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Artificial Intelligence and Machine Learning in Medical Imaging

Tizhoosh on AI

June 6, 2018Insights from industry: Dr. Hamid R. TizhooshProfessor Faculty of EngineeringUniversity of Waterloo - An interview with Professor Hamid Tizhoosh, conducted by James Ives

 

Please give an overview of the past research into machine learning and artificial intelligence in medical imaging. What are we currently able to do with this research?

The two major tasks in medical imaging that appear to be naturally predestined to be solved with AI algorithms are segmentation and classification. Most of techniques used in medical imaging were conventional image processing, or more widely formulated computer vision algorithms.

One can find many works with artificial neural networks, the backbone of deep learning. However, most works were focused on conventional computer vision which focused, and still does, on "handcrafted" features, techniques that were the results of manual design to extract useful and differentiating information from medical images.

Some progress was visible in the late 90s and early 2000s (for instance, the SIFT method in 1999, or visual dictionaries in early 2000s) but there were no breakthroughs. However, techniques like clustering and classification were in use with moderate success.

K-means (an old clustering method), support vector machines (SVM), probabilistic schemes, and decisions trees and their extended version 'random forests' were among successful approaches.  But artificial neural networks continued to fall short of expectations not just in medical imaging, but in computer vision in general.

Shallow networks (consisting of a few layers of artificial neurons) could not solve difficult problems and deep networks (consisting of many layers of artificial neurons) could not be trained because they were too big. By the mid 2000s there was theoretical progress in this field with the first major success stories in early 2010s on large datasets like ImageNet.

Now suddenly, it was possible to recognise cats and cars in an image, perform facial recognition and automatically label images with a caption describing its content. The investigations of applications of these powerful AI methods in medical imaging has started in the past 3-4 years and is in its infancy but promising results have been reported here and there.  

What applications are there for machine learning and artificial intelligence in medical imaging?

Based on recent publications, it seems that the focus of many researchers is on diagnosis, mainly cancer diagnosis, where the output of the AI software is often a "yes/no" decision for malignant/benign, respectively.

The other stream is working on segmenting (marking) specific parts of the images, again with the main attention of many works being on cancer diagnosis and analysis, but also for treatment planning and monitoring.

However, there is much more that AI can offer to medical imaging. Looking at its potentials for radiogenomics, auto-captioning of medical images, recognition of highly non-linear patterns in large datasets, and quantification and visualization of extremely complex image content, are just some examples. We are at the very beginning of an exciting path with many bifurcations.

:: Read the full interview on News Medical Website

 

 

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