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Saliency-driven explainable deep learning in medical imaging: bridging visual explainability and statistical

Yusuf Brima1, Marcellin Atemkeng2

  • 1Computer Vision, Institute of Cognitive Science, Osnabrück University, Osnabrueck, D-49090, Lower Saxony, Germany. ybrima@uos.de.

Biodata Mining
|June 22, 2024
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Summary
This summary is machine-generated.

This study introduces a new framework for explaining deep learning in medical imaging. It combines visual and statistical methods to improve trust and adoption of AI in healthcare.

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Area of Science:

  • Medical Image Analysis
  • Artificial Intelligence in Healthcare
  • Explainable AI (XAI)

Background:

  • Deep learning models show promise in medical imaging but lack explainability, hindering clinical trust and adoption.
  • Current research often relies on visual inspection rather than quantitative analysis for attribution in medical imaging.
  • Explainability techniques are crucial for increasing stakeholder confidence in AI-driven healthcare solutions.

Purpose of the Study:

  • To propose and evaluate an image-based saliency framework for enhancing the explainability of deep learning models in medical image analysis.
  • To integrate qualitative and statistical quantitative assessments for a comprehensive evaluation of saliency methods.
  • To improve the transparency and trustworthiness of deep learning models in clinical settings.

Main Methods:

  • Utilized adaptive path-based gradient integration, gradient-free techniques, and class activation mapping (CAM) derivatives.
  • Applied the framework to brain tumor MRI and COVID-19 chest X-ray datasets using deep convolutional neural networks.
  • Employed Accuracy Information Curves (AICs) and Softmax Information Curves (SICs) for quantitative assessment of saliency effectiveness.

Main Results:

  • Visual inspection showed ScoreCAM, XRAI, GradCAM, and GradCAM++ produced clinically interpretable attribution maps, highlighting biomarkers and model biases.
  • Empirical evaluations indicated ScoreCAM and XRAI were effective in retaining relevant image regions, evidenced by higher AUC values.
  • Softmax Information Curves (SICs) revealed variability, with some random saliency masks outperforming established methods, stressing the need for combined evaluation metrics.

Conclusions:

  • Combining qualitative and quantitative approaches is essential for comprehensive evaluation and enhances the transparency and trustworthiness of deep learning models.
  • Selecting appropriate saliency methods tailored to specific medical imaging tasks is crucial for effective model explainability.
  • This work advances model explainability to foster greater trust and clinical adoption of AI in healthcare, with future work focusing on refining metrics and expanding modalities.