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Article

Explainable Multimedia Feature Fusion for Medical Applications

1
Faculty of Mathematics and Computer Science, University of Hagen, Universitätsstrasse 1, 58097 Hagen, Germany
2
Academy for International Science & Research (AISR), Derry BT48 7JL, UK
3
Blekinge Institute of Technology, 371 79 Karlskrona, Sweden
*
Author to whom correspondence should be addressed.
Academic Editors: Giuseppe Placidi, Mrinal Mandal and Mustapha Bouhrara
J. Imaging 2022, 8(4), 104; https://doi.org/10.3390/jimaging8040104
Received: 15 March 2022 / Revised: 4 April 2022 / Accepted: 5 April 2022 / Published: 8 April 2022
(This article belongs to the Special Issue Intelligent Strategies for Medical Image Analysis)
Due to the exponential growth of medical information in the form of, e.g., text, images, Electrocardiograms (ECGs), X-rays, and multimedia, the management of a patient’s data has become a huge challenge. In particular, the extraction of features from various different formats and their representation in a homogeneous way are areas of interest in medical applications. Multimedia Information Retrieval (MMIR) frameworks, like the Generic Multimedia Analysis Framework (GMAF), can contribute to solving this problem, when adapted to special requirements and modalities of medical applications. In this paper, we demonstrate how typical multimedia processing techniques can be extended and adapted to medical applications and how these applications benefit from employing a Multimedia Feature Graph (MMFG) and specialized, efficient indexing structures in the form of Graph Codes. These Graph Codes are transformed to feature relevant Graph Codes by employing a modified Term Frequency Inverse Document Frequency (TFIDF) algorithm, which further supports value ranges and Boolean operations required in the medical context. On this basis, various metrics for the calculation of similarity, recommendations, and automated inferencing and reasoning can be applied supporting the field of diagnostics. Finally, the presentation of these new facilities in the form of explainability is introduced and demonstrated. Thus, in this paper, we show how Graph Codes contribute new querying options for diagnosis and how Explainable Graph Codes can help to readily understand medical multimedia formats. View Full-Text
Keywords: indexing; retrieval; explainability; semantic; multimedia; feature graph; graph code indexing; retrieval; explainability; semantic; multimedia; feature graph; graph code
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MDPI and ACS Style

Wagenpfeil, S.; Mc Kevitt, P.; Cheddad, A.; Hemmje, M. Explainable Multimedia Feature Fusion for Medical Applications. J. Imaging 2022, 8, 104. https://doi.org/10.3390/jimaging8040104

AMA Style

Wagenpfeil S, Mc Kevitt P, Cheddad A, Hemmje M. Explainable Multimedia Feature Fusion for Medical Applications. Journal of Imaging. 2022; 8(4):104. https://doi.org/10.3390/jimaging8040104

Chicago/Turabian Style

Wagenpfeil, Stefan, Paul Mc Kevitt, Abbas Cheddad, and Matthias Hemmje. 2022. "Explainable Multimedia Feature Fusion for Medical Applications" Journal of Imaging 8, no. 4: 104. https://doi.org/10.3390/jimaging8040104

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