[3] | Kirill Borodulin, Gleb Radchenko, Aleksandr Shestakov, Leonid Sokolinsky, Andrey Tchernykh, Radu Prodan, Towards Digital Twins Cloud Platform: Microservices and Computational Workflows to Rule a Smart Factory, In 2017 IEEE/ACM $10^\mathitth$ International Conference on Utility and Cloud Computing, ACM, pp. 209-210, 2017.
[bib] |
[2] | Christian Beecks, Sabrina Kletz, Klaus Schoeffmann, Large-Scale Endoscopic Image and Video Linking with Gradient-Based Signatures, In Proceedings of the Third IEEE International Conference on Multimedia Big Data (BigMM 2017) (Shu-Ching Chen, Philip Chen-Yu Sheu, eds.), IEEE, Laguna Hills, California, USA, pp. 5, 2017.
[bib][url] [doi] [abstract]
Abstract: Given a large-scale video archive of surgical interventions and a medical image showing a specific moment of an operation, how to find the most image-related videos efficiently without the utilization of additional semantic characteristics? In this paper, we investigate a novel content-based approach of linking medical images with relevant video segments arising from endoscopic procedures. We propose to approximate the video segments' content-based features by gradient-based signatures and to index these signatures with the Minkowski distance in order to determine the most query-like video segments efficiently. We benchmark our approach on a large endoscopic image and video archive and show that our approach achieves a significant improvement in efficiency in comparison to the state-of-the-art while maintaining high accuracy.
|
[1] | Konstantin Pogorelov, Kristin Ranheim Randel, Thomas de Lange, Sigrun L. Eskeland, Carsten Griwodz, Concetto Spampinato, Mario Taschwer, Mathias Lux, Peter T. Schmidt, Michael Riegler, Pal Halvorsen, Nerthus: A Bowel Preparation Quality Video Dataset, In Proceedings of the 8th ACM on Multimedia Systems Conference (MMSys 2017) (Kuan-Ta Chen, Pablo Cesar, Cheng-Hsin Hsu, eds.), Association for Computing Machinery (ACM), pp. 170-174, 2017.
[bib][url] [doi] [abstract]
Abstract: Bowel preparation (cleansing) is considered to be a key precondition for successful colonoscopy (endoscopic examination of the bowel). The degree of bowel cleansing directly affects the possibility to detect diseases and may influence decisions on screening and follow-up examination intervals. An accurate assessment of bowel preparation quality is therefore important. Despite the use of reliable and validated bowel preparation scales, the grading may vary from one doctor to another. An objective and automated assessment of bowel cleansing would contribute to reduce such inequalities and optimize use of medical resources. This would also be a valuable feature for automatic endoscopy reporting in the future. In this paper, we present Nerthus, a dataset containing videos from inside the gastrointestinal (GI) tract, showing different degrees of bowel cleansing. By providing this dataset, we invite multimedia researchers to contribute in the medical field by making systems automatically evaluate the quality of bowel cleansing for colonoscopy. Such innovations would probably contribute to improve the medical field of GI endoscopy.
|