Laboratory for Knowledge Inference in
Medical Image Analysis

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Big Image Data, Machine Learning, and The Future of Medical Imaging

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 with the ultimate goal of increasing the quality of these images, among others the resolution and contrast. 

With every year, the number of images we capture for each patient increases, making efficient processing this image quite challenging. On the other side, there is a tremendous level of knowledge buried in our huge databases, a knowledge that goes untapped.  The Laboratory for Knowledge Inference in Medical Image Analysis, short KIMIA Lab, has been founded with the specific mandate to explore the knowledge mining in large medical image archives using advanced machine-learning algorithms. We use deep networks, supervised and unsupervised clustering methods, dimensionality reduction, and generative models to overcome the challenges of dealing with big image data.

Big Image Data

KIMIA Lab, established on October 20, 2013 at the University of Waterloo, envisions to conduct research at the forefront of big image data in medical archives with ultimate goal of extracting information that cannot only support a more speedy and accurate diagnosis and treatment of many diseases but also, and more significantly, establish new quality assurance based on mining of collective knowledge and wisdom.

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© KIMIA Lab, University of Waterloo, Canada, 2013-2017