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Recent Advances in Explainable Artificial Intelligence for Magnetic Resonance Imaging.

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Explainable artificial intelligence (XAI) methods are emerging to demystify deep learning (DL) models used in magnetic resonance imaging (MRI) analysis. This survey reviews XAI applications for interpreting DL in MRI, addressing the need for trust in AI-driven medical diagnoses.

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Grad-CAMMR angiographyconvolutional neural networksdeep learningdiffusion MRIexplainable artificial intelligencefunctional MRImagnetic resonance imaging

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Machine Learning

Background:

  • Deep learning (DL) models enhance magnetic resonance imaging (MRI) analysis for medical diagnoses but often function as "black boxes", hindering expert trust.
  • Explainable artificial intelligence (XAI) is a growing field focused on demystifying DL models, crucial for their adoption in clinical settings.

Purpose of the Study:

  • To outline and review current applications of XAI techniques for interpreting DL models in MRI data analysis.
  • To provide a comprehensive overview of XAI frameworks and methods relevant to medical imaging.

Main Methods:

  • Introduction to common MRI data modalities and a history of DL models.
  • Elaboration on XAI frameworks and popular XAI methods.
  • Review of XAI applications in MRI across various human tissues/organs, including quantitative analysis of researcher insights and evaluation of XAI methods.

Main Results:

  • XAI techniques are still developing for MRI analysis, but numerous studies are exploring their use.
  • A quantitative analysis reveals researcher perspectives on current XAI techniques in MRI.

Conclusions:

  • This survey highlights recent advancements in XAI for explaining DL models in MRI applications.
  • Further development and validation of XAI methods are needed to build trust and facilitate the clinical integration of AI in medical imaging.