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Kimia Lab


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 […]


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 […]


ORF-RE Consortium Digital Pathology Artificial Intelligence Image Search Image Identification OMPRN CPTRG McMaster Health Sciences Cytology Histopathology Molecular Medicine 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 […]

Kimia Team

Former KIMIA Members Cheeseman, Alison [2019-2020] Ahn, Jun [2019-2020] Sze-To, Ho Yin, Antonio [2019-2020] Kashani, Hany [2019-2020] Lifshitz, Shalev [2019] Li, Larry [2018] Thangarajah, Karish [2018] Kieffer, Keagan [2018] Adnan, Mohamed [2018] Chenni, Wafa [2018] Herbi, Habib [2018] Zhu, Shujin [2017] Kiefer, Brady [2017] Kumar, Meghana D [2017] Liang, Ethan [Winter 2017] Khatami, Amin [ Winter 2017] Xu, Yinghai (Candice)[Spring 2016] Christopher Mitcheltree [ Winter 2017, […]


UW Professor on The Pathologist’s “Power List 2020”

UW Professor on The Pathologist’s “Power List 2020”

UW’s Hamid Tizhoosh is among the 20 pathologists and computer scientists listed in this year’s “Power List” of The Pathologist who achieved  Big Breakthroughs, “ trailblazers working at the cutting edge and driving forward the future of the field”. The magazine collects nominations from the pathology community. An expert panel selects the distinguished physicians and researchers based on their track record and achievements in the field. […]

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Kimia Lab Students publish and present at ECCV and CVPR

Kimia Lab Students publish and present at ECCV and CVPR

A novel method developed by graduate students from Kimia Lab, Waterloo Engineering, Mohammed Adnan (1st-year MASc.), and Shivam Kalra (2nd year Ph.D.) has the potential to have a major impact in histopathological image analysis and cancer diagnostics. Their technique uses artificial intelligence (AI) to render digital representations of extremely high-resolution biopsy images. These digital representations can be used for real-time image search, providing critical information to pathologists for well-informed and […]

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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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+1 519 888 4567


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