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From Annotation to Computer-Aided Diagnosis: Detailed Evaluation of a Medical Multimedia System

Published: 31 May 2017 Publication History

Abstract

Holistic medical multimedia systems covering end-to-end functionality from data collection to aided diagnosis are highly needed, but rare. In many hospitals, the potential value of multimedia data collected through routine examinations is not recognized. Moreover, the availability of the data is limited, as the health care personnel may not have direct access to stored data. However, medical specialists interact with multimedia content daily through their everyday work and have an increasing interest in finding ways to use it to facilitate their work processes. In this article, we present a novel, holistic multimedia system aiming to tackle automatic analysis of video from gastrointestinal (GI) endoscopy. The proposed system comprises the whole pipeline, including data collection, processing, analysis, and visualization. It combines filters using machine learning, image recognition, and extraction of global and local image features. The novelty is primarily in this holistic approach and its real-time performance, where we automate a complete algorithmic GI screening process. We built the system in a modular way to make it easily extendable to analyze various abnormalities, and we made it efficient in order to run in real time. The conducted experimental evaluation proves that the detection and localization accuracy are comparable or even better than existing systems, but it is by far leading in terms of real-time performance and efficient resource consumption.

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    cover image ACM Transactions on Multimedia Computing, Communications, and Applications
    ACM Transactions on Multimedia Computing, Communications, and Applications  Volume 13, Issue 3
    August 2017
    233 pages
    ISSN:1551-6857
    EISSN:1551-6865
    DOI:10.1145/3104033
    Issue’s Table of Contents
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    Publication History

    Published: 31 May 2017
    Accepted: 01 April 2017
    Revised: 01 March 2017
    Received: 01 March 2016
    Published in TOMM Volume 13, Issue 3

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    Author Tags

    1. Medical multimedia system
    2. evaluation
    3. gastrointestinal tract

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    • Norwegian FRINATEK project “EONS”

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    • (2023)Fine-tuned deep neural networks for polyp detection in colonoscopy imagesPersonal and Ubiquitous Computing10.1007/s00779-021-01660-y27:2(235-247)Online publication date: 1-Apr-2023
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