KIMIA Lab
Laboratory for Knowledge Inference in
Medical Image Analysis

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KIMIA Lab - Searching for Knowledge in BIG Image Data

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. Big data, due to their sheer volume and inherent variety, are extremely challenging to manage and hence difficult to understand. 

More and more users, and more and more machines generate more unstructured data on a daily basis. The Internet of Things (IoT) is emerging as a major source of big data in many different application fields. Capturing the essence of such massive stream of data is beyond traditional computation facilities and their classical methodologies. Large data centers and intelligent algorithms, embedded within capable and flexible distributed computing environments such as Hadoop, are necessary to make sense of big data.

One of the major fields "suffering" from big data, which has been widely neglected so far, 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, their processing is even more challenging.

BIg Data

Radiology and Pathology

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 desighing smart search, classifucation and annotation technologies.

 

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