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Kimia Lab


“Kimia Lab is not accepting MSc/PhD applications before March 2022.”

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, […]


Kimia Lab joins WHO Consortium for Cancer Research

Kimia Lab joins WHO Consortium for Cancer Research

November 4, 2020 – UW’s Kimia Lab will contribute to WHO’s global research for cancer categorization.  The International Collaboration for Cancer Classification and Research (IC3R) officially accepted Kimia Lab as a full member on October 22, 2020. The UW lab, which is specialized on image search in medical archives, will assist IC3R to implement content-based image retrieval for a  WHO global digital atlas of histopathology […]

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World’s largest human tissue archive adopts Kimia’s AI algorithms

World’s largest human tissue archive adopts Kimia’s AI algorithms

Artificial intelligence technology will be used to help modernize US federal pathology facility Waterloo, ON, October 26, 2020 – Technology developed by Kimia researchers has been adopted by a major pathology facility in the United States. The Joint Pathology Center (JPC), which has the world’s largest collection of preserved human tissue samples, will use an artificial intelligence (AI) search engine to index and search its […]

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Kimia Lab and Huron at Pathology Visions 2020

Kimia Lab and Huron at Pathology Visions 2020

Monday, October 26 | 9-9:45am PTLive Q&A: 9:45-10:05am PT In this pre-conference workshop, Huron Digital Pathology’s CEO, Patrick Myles, and their AI advisor, Professor Hamid Tizhoosh, will illustrate how Huron’s LagottoTM image search platform has been designed to integrate seamlessly into existing and emerging digital pathology workflows, describing several workflow scenarios. Attendees will learn the following: How does WSI indexing work? What file formats does Lagotto […]

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+1 519 888 4567


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