Medical Imaging Faculty Promotions Special

https://mailchi.mp/0450015915a2/vckmsppho8?e=8c3a95fdc4

Announcements

Faculty Promotions 

A Message from Dr. Alan Moody, Chair – Department of Medical Imaging

Dear Colleagues,

Please join me in congratulating our faculty members on their promotions that are richly deserved, and recognize their contributions to teaching, research and creative professional activity. All promotions are effective July 1, 2021.

Please click on the names below to review a brief bio of each promotee 

Eric Bartlett – Associate Professor
Kate Hanneman – Associate Professor
Farzad Khavati – Associate Professor
Anish Kirpalani – Associate Professor
Pejman Maralani – Associate Professor
Joao Amaral – Professor
Sangeet Ghai – Professor
John Kachura – Professor

Eric Bartlett – Associate Professor

Dr. Bartlett was awarded his promotion on the basis of Excellence in Teaching and Education strongly supported by Creative Professional Activity (CPA). 

Dr. Eric Bartlett (JDMI) obtained a Master of Public Health in 1995 in the Department of Medicine, University of Oklahoma, Oklahoma City, Oklahoma (USA).  In 1998, Dr. Bartlett obtained his MD in the Department of Medicine, University of Oklahoma, Oklahoma City, Oklahoma.  His Diagnostic Radiology residency was completed at the University of Iowa Hospitals and Clinics, Iowa (1999 to 2003). Following his residency, Dr. Bartlett came to the University of Toronto and completed a Neuroradiology Clinical Fellowship from 2003 to 2005 in the Department of Medical Imaging. Subsequently, Dr. Bartlett was appointed Assistant Professor in the Faculty of Medicine at the Feinberg School of Medicine and Northwestern University in Chicago, Illinois from 2005 to 2007. 

In July 2007, Dr. Bartlett returned to the University of Toronto and was appointed Assistant Professor in the Department of Medical Imaging, Neuroradiology Division, and Staff Radiologist at the Joint Department of Medical Imaging at Mount Sinai Hospital, the University Health Network and Women’s College Hospital.  In October 2013, he was appointed Program Director of the Diagnostic Radiology Residency Program, Faculty of Medicine, University of Toronto, a leadership position he held until December 2020. 


The culmination of Dr. Bartlett’s Program Directorship came recently during the RCPSC Accreditation visit when the program was granted full accreditation.  Furthermore, in recognition of the innovations undertaken in the program recommendations were made to acknowledge a number of Leading Practices and/ Innovations a great reflection of the novel work Dr. Bartlett has brought to the residency program.

In 2020, Dr. Bartlett was awarded the Outstanding Faculty Mentor Award by the Diagnostic Radiology Residency Program and since his appointment at the University of Toronto, has been awarded the Outstanding Teacher Award by the Diagnostic Radiology Residency Program a total of eight times, and was awarded the Outstanding Teacher Award by the Neuroradiology Fellowship Program four times. In 2019, he was awarded the “E.L. Lansdown Teaching Award our department’s most prestigious teaching award, from the Diagnostic Radiology Program. In 2018 was awarded the RDoC Puddester Award for Resident Wellness, a National award from the Residents of Canada. Dr. Bartlett has also managed to connect with all the Diagnostic Radiology Residency Programs throughout Canada and institute successful nationwide educational activities.

Dr. Bartlett has an h-index of 13, 619 citations (Scopus, March 5, 2021) and has 47 peer-reviewed publications. He also has two book chapters and is invited to give talks nationally and internationally.
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Kate Hanneman – Associate Professor

Dr. Hanneman was promoted on the basis of Excellence in Research. 

Dr. Hanneman obtained her MD at the University of Toronto in 2009. From 2009 to 2014, she completed Diagnostic Radiology Residency Program in the Department of Medical Imaging at the University of Toronto. Following residency training, Dr. Hanneman completed a one-year clinical fellowship in Cardiovascular Imaging at Stanford University in California (July 2014 to June 30, 2015). In July 2015, she was appointed Assistant Professor in the Department of Medical Imaging at the University of Toronto and Staff Radiologist in the Joint Department of Medical Imaging at Mount Sinai Hospital, the University Health Network, and Women’s College Hospital (Cardiothoracic Imaging Division). Dr. Hanneman is a Clinician Investigator completing a Master of Public Health (MPH), Epidemiology, at the Harvard T.H. Chan School of Public Health, Harvard University, Boston (June 2016 to May 2018).

Dr. Hanneman’s work has been recognized by her peers on a number of occasions including, the 2015 Young Investigator Award, Society of Computed Body Tomography and Magnetic Resonance, Toronto, ON. (Research Award, Specialty: Cardiac Imaging) and  2015 American Heart Association (AHA) Council on Cardiovascular Radiology and Intervention (CVRI) Young Investigator Award, first place, North American Society of Cardiovascular Imaging (NASCI), San Diego, CA, USA. (Research Award, Specialty: Cardiac Imaging).

Dr. Hanneman is a sought after teacher locally and further afield.  She is heavily committed to the teaching of residents and fellows in the area of cardiovascular imaging, as well as being involved in undergraduate teaching and continuing medical education.  Just like her increasing research profile, Dr. Hanneman is also recognized on the international stage for her educational efforts exemplified by the awards of the Radiological Society of North America Honored Educator Award in both 2017 and 2018.

Dr. Hanneman has an h-index of 15, 56 documents and 654 citations (Scopus, March 5, 2021), with 59 peer-reviewed publications of which she is Principal author on 15 and Senior Responsible Author on 17. She also has seven book chapters. 

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Farzad Khavati – Associate Professor

Dr. Khalvati was promoted on the basis of Excellence in Research.

Dr. Khalvati received his MASc in Electrical and Computer Engineering at the University of Waterloo in 2003. This was followed by his Ph.D. (also at the University of Waterloo) in 2009. From July 2009 to December 2012, Dr. Khalvati was appointed Senior Scientist and Chief Technology Officer at Segasist Technologies in Toronto. From January 2013 to September 2014, he was appointed Research Associate, Physical Sciences Platform, at Sunnybrook Research Institute followed by an appointment as a Junior Scientist, Physical Sciences Platform at Sunnybrook Research Institute. In 2017, Dr. Khalvati was appointed Staff Scientist at the Lunenfeld-Tanenbaum Research Institute.

In December 2014, Dr. Khalvati was appointed a Status Only Assistant Professor in the Department of Medical Imaging at the University of Toronto.  He holds a non-budgetary cross-appointment to the Department of Mechanical and Industrial Engineering as well as to the Institute of Medical Science; he is also a faculty affiliate at the Vector Institute.

In March 2020, Dr. Khalvati moved to the Hospital for Sick Children when he was appointed as a ‘Chair in Medical Imaging and Artificial Intelligence’, within the Temerty Faculty of Medicine and Associate Scientist at the Hospital for Sick Children.

Dr. Khalvati has contributed significantly to the Department of Medical Imaging’s AI research during his positions at various research institutes of the University of Toronto hospitals.  During that time, he developed his research platform using medical imaging as a means of detecting and characterizing cancer non-invasively without the need for biopsy, providing a means of interrogating the whole tumour and overcoming the problems of tissue heterogeneity.

Dr. Khalvati is a sought after speaker; he has received invitations to speak nationally and internationally.  He is dedicated to supporting the research process as an editorial board member, guest editor, peer reviewer, program committee member for international conferences and workshops. Dr. Khalvati is also a reviewer for the Ontario Centre of Excellence (OCE) for which he has reviewed the prodigious number of over 100 research proposals since his last promotion.

Dr. Khalvati has an h-index of 16, and 802 citations (Scopus, March 5, 2021); with 58 peer-reviewed publications. He also has one book chapter and holds three patents. 

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Anish Kirpalani – Associate Professor

Dr. Kirpalani was awarded his promotion on the basis of Excellence in Creative Professional Activity (Professional Innovation and Creative Excellence).  

Dr. Kirpalani received his MD from McMaster University in Hamilton, Ontario in 2000. He also has a Master of Applied Science from the Institute of Biomedical Engineering and Cardiovascular Sciences Collaborative Program at the University of Toronto (1996). From 2000 to 2005 Dr. Kirpalani completed a 5-year Diagnostic Radiology residency program in the Department of Medical Imaging at the University of Toronto. Following this, he moved to Chicago to complete a one-year Body MRI Fellowship (concentrating on Abdominal and Cardiovascular MRI) at Northwestern University. From 2006 to 2008, Dr. Kirpalani was appointed staff radiologist at Texas Radiology Associates, in Dallas and Plano, Texas. In 2009, he was appointed Lecturer in the Department of Medical Imaging, University of Toronto, and Staff Radiologist in the Division of Body Imaging in the Department of Medical Imaging at St. Michael’s Hospital. In 2011, Dr. Kirpalani was promoted to Assistant Professor at the University of Toronto. Since 2015, he has been cross-appointed to the Institute of Medical Science as an Associate Member. He was been appointed Division Head of Abdominal Imaging at St. Michael’s Hospital in 2017.

Dr. Kirpalani has made significant contributions to the development of professional innovation and creative excellence.  This has been achieved through three inter-connected pillars at the national level and beyond through leadership in his support for the founding of a research MRI centre at St Michael’s Hospital; providing an exemplar of research practice at the Centre through the development of new techniques within the MRI research centre for the investigation of renal disease and finally using this platform to develop a now nationally recognised MRI educational course which on the most recent iteration had over 700 registrants.

Dr. Kirpalani’s own research includes leveraging the new MRI Research Centre to help support his research program which was centered around renal diseases, a specialty area particularly served by St Michael’s Hospital.  

Dr. Kirpalani has received the Professor R. McCallum Award for Excellence in Quality, Innovation, and Research in Medical Imaging, Department of Medical Imaging, St. Michael’s Hospital on three occasions (2015, 2014, 2013).

Dr. Kirpalani has an h-index of 13, 423 citations (Scopus, March 5, 2021); and has 39 peer-reviewed publications plus 2 additional articles in preparation. He is also invited to give talks nationally and internationally.

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Pejman Maralani – Associate Professor

Dr. Maralani was promoted on the basis of Excellence in Creative Professional Activity (CPA) 

Dr. Maralani received his MD in 2005 at the Tehran University of Medical Sciences. From July 2006 to June 2011, he completed his Diagnostic Radiology residency training at the University of Ottawa. This was followed by a two-year Neuroradiology Fellowship at the University of Pennsylvania from July 2011 to June 30, 2013.  In July 2013, he was appointed Lecturer in the Department of Medical Imaging at the University of Toronto and Staff Radiologist in the Division of Neuroradiology at Sunnybrook Health Sciences Centre. In July 2015, Dr. Maralani was promoted to Assistant Professor. Since May 2016, Dr. Maralani has been the Program Director of the Neuroradiology Training Program at the University of Toronto.  He is also cross-appointed to the Institute of Medical Science as an Associate Member.

Dr. Maralani has made significant contributions to the development of professional practice.  These have involved involvement at a national level, preparing guidelines and recommendations for scanning patients with MRI using contrast agents and in those with implanted devices. Dr. Maralani has achieved this through his membership of the Canadian Association of Radiologists committee on contrast media and his chairing of the committee on device and implants. Dr. Maralani was co-lead of the original literature review and drafting of the guideline of Gadobutrol (Gadovist) (Can Assoc Radiol J.2018 May;69(2):136-150). This then led to the development of an updated clinical practice guideline in 2019 (Can Assoc Radiol J. 2019 Aug;70(3):226-232) and a further publication on gadoxetic acid contrast agent in 2020 (Radiology. 2020 May 26;200073).  A significant outcome of this work has been specific advice on renal function testing prior to contrast administration which has been a time-consuming and intrusive activity in everyday clinical practice.

Dr. Maralani has been the Neuroradiology Residency Program Director for the last 5 years culminating in successful accreditation in the last Royal College of Physicians and Surgeons Canada (RCPSC) accreditation cycle completed in late 2020.  At this time, the program was judged to have no Areas for Improvement (AFI) and a planned re-accreditation visit in 8 years. 

He is highly regarded as an excellent teacher and mentor to our residents and fellows and has also been involved in the teaching of undergraduate medical students. His success as a teacher within our programs is reflected in the fact that he has been awarded outstanding teacher in the residency five times (2019, 2017, 2016, 2015, 2014) and outstanding teacher in the fellowship four times (2019, 2018, 2016, 2015), also capturing our most prestigious residency teaching award, the Edward E Lansdown award, on one occasion in 2014.

Dr. Maralani has an h-index of 15 and 625 citations (Scopus, March 5, 2021), with 51 peer-reviewed publications, as well as one book chapter.

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Joao Amaral –  Professor

Dr. Amaral has been promoted on the basis of Excellence in Creative Professional Activity

Dr. Amaral completed his MD from the Department of Medicine, Federal University of Parana, Brazil from January 1991 to December 1996. From February 1997 to January 2000, he completed his Diagnostic Radiology Residency at the Hospital de Clinicas – Federal University of Parana, Curitiba, Brazil.   From June 2000 – November 2000, Dr. Amaral was a Research and Observer fellow, Pediatric Radiology at the Miami Children’s Hospital, Miami Florida.  Dr. Amaral completed a Clinical Fellowship in Pediatric/Interventional Radiology at the Hospital for Sick Children, University of Toronto from March 2001 to December 2003.  Following this, Dr. Amaral completed a Clinical/Research Fellowship in Interventional Pediatric Radiology at the Hospital for Sick Children, University of Toronto.

In 2006, Dr. Amaral was appointed Staff Radiologist, Pediatric Interventional Radiology at the Hospital for Sick Children.   Dr. Amaral was promoted to the rank of Associate Professor in the Department of Medical Imaging, University of Toronto in July 2012.   In September 2014, Dr. Amaral took on two leadership roles at the Hospital for Sick Children – Division Head, Interventional Radiology, and Co-Director, Centre for Image-Guided Care. In addition, from December 2017 – December 2020, Dr. Amaral was appointed the Associate Chief, Diagnostic Imaging Department at the Hospital for Sick Children.  In March 2020, Dr. Amaral was selected as the inaugural ‘Sick Children Chair in Image-Guided Care’.

Pediatric Interventional Radiology is a highly specialized area, with very few trained physicians all over the world. Dr. Amaral is an internationally renowned physician; Interventional Radiologists from across the world shadow or consult him for expert opinions in Pediatric Interventional Radiology. In addition, Dr. Amaral is a Board member for the Society of Pediatric Interventional Radiology, which is the most influential and important society in his subspecialty. Dr. Amaral has assumed a number of leadership roles both locally, nationally, and internationally.

Dr. Amaral has achieved an h-index of 21, 96 documents, and 1080 citations (Scopus, March 6, 2021). 

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Sangeet Ghai –  Professor

Dr. Ghai has been promoted on the basis of Excellence in Research.

Dr. Ghai is a graduate of Government Medical College, India, successfully completing his Residency training in October 1998. In 2002, he graduated from a Fellowship Program at the All India Institute of Medical Sciences. Subsequently, he completed a one-year Clinical Fellowship in Abdominal Imaging at the University Health Network/Mount Sinai Hospital from June 2002 to June 2003, followed by a second one-year Clinical Fellowship in Vascular & Interventional Radiology at the University Health Network/Mount Sinai Hospital from July 2003 to June 2004. 

In January 2007, he was appointed Assistant Professor in the Abdominal Imaging Division of the Department of Medical Imaging at the University of Toronto and Staff Radiologist in the Joint Department of Medical Imaging at the University Health Network, Mount Sinai Hospital, and Women’s College Hospital. Dr. Ghai was promoted to Associate Professor in July 2015.

Dr. Ghai’s research interest is in the diagnosis and treatment of localized prostate cancer.  Two new non-surgical treatment methods have undergone significant clinical research through Sangeet’s work, these being magnetic resonance focused ultrasound and magnetic resonance-guided focal laser thermal therapy.   

In 2018 Sangeet was appointed as the Director of the Princess Margaret Hospital Prostate Centre since which time he has introduced innovations such as ultrasound MRI image fusion biopsies and high-frequency micro-ultrasound biopsy. Dr. Ghai’s academic work was recognized in 2018 when he was appointed as the Vice Chief of Research at the Joint Department of Medical Imaging. Nationally and internationally Dr. Ghai has been invited as an expert reviewer for grants on prostate imaging and therapy.  

Dr. Ghai has an h-index of 23 and 1646 citations (Scopus, March 5, 2021); with 102 peer-reviewed publications of which he is Principal author on 26. He also has seven book chapters. 

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John Kachura –  Professor

Dr. Kachura has been promoted on the basis of Excellence in Creative Professional Activity.  

Dr. Kachura received his MD from the University of Toronto, with honours, in 1989.  He then completed a Comprehensive Medical Internship at the Toronto Western Hospital from 1989-1990, followed by a residency in Diagnostic Radiology at the University of Toronto from 1990-1994. In 1994-1995, Dr. Kachura completed a Fellowship in Vascular and Interventional Radiology at University Hospital and Boston City Hospital (Boston Medical Center), Boston University School of Medicine. 

In 1994-1995, Dr. Kachura was appointed as Instructor in the Department of Radiology, Boston University School of Medicine and from July 1995 to April 1998, Dr. Kachura held the appointment of Lecturer at the University of Toronto, Department of Medical Imaging.  Subsequently, he was appointed Assistant Professor in the Department of Medical Imaging, University of Toronto in 1998. In 2001, Dr. Kachura was appointed Division Head, Vascular and Interventional Radiology at the University Health Network/Mount Sinai Hospital.  In 2009, Dr. Kachura was appointed as Associate Professor in the Department of Medicine, University of Toronto.

In 2013, Dr. Kachura received the prestigious and internationally renowned Society of Interventional Radiology Fellowship (FSIR) granted to members who have made significant contributions to the field of interventional radiology Of the 7000+ SIR members only 10% receive FSIR recognition. Dr. Kachura has dedicated his career to improving patient care through the application of Interventional Radiology and Interventional Oncology on an international level. Dr. Kachura has held numerous administrative roles with a sustained track record of impact on a national and international scale. 

Dr. Kachura has an h-index of 27, 78 documents and 3342 total citations (Scopus, March 5, 2021).

New Article: A Comprehensive Study of Data Augmentation Strategies for Prostate Cancer Detection in Diffusion-Weighted MRI Using Convolutional Neural Networks

Published in Journal of Digital Imaging

Data augmentation refers to a group of techniques whose goal is to battle limited amount of available data to improve model generalization and push sample distribution toward the true distribution. While different augmentation strategies and their combinations have been investigated for various computer vision tasks in the context of deep learning, a specific work in the domain of medical imaging is rare and to the best of our knowledge, there has been no dedicated work on exploring the effects of various augmentation methods on the performance of deep learning models in prostate cancer detection. In this work, we have statically applied five most frequently used augmentation techniques (random rotation, horizontal flip, vertical flip, random crop, and translation) to prostate diffusion-weighted magnetic resonance imaging training dataset of 217 patients separately and evaluated the effect of each method on the accuracy of prostate cancer detection. The augmentation algorithms were applied independently to each data channel and a shallow as well as a deep convolutional neural network (CNN) was trained on the five augmented sets separately. We used area under receiver operating characteristic (ROC) curve (AUC) to evaluate the performance of the trained CNNs on a separate test set of 95 patients, using a validation set of 102 patients for finetuning. The shallow network outperformed the deep network with the best 2D slice-based AUC of 0.85 obtained by the rotation method.

New Article: RANDGAN: Randomized Generative Adversarial Network for Detection of COVID-19 in Chest X-ray

Published in Nature Scientific Reports

COVID-19 spread across the globe at an immense rate and has left healthcare systems incapacitated to diagnose and test patients at the needed rate. Studies have shown promising results for detection of COVID-19 from viral bacterial pneumonia in chest X-rays. Automation of COVID-19 testing using medical images can speed up the testing process of patients where health care systems lack sufficient numbers of the reverse-transcription polymerase chain reaction tests. Supervised deep learning models such as convolutional neural networks need enough labeled data for all classes to correctly learn the task of detection. Gathering labeled data is a cumbersome task and requires time and resources which could further strain health care systems and radiologists at the early stages of a pandemic such as COVID-19. In this study, we propose a randomized generative adversarial network (RANDGAN) that detects images of an unknown class (COVID-19) from known and labelled classes (Normal and Viral Pneumonia) without the need for labels and training data from the unknown class of images (COVID-19). We used the largest publicly available COVID-19 chest X-ray dataset, COVIDx, which is comprised of Normal, Pneumonia, and COVID-19 images from multiple public databases. In this work, we use transfer learning to segment the lungs in the COVIDx dataset. Next, we show why segmentation of the region of interest (lungs) is vital to correctly learn the task of classification, specifically in datasets that contain images from different resources as it is the case for the COVIDx dataset. Finally, we show improved results in detection of COVID-19 cases using our generative model (RANDGAN) compared to conventional generative adversarial networks for anomaly detection in medical images, improving the area under the ROC curve from 0.71 to 0.77.

Opening for AI in Medicine Research

Clinical Research Project Assistant position in Artificial Intelligence in Medical Imaging

At Intelligent Medical Image Computing Systems (IMICS) Lab at the Hospital for Sick Children (fully affiliated with The University of Toronto), we conduct high-throughput research in design and development of Artificial Intelligence solutions for Medical Imaging. The Clinical Research Project Assistant will work in close collaboration with the PI, graduate students as well as radiology fellows in a research project focused on developing innovative medical image analytics and machine (deep) learning solutions for classification and detection of brain tumours in pediatric population using MRI images. She/he will work effectively with the lab members to produce high quality research, algorithms, codes, manuscripts, and documents.

To apply, please visit SickKids Hospital Career page. Deadline to apply, Feb 19, 2021.

New Article: Improving prognostic performance in resectable pancreatic ductal adenocarcinoma using radiomics and deep learning features fusion in CT images

Published in Nature Scientific Reports

As an analytic pipeline for quantitative imaging feature extraction and analysis, radiomics has grown rapidly in the past decade. On the other hand, recent advances in deep learning and transfer learning have shown significant potential in the quantitative medical imaging field, raising the research question of whether deep transfer learning features have predictive information in addition to radiomics features. In this study, using CT images from Pancreatic Ductal Adenocarcinoma (PDAC) patients recruited in two independent hospitals, we discovered most transfer learning features have weak linear relationships with radiomics features, suggesting a potential complementary relationship between these two feature sets. We also tested the prognostic performance for overall survival using four feature fusion and reduction methods for combining radiomics and transfer learning features and compared the results with our proposed risk score-based feature fusion method. It was shown that the risk score-based feature fusion method significantly improves the prognosis performance for predicting overall survival in PDAC patients compared to other traditional feature reduction methods used in previous radiomics studies (40% increase in area under ROC curve (AUC) yielding AUC of 0.84).

Opening for Graduate Studies in AI in Medicine

At The Hospital for Sick Children, we have opening for a fully funded MSc student (domestic applicants only) in the field of Machine Learning for Medical Imaging and Medicine for January 2021 admission to Institute of Medical Science (IMS) at the University of Toronto. The research project is AI in Medicine with the emphasis on radiomics and deep learning for diagnosis and prognosis of brain tumours, which requires a strong background in statistical analysis and machine learning. The successful candidate may have the option to start as a Research Assistant at SickKids in Sep 2020 until she/he transitions to MSc student in January 2021. If interested, please send your CV and transcripts along with list of references to farzad dot khalvati at utoronto.ca before Aug 23, 2020. The successful candidate will be invited to apply to the School of Graduate Studies at the University of Toronto.

Call for Papers: AI in Medicine

Sensors Special Issue: Deep Learning-Based Imaging and Sensing Technologies for Biomedical Applications (Impact Factor: 3.27)

With the advent of deep learning, Artificial Intelligence (AI) models, including convolutional neural networks (CNNs), have delivered promising results for health monitoring and detection and prediction of different diseases using biomedical imaging and sensing technologies. These technologies help to improve the overall patient outcome by providing personalized diagnostics, prognostics, and treatment, improving the quality of life of patients. The unique challenges of developing AI models for health monitoring and disease diagnosis and prognosis using imaging and sensing technologies require customized models that go beyond off-the-shelf and generic AI solutions. These challenges include high accuracy, reliability, and explainability of the AI results for biomedical applications. To bring state-of-the-art research together, research papers reporting novel AI-driven imaging and/or sensing technologies with clinical applications are invited for submission to this Special Issue. The scope and topic of this Special Issue includes but is not limited to:

  • AI-driven advances in biomedical optical imaging/sensing technologies (e.g., optical imaging, optical coherence tomography, near infrared spectroscopy, diffuse optical spectroscopy) for biomedical applications;
  • AI-driven advances in medical image analysis using deep learning for different imaging modalities including X-ray, CT, MRI, PET, ultrasound, etc.;
  • Advances in AI-based solutions for disease diagnosis and prognosis using imaging and/or sensing technologies;
  • Advances in AI explainability solutions for imaging and/or sensing technologies that address different aspects of AI explainability, including novel attention map generators as well as ways to interpret the results and integrate them into clinical settings.

Dr. Farzad Khalvati
Guest Editor

Welcome to IMICS Lab Webpage

At Intelligent Medical Image Computing Lab (IMICS Lab) at The Hospital for Sick Children, 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. IMICS Lab is affiliated with Department of Medical ImagingInstitute of Medical Science, and Department of Mechanical and Industrial Engineering at the University of Toronto.