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- Borgli, H, et al.
(författare)
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HyperKvasir, a comprehensive multi-class image and video dataset for gastrointestinal endoscopy
- 2020
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Ingår i: Scientific data. - : Springer Science and Business Media LLC. - 2052-4463. ; 7:1, s. 283-
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Tidskriftsartikel (refereegranskat)abstract
- Artificial intelligence is currently a hot topic in medicine. However, medical data is often sparse and hard to obtain due to legal restrictions and lack of medical personnel for the cumbersome and tedious process to manually label training data. These constraints make it difficult to develop systems for automatic analysis, like detecting disease or other lesions. In this respect, this article presents HyperKvasir, the largest image and video dataset of the gastrointestinal tract available today. The data is collected during real gastro- and colonoscopy examinations at Bærum Hospital in Norway and partly labeled by experienced gastrointestinal endoscopists. The dataset contains 110,079 images and 374 videos, and represents anatomical landmarks as well as pathological and normal findings. The total number of images and video frames together is around 1 million. Initial experiments demonstrate the potential benefits of artificial intelligence-based computer-assisted diagnosis systems. The HyperKvasir dataset can play a valuable role in developing better algorithms and computer-assisted examination systems not only for gastro- and colonoscopy, but also for other fields in medicine.
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| 4. |
- Riegler, M, et al.
(författare)
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From Annotation to Computer-Aided Diagnosis: Detailed Evaluation of a Medical Multimedia System
- 2017
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Ingår i: ACM TRANSACTIONS ON MULTIMEDIA COMPUTING COMMUNICATIONS AND APPLICATIONS. - : Association for Computing Machinery (ACM). - 1551-6857 .- 1551-6865. ; 13:3
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Tidskriftsartikel (övrigt vetenskapligt/konstnärligt)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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