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A Study on the Reliability of Visual XAI Methods for X-Ray Images.

Jan Stodt1, Manav Madan1, Christoph Reich1

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This study evaluates visual explainability methods (Grad-CAM, Eigen-CAM) for YOLO object detection models in medical imaging. These techniques aim to improve trust and transparency in AI diagnostic tools by highlighting important image regions.

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Area of Science:

  • Artificial Intelligence
  • Medical Imaging
  • Computer Vision

Background:

  • YOLOv4 and YOLOv5 demonstrate high performance in medical diagnostics.
  • The black-box nature of YOLO models hinders adoption in healthcare due to trust and explainability concerns.

Purpose of the Study:

  • To evaluate visual explainability techniques (Grad-CAM, Eigen-CAM) for YOLO object detection models.
  • To assess the effectiveness of these methods in explaining AI decisions in medical diagnostics.

Main Methods:

  • Applied Grad-CAM and Eigen-CAM to YOLO models without requiring new layer implementations.
  • Evaluated method performance on the VinDrCXR Chest X-ray Abnormalities Detection dataset.

Main Results:

  • Grad-CAM and Eigen-CAM are applicable to YOLO models for generating visual explanations.
  • Discussion of the limitations of these methods in providing clear explanations to data scientists.

Conclusions:

  • Visual XAI methods like Grad-CAM and Eigen-CAM can be applied to YOLO models.
  • Further research is needed to address the limitations in explaining AI decisions to data scientists in medical applications.