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Searching for Knowledge in BIG Image Data Using AI

With big data, we mean any enormous and multifaceted collection of data (texts, numbers, documents, images, videos, etc.) that cannot be analyzed by ordinary computing devices and algorithms but through artificial intelligence algorithms. Big data, due to their sheer volume and inherent variety, are extremely challenging to manage and hence difficult to understand. 

One of the major fields that generate big data is the biomedical and healthcare field in general and medical imaging in particular. The latter is the focus of our research at KIMIA Lab. Images do have a special place in this regard because as two-dimensional data structures, their processing is even more challenging.

More than approximately two trillion medical images are captured worldwide each year. A large number of these images have to be stored for several years. There is a huge amount of information contained in these images and their annotations (notes on diagnosis, biopsy, treatment, etc.). Presently this colossal pool of human knowledge is going untapped. Employing machine-learning algorithms on distributed platforms may help us to overcome this barrier and to create the frontier for the 21st-century medical imaging.

The Laboratory for Knowledge Inference in Medical Image Analysis, short KIMIA Lab, has been founded with the specific mandate to extract knowledge from large medical image archives by designing smart search, classification and annotation technologies.

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About Kimia Lab

Medical imaging is a fundamental aspect of modern medicine. Images from various modalities (e.g., MRI, CT, PET) are generated and used in many different clinical settings such as the analysis of tumors and cancer treatment planning. In North America alone, over 700 billion images are produced annually, with some procedures generating thousands of images requiring analysis. With continued investments and improvements in imaging equipment, the […]

Research

Big Image Data, Artificial Intelligence, and The Future of Digital Pathology The modern medicine is inconceivable without all imaging modalities available to radiologists, oncologists, cardiologists, pathologists and other clinicians. Computed tomography, magnetic resonance imaging, and ultrasound imaging are among the most commonly used imaging techniques. These technologies enable us to look inside the human body for diagnosis, treatment and monitoring purposes. Innovative technologies constantly emerging […]

Projects

ORF-RE Consortium Digital Pathology Artificial Intelligence Image Search Image Identification Vector Institute – UHN Radiology Artificial Intelligence Pneumothorax Identification SIF Network – Sunnybrook Digital Pathology Artificial Intelligence Auto-Reporting Consensus Building A large part of basic research at KIMIA Lab is concentrated at identification, tagging, captioning and search in Whole Slide Imaging (WSI) in digital pathology. We use diverse old and new techniques to learn how to generate barcodes […]

Kimia Team

“Kimia Lab is not accepting new MSc/PhD applications until early 2021.” Current KIMIA Team Adnan, Mohammed [AI in Digital Pathology] Afshari, Mehdi [Deep Learning, Unsupervised Learning, AI in Image Recognition] Ahn, Jun [x-ray image classification & retrieval, deep-learning] Babaie, Morteza [Machine Learning, Pattern recognition, Binary descriptors] Hemati, Sobhan [Statistical┬áMachine Learning, Deep Learning] Kalra, Shivam [deep learning] Maleki, Danial [AI in Digital Pathology] Maulinkumar Zaveri, Manit [AI […]

News

Diagnostic consensus for cancer is possible through image search using AI

Diagnostic consensus for cancer is possible through image search using AI

Pan-cancer diagnostic consensus through searching archival histopathology images using artificial intelligence A new system combining artificial intelligence (AI) with human knowledge promises faster and more accurate cancer diagnosis. The powerful technology, developed by a team led by engineering researchers at the University of Waterloo, uses digital images of tissue samples to match new cases of suspected cancer with previously diagnosed cases in a database. In […]

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Kimia Lab Presents Posters at USCAP 2020

Kimia Lab Presents Posters at USCAP 2020

The USCAP 109th Annual Meeting United States and Canadian Academy of Pathology (USCAP) will hold its 109th Annual Meeting for the first time in Convention Center Los Angeles, California, February 29-March 5, 2020. Kimia Lab will present posters on Detecting Specimen Contamination in Whole Slide Imaging Using Artificial Intelligence and Automatic Assessment of Tumor Cellularity in Histopathology Images Using Weakly-Supervised segmentation. Poster VI – Wednesday March […]

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Shopify Data Talks – The History of AI

Shopify Data Talks – The History of AI

Welcome to the first of the Shopify Data Talks series! Join us with Dr. Hamid Tizhoosh as he takes us through the past, present, and future of AI. Tackling questions like when was modern AI born?, why did AI experience a renaissance?, what happens next?, and what should we do now? We will be gathering at Shopify’s 57 Erb St. location in Waterloo with time for networking, drinks, […]

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Contact Us

Email:

kimia.admin@uwaterloo.ca

Phone:

+1 519 888 4567

Address:

E7 Building (Engineering 7)
University of Waterloo
200 University Ave W, Waterloo, ON N2L 3G1