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“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.

Featured Pages

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 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 with […]

Projects

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 Abtin Riasatian [2019-2021] Manit Maulinkumar Zaveri [2018-2020] 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 […]

News

Pathology News – Conversation with Dr. Hamid Tizhoosh, Founder of KIMIA Lab and Leading Expert in the Development of Unsupervised AI for Tissue Pathology

Pathology News – Conversation with Dr. Hamid Tizhoosh, Founder of KIMIA Lab and Leading Expert in the Development of Unsupervised AI for Tissue Pathology

Pathology News, Jonathon Tunstall, interviewed Dr. Hamid Tizhoosh on June 21, 2021. The interview has been published on the Pathology News website on Sept 6, 2021. “What we have to learn from day one when we design these AI applications, is that pathology has to come with us. We cannot just design a network as computer scientists and then go to the pathologists just when […]

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Waterloo Researchers are Accelerating the Cancer Diagnosis Process via Artificial Intelligence (AI)

Waterloo Researchers are Accelerating the Cancer Diagnosis Process via Artificial Intelligence (AI)

For many pathologists, diagnosing cancer is one stressful and complicated process. Typically, these medical professionals are alone in their office, examining a biopsy sample through a microscope. Only in major research hospitals may they have the privilege of consulting with other colleagues for a second opinion. In only very doubtful cases, they may request a teleconsultation. Although printed or digital atlases containing thousands of sample […]

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Kimia Lab Members Selected as Vector  Postgraduate Affiliates

Kimia Lab Members Selected as Vector Postgraduate Affiliates

March 31, 2021 – The Vector Institute announced the 2021 candidates for its Postgraduate Affiliate Program. The new cohort of 30 talented researchers is a combination of new and returning members to Vector’s growing community. Established in 2018, the program promotes engagement and collaboration among researchers in the AI community who are in the early stages of their careers. Morteza Babaie and Shivam Kalra from Kimia Lab are among […]

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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

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