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Explainable Multimedia Feature Fusion for Medical Applications
Univ Hagen, DEU.
Acad Int Sci & Res AISR, GBR.
Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science.ORCID iD: 0000-0002-4390-411X
Univ Hagen, DEU.
2022 (English)In: JOURNAL OF IMAGING, ISSN 2313-433X, Vol. 8, no 4, article id 104Article in journal (Refereed) Published
Abstract [en]

Due to the exponential growth of medical information in the form of, e.g., text, images, Electrocardiograms (ECGs), X-ray, 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 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.

Place, publisher, year, edition, pages
MDPI, 2022. Vol. 8, no 4, article id 104
Keywords [en]
indexing, retrieval, explainability, semantic, multimedia, feature graph, graph code, OF-THE-ART
National Category
Computer Sciences
Identifiers
URN: urn:nbn:se:bth-22891DOI: 10.3390/jimaging8040104ISI: 000785333300001PubMedID: 35448231OAI: oai:DiVA.org:bth-22891DiVA, id: diva2:1656566
Note

open access

Available from: 2022-05-06 Created: 2022-05-06 Last updated: 2022-05-06Bibliographically approved

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Cheddad, Abbas

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