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Area of Science:

  • Artificial Intelligence
  • Medical Imaging
  • Natural Language Processing

Background:

  • Deep learning models are increasingly applicable in medical imaging due to advancements in natural language processing (NLP).
  • Researchers recognize the limitations of image-only analysis and the importance of integrating multimodal inputs.
  • A lack of comprehensive literature surveys hinders domain progress.

Purpose of the Study:

  • To review existing research perspectives, architectures, tasks, datasets, and performance measures in multimodal medical image analysis.
  • To provide a detailed summary of academic research for researchers and healthcare professionals.
  • To offer insights for future research directions in the field.

Main Methods:

  • Systematic review of current literature on multimodal medical image analysis.
  • Analysis of deep learning architectures, tasks, datasets, and performance metrics.
  • Identification of research trends and future research opportunities.

Main Results:

  • The study reviews various research perspectives and methodologies in multimodal medical image analysis.
  • Key architectures, tasks, datasets, and performance measures are examined.
  • Identified limitations and future research directions are presented.

Conclusions:

  • Comprehensive review of multimodal deep learning in medical imaging is crucial for advancing the field.
  • Integrating multimodal inputs alongside medical images offers significant potential.
  • This work provides a foundation for future research and development in AI-driven medical image analysis.