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Area of Science:

  • Artificial Intelligence
  • Medical Imaging Analysis
  • Neuroscience

Background:

  • Deep learning models excel in analyzing complex medical images.
  • Understanding model predictions (explainability) is crucial for clinical trust.
  • Existing Shapley value explanation (SHAP) methods face computational and noise sensitivity challenges.

Purpose of the Study:

  • To develop an uncertainty estimation method for SHAP-based feature importance in medical imaging.
  • To improve the reliability and robustness of SHAP explanations for high-dimensional data.
  • To explore the potential of uncertainty-aware SHAP for biomarker discovery in neuroimaging.

Main Methods:

  • Proposed an uncertainty estimation technique for SHAP feature importance.
  • Provided theoretical justifications within the Shapley value framework.
  • Validated the method on the MNIST dataset and a public neuroimaging dataset.

Main Results:

  • The developed method quantifies uncertainty in SHAP feature importance.
  • Demonstrated improved robustness compared to standard SHAP.
  • Showcased potential for identifying disease-related biomarkers in neuroimaging data.

Conclusions:

  • The proposed uncertainty estimation enhances the trustworthiness of SHAP for medical deep learning.
  • This approach offers a more reliable tool for interpreting complex medical image analyses.
  • Facilitates the discovery of novel biomarkers from neuroimaging data.