EIR — Efficient computer aided diagnosis framework for gastrointestinal endoscopies

Analysis of medical videos for detection of abnormalities like lesions and diseases requires both high precision and recall but also real-time processing for live feedback during standard colonoscopies and scalability for massive population based screening, which can be done using a capsular video endoscope. Existing related work in this field does not provide the necessary combination of detection 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, and 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 doctor. Initial experiments show that our system has detection and localisation accuracy at least as good as existing systems, but it stands out in terms of real-time performance and low resource consumption for scalability.