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PhD Aptitude Defence - In vivo classification of inflammation in blood vessels with convolutional neural networks

Who: Stuart Mcilroy

Title: In vivo classification of inflammation in blood vessels with convolutional neural networks

Examining Committee:

Dr Thomas Trappenberg - Faculty of Computer Science (Supervisor)
Dr. Christian Lehmann - Department of Anesthesia, Pain Management & Perioperative Medicine (External Examiner)
Dr. Qigang Gao  - Faculty of Computer Science (Reader)
Dr. Dirk Arnold - Faculty of Computer Science (Reader)

 

Abstract:

An emerging field in medical diagnostics is the study of micro-circulations in blood vessels. Several characteristics of the micro-circulations in blood vessels have been shown to predict inflammation in a patient’s tissue. The characteristics are video recorded via a camera inserted into the subject. At present the analysis is done manually by visual inspection of the videos to determine inflammation. In our paper, we propose a technique to automatically classify the videos as containing inflammation or not. Our technique uses a convolutional neural network. Our network achieves an accuracy of 83%. We further divide inflammation into extreme and moderate inflammation and our network achieves an accuracy of 80%.

Time

Location

FCS Room 142