Graph-search Based UNet-d For The Analysis Of Endoscopic Images

Yang, S. and Cochran, S. (2019) Graph-search Based UNet-d For The Analysis Of Endoscopic Images. In: 2019 Challenge on Endoscopy Artefacts Detection (EAD2019), Venice, Italy, 08 Apr 2019,

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While object recognition in deep neural networks (DNN) has shown remarkable success in natural images, endoscopic images still cannot be fully analysed using DNNs, since analysing endoscopic images must account for occlusion, light reflection and image blur. UNet based deep convolutional neural networks (DNNs) offer great potential to extract high-level spatial features, thanks to its hierarchical nature with multiple levels of abstraction, which is especially useful for working with multimodal endoscopic images with white light and fluoroscopy in the diagnosis of esophageal disease. However, the currently reported inference time for DNNs is above 200ms, which is unsuitable to integrate into robotic control loops. This work addresses real-time object detection and semantic segmentation in endoscopic devices. We show that endoscopic assistive diagnosis can approach satisfy detection rates with a fast inference time.

Item Type:Conference Proceedings
Glasgow Author(s) Enlighten ID:Yang, Dr Shufan and Cochran, Professor Sandy
Authors: Yang, S., and Cochran, S.
College/School:College of Science and Engineering > School of Engineering > Systems Power and Energy
Copyright Holders:Copyright © 2019 The Authors
Publisher Policy:Reproduced in accordance with the copyright policy of the publisher

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