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Introduction of an Integrated Pathology Image Management, Artificial Intelligence, and Reporting System
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Validating Automatic Concept-Based Explanations for AI-Based Digital Histopathology.

Daniel Sauter1, Georg Lodde2, Felix Nensa3,4

  • 1Department of Computer Science, Fachhochschule Dortmund, 44227 Dortmund, Germany.

Sensors (Basel, Switzerland)
|July 27, 2022
PubMed
Summary
This summary is machine-generated.

Automated Concept-based Explanation (ACE) offers a valid approach to understanding deep learning in digital histopathology, revealing biases missed by conventional methods. This explainable AI tool enhances transparency and classification performance in skin cancer detection.

Keywords:
basal cell carcinomabias discoverycomputational histopathologyconcept attributionexplainable AIintra-epidermal carcinomamalignant melanomasaliency mapsquamous cell carcinoma

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Area of Science:

  • Digital pathology
  • Artificial Intelligence
  • Computer Vision

Background:

  • Deep learning in digital histopathology faces challenges like bias, impacting transparency and performance.
  • Existing explainable AI (XAI) tools have limitations in complexity and applicability.
  • Automated Concept-based Explanation (ACE) is a novel XAI method for extracting visual concepts from image data.

Purpose of the Study:

  • To evaluate the technical validity of ACE in digital histopathology.
  • To compare ACE with Guided Gradient-weighted Class Activation Mapping (Grad-CAM).
  • To assess ACE's ability to detect and explain biases in histopathological deep learning models.

Main Methods:

  • Developed and studied five Convolutional Neural Networks (CNNs) across four skin cancer datasets.
  • Applied ACE to extract visual concepts and compared explanations with Guided Grad-CAM.
  • Followed design science principles for technical validation of ACE.

Main Results:

  • ACE demonstrated technical validity for insights into histopathological CNN decision-making.
  • ACE successfully identified various biases, including class sampling ratio, measurement, sampling, and class-correlated biases.
  • Complementary use of ACE and Guided Grad-CAM provided enhanced benefits, though intuitiveness varied across scenarios.

Conclusions:

  • ACE is a valuable tool for uncovering hidden biases and improving the understanding of deep learning models in digital histopathology.
  • ACE offers explanations that can surpass those of conventional methods like Grad-CAM.
  • Practical solutions were proposed for technical challenges associated with ACE implementation.