OICR BioLab: Mini-symposium on Artificial Intelligence in Cancer Imaging

OICR BioLab: Mini-symposium on Artificial Intelligence in Cancer Imaging

When and Where

Date: Friday, June 21, 2019 - Friday, June 21, 2019 (1 day)
Price: Free Location: 661 University Ave
West Tower, Suite 510 Toronto, ON, M5G 0A3 View map »

Registration Contact

Dr. Vanya Peltekova , Ph.D
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BioLab is hosting a mini-symposium on Artificial Intelligence in Cancer Imaging: Bridging the Gap between Pathologist and Algorithm on June 21st 1-4:30 pm at OICR.

Description:

The synergy between digital imaging, machine learning, and pathology hold high expectations for improving patient stratification in oncology. Intradisciplinary experts from the University of Waterloo, Princess Margaret Cancer Centre, and STTARR Imaging Facilities at UHN will share their knowledge on intelligent digital imaging workflows, machine learning solutions, and AI.

OBJECTIVES

The audience will gain knowledge on intelligent digital imaging workflows, machine learning (ML) tools, and artificial intelligence (AI) analysis that can assist pathologists and support researchers in integrating multilayered image data and machine learning algorithms into cancer diagnostic decision-making.

EVENT DETAILS

Our keynote speakers include Dr. Hamid Tizhoosh, Professor, University of Waterloo, Ontario, and Director of the Knowledge Inference in Medical Image Analysis (KIMIA) Lab; Dr. Phedias Diamandis, Neuropathologist and Clinician Scientist at UHN and Princess Margaret Cancer Centre, and; Dr. Trevor McKee, STTARR imaging facilities, UHN.

The symposium will provide opportunities for researchers, students, and health professionals to:

  • meet experts from digital imaging, machine learning, and pathology;
  • participate in discussion on how future imaging demands can be met via integration of pathology, imaging and bioinformatics;
  • learn more on how AI and ML tools can be conceptualized, synergized, and used in image based clinical tasks for maximizing the pathological image data output.

AGENDA

1-2 p.m. How to go digital in pathology
Dr. H. Tizhoosh, KIMIA Lab, University of Waterloo
Introduction to the Digital pathology, a rapidly evolving and essential technology, with specific support for tissue-based research, drug development and the practice of human pathology.

2–2:15 p.m. Artificial Intelligence (AI) algorithms in digital pathology: an overview
Dr. Morteza Babaie and Amir Safarpour, KIMIA Lab, University of Waterloo
Foundations and approaches in developing computerized algorithms for high dimensional data and image analysis for predicting disease outcome. How AI uses algorithms to represent data, classify data, and search for similar instances, either in supervised or unsupervised approaches.

2:15–2:30 p.m. Image search and diagnosis: a first validation using TCGA data
Shivam Kalra, KIMIA Lab, University of Waterloo
An overview, strategies and applications for working on deep networks, metric learning, autoencoders, and searching in large archives of pathology images.

2:30–3:30 p.m. Understanding Machine Engineered Reasoning in Pathology
Dr. Phedias Diamandis, MD, PhD, FRCPC
A pathologist perspective on integration of artificial intelligence and machine learning into diagnostic pathology. Examples of how computer-aided image analysis can be used in various tasks in cancer imaging, e.g. detection, diagnosis, prognosis, and response to therapy. Learn how digital tools can be applied to resolve phenotypic heterogeneity in different glioblastoma niches, empower data mining with patient characteristics to build novel predictive indicators for tumor detection, monitoring and therapy.

3:30–4:30 p.m. To use AI or not? Machine Learning in Practice: clinical trial and translational research applications
Dr. Trevor McKee, PhD, STTARR Imaging facilities
Principles underlying the development of algorithms for the segmentation analysis of histopathology images. Examples of how semi-automated cell-counting strategies on single and multiplexed stained tissue sections can be used for obtaining information on cell markers in relation to cell phenotypes and spatial arrangements. Examples from quantitative analysis on clinical trial specimens will be used to illustrate the current application of machine learning methods in a high-throughput core facility environment.

There is no cost to attend.

For additional information on speakers and agenda, click here.

To register to the event, click here.

Pathologists and Researchers who are interested in being part of the OMPRN: