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E2-MIL: An explainable and evidential multiple instance learning framework for whole slide image classification.

Jiangbo Shi1, Chen Li1, Tieliang Gong1

  • 1School of Computer Science and Technology, Xi'an Jiaotong University, Xi'an, Shaanxi, 710049, China.

Medical Image Analysis
|August 11, 2024
PubMed
Summary
This summary is machine-generated.

This study introduces an explainable and evidential multiple instance learning (E²-MIL) framework to improve whole slide image (WSI) classification in computational pathology. The novel approach enhances tumor localization and provides reliable uncertainty estimation for better diagnostic accuracy.

Keywords:
HistopathologyMultiple instance learningUncertainty estimationWhole slide image analysis

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Area of Science:

  • Computational pathology
  • Artificial intelligence in medicine
  • Image analysis

Background:

  • Multiple instance learning (MIL) methods are standard for whole slide image (WSI) analysis.
  • Current MIL methods struggle with accurate tumor localization and reliable uncertainty estimation due to sparse slide-level supervision.
  • This limits interpretability and trustworthiness in computational pathology applications.

Purpose of the Study:

  • To develop an explainable and evidential multiple instance learning (E²-MIL) framework for whole slide image classification.
  • To address limitations in tumor region localization and predictive uncertainty estimation in existing MIL methods.
  • To enhance the interpretability and reliability of computational pathology tools.

Main Methods:

  • Proposed E²-MIL framework with three modules: detail-aware attention distillation (DAM), structure-aware attention refinement (SRM), and uncertainty-aware instance classification (UIC).
  • DAM utilizes complementary sub-bags for detailed attention knowledge and introduces masked self-guidance loss.
  • SRM models spatial relations for structure-aware attention maps, while UIC employs subjective logic theory for uncertainty estimation.

Main Results:

  • Demonstrated superior slide-level and instance-level classification performance across three large, multi-center datasets.
  • Achieved improved localization of tumor regions and enhanced interpretability.
  • Provided robust uncertainty estimation, increasing the reliability of prediction results.

Conclusions:

  • The E²-MIL framework significantly advances whole slide image classification in computational pathology.
  • The proposed method offers enhanced tumor localization, interpretability, and reliable uncertainty quantification.
  • E²-MIL shows strong potential for improving diagnostic accuracy and clinical decision-making.