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Explainable Multimedia Feature Fusion for Medical Applications.

Stefan Wagenpfeil1, Paul Mc Kevitt2, Abbas Cheddad3

  • 1Faculty of Mathematics and Computer Science, University of Hagen, Universitätsstrasse 1, 58097 Hagen, Germany.

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Managing diverse medical data is challenging. This study introduces Graph Codes and Explainable Graph Codes for efficient multimedia analysis, improving medical diagnosis and data understanding.

Keywords:
explainabilityfeature graphgraph codeindexingmultimediaretrievalsemantic

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Area of Science:

  • Medical Informatics
  • Computer Science
  • Data Science

Background:

  • Exponential growth of diverse medical data (text, images, ECGs, X-rays, multimedia) poses significant data management challenges.
  • Homogeneous feature extraction and representation from varied medical data formats are critical for advanced applications.

Purpose of the Study:

  • To adapt multimedia information retrieval (MMIR) frameworks for medical applications.
  • To demonstrate the benefits of Multimedia Feature Graphs (MMFG) and Graph Codes for medical data analysis.
  • To introduce explainable AI (XAI) methods for medical multimedia interpretation.

Main Methods:

  • Extension and adaptation of multimedia processing techniques for medical data.
  • Utilization of Multimedia Feature Graphs (MMFG) and Graph Codes for indexing and querying.
  • Modification of the Term Frequency Inverse Document Frequency (TFIDF) algorithm for medical context, including value ranges and Boolean operations.
  • Development of explainability features for Graph Codes.

Main Results:

  • Graph Codes provide efficient indexing and querying for medical multimedia data.
  • Modified TFIDF algorithm enhances feature relevance and supports medical-specific operations.
  • The framework enables similarity calculations, recommendations, and automated reasoning for diagnostics.
  • Explainable Graph Codes facilitate understanding of complex medical multimedia information.

Conclusions:

  • Graph Codes offer novel querying capabilities for medical diagnosis.
  • Explainable Graph Codes significantly improve the interpretability of medical multimedia data.
  • The proposed framework enhances medical data management and diagnostic support systems.