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Author Spotlight: Segmentation and VR for Advanced Neurovascular Interventions
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Concept-based reasoning in medical imaging.

Anuja Vats1, Marius Pedersen2, Ahmed Mohammed2,3

  • 1Department of Computer Science, NTNU, 2815, Gjøvik, Norway. anuja.vats@ntnu.no.

International Journal of Computer Assisted Radiology and Surgery
|May 25, 2023
PubMed
Summary
This summary is machine-generated.

This study introduces a method to explain AI predictions using concepts mined from unlabeled medical data. It helps define concepts for better model interpretability, especially when clear examples are scarce.

Keywords:
Biomedical imagingCapsule endoscopyDeep learningInterpretability

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Area of Science:

  • Artificial Intelligence
  • Medical Imaging Analysis
  • Machine Learning Interpretability

Background:

  • Concept-based reasoning is crucial for enhancing model interpretability.
  • Defining optimal concepts can be challenging, particularly in medical domains with limited clear instances.

Purpose of the Study:

  • To propose an approach for using organically mined concepts from unlabeled data to explain classifier predictions.
  • To address the challenge of defining good concepts for model interpretability in specialized domains.

Main Methods:

  • A Concept Mapping Module (CMM) was developed, featuring a convolutional encoder and a similarity block.
  • The CMM maps capsule endoscopy images to a latent vector and retrieves the closest aligning concept for explanation.

Main Results:

  • Abnormal capsule endoscopy images were explained using pathology-related concepts like inflammation, vascularity, ulcer, and polyp.
  • Non-pathological concepts such as anatomy, debris, and intestinal fluid were also identified.

Conclusions:

  • The proposed method enables concept-based explanations for AI predictions.
  • Leveraging the latent space of generative models like StyleGAN facilitates concept discovery and iterative refinement.