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Explainable AI for computational pathology identifies model limitations and tissue biomarkers.

Jakub R Kaczmarzyk1,2,3, Joel H Saltz1, Peter K Koo2

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|September 16, 2024
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Summary
This summary is machine-generated.

HIPPO, an explainable AI framework, enhances trust in digital pathology by generating image counterfactuals for quantitative model evaluation. It uncovers limitations missed by traditional metrics, improving transparency and reliability in clinical AI applications.

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Area of Science:

  • Computational pathology
  • Artificial Intelligence
  • Medical Imaging

Background:

  • Deep learning models in digital pathology lack transparency, hindering clinical trust and adoption.
  • Explainable AI (XAI) is crucial for enhancing model reliability and understanding decision-making processes.

Purpose of the Study:

  • To introduce HIPPO, an XAI framework for digital pathology.
  • To enable quantitative hypothesis testing, bias detection, and model evaluation using image counterfactuals.
  • To improve the trustworthiness and clinical adoption of AI models in pathology.

Main Methods:

  • HIPPO systematically modifies tissue regions in whole slide images to create counterfactuals.
  • The framework was applied to breast metastasis detection, cancer prognostication, and glioma mutation classification.
  • HIPPO's performance was compared against traditional metrics and attention-based XAI methods.

Main Results:

  • HIPPO identified critical model limitations in metastasis detection missed by standard metrics and attention.
  • For prognostication, HIPPO provided more nuanced insights into tissue drivers than attention.
  • HIPPO aided hypothesis generation for melanoma immunotherapy and improved identification of false negatives in mutation classification.

Conclusions:

  • HIPPO expands the XAI toolkit for computational pathology, offering deeper insights into model behavior.
  • The framework supports the development, deployment, and regulation of trustworthy AI in digital pathology.
  • HIPPO promotes broader adoption of AI in clinical and research settings by enhancing model transparency.