|Authors||K. Pogorelov, S. L. Eskeland, T. de Lange, C. Griwodz, K. R. Randel, H. K. Stensland, D. Dang-Nguyen, C. Spampinato, D. Johansen, M. Riegler et al.|
|Editors||P. Cesar and C. Hsu|
|Title||A Holistic Multimedia System for Gastrointestinal Tract Disease Detection|
|Project(s)||Efficient EONS: Execution of Large Workloads on Elastic Heterogeneous Resources, Unified PCIe IO: Unified PCI Express for Distributed Component Virtualization|
|Publication Type||Proceedings, refereed|
|Year of Publication||2017|
|Conference Name||8th annual ACM conference on Multimedia Systems (MMSys)|
Analysis of medical videos for detection of abnormalities and diseases requires both high precision and recall, but also real-time processing for live feedback and scalability for massive screening of entire populations. Existing work on this field does not provide the necessary combination of retrieval accuracy and performance.
In this paper, a multimedia system is presented where the aim is to tackle automatic analysis of videos from the human gastrointestinal (GI) tract. The system includes the whole pipeline from data collection, processing and analysis, to visualization. The system combines filters using machine learning, image recognition and extraction of global and local image features. Furthermore, it is built in a modular way so that it can easily be extended. At the same time, it is developed for efficient processing in order to provide real-time feedback to the doctors. Our experimental evaluation proves that our system has detection and localisation accuracy at least as good as existing systems for polyp detection, it is capable of detecting a wider range of diseases, it can analyze video in real-time, and it has a low resource consumption for scalability.