacob Yoo (2017, 2019) (Now MSc student at York University)
Yucheng Zhang (2015-2017) (Now Medical Student at University of Toronto)
Tyler Clark (2016-2017)
Harry Kim (2018)
Jeffrey Francis (2017)
Dylan Clark (2017)
Steve Ming (2015)
Derek Jedral (2015)
Nilaan Gunabalachandran (2015)
IMICS Lab is located on the 8th floor at Peter Gilgan Centre for Research and Learning (PGCRL) building on SickKids campus: 686 Bay St, Toronto, ON M5G 0A4.
Email: farzad dot khalvati at utoronto dot ca