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Concepts and Prototypes

Concepts and Prototypes

The human nervous system handles vast amounts of information by translating sensory stimuli into neural impulses, which the brain processes, creating thoughts expressed through language or stored as memories. The brain also synthesizes information from emotions and memories, which significantly influence thoughts and behaviors. This intricate process creates a comprehensive mental picture.
The brain organizes this information using concepts, which are mental categories grouping linguistic data,...

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Proto-Caps: interpretable medical image classification using prototype learning and privileged information.

Luisa Gallée^1,2, Catharina Silvia Lisson^2,3, Timo Ropinski^2,4

¹Experimental Radiology, Ulm University Medical Center, Germany, Ulm, Germany.

Peerj. Computer Science

|June 26, 2025

Summary

This summary is machine-generated.

We developed Proto-Caps, an explainable AI (xAI) model for medical image classification. It uses visual prototypes for understandable explanations, achieving high performance without sacrificing accuracy.

Keywords:

Capsule network Explainable AI Medical image classification Prototype learning

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Area of Science:

Artificial Intelligence
Medical Imaging
Computer Vision

Background:

Explainable AI (xAI) is crucial for high-risk applications like medicine.
Understanding AI decision-making is essential for diagnostic and therapeutic support systems.
Current xAI methods may lack intuitive explanations for clinical evaluation.

Purpose of the Study:

To introduce Proto-Caps, an intrinsically explainable model for image classification.
To provide intuitive and comprehensive explanations for AI-driven medical decisions.
To enhance the trustworthiness and performance of AI in medical contexts.

Main Methods:

Developed Proto-Caps, a novel intrinsically explainable model for image classification.
Utilized human-defined visual prototypes to explain model decisions.
Evaluated performance on two public medical image datasets.
Assessed explanation truthfulness by analyzing prediction-explanation alignment.

Main Results:

Proto-Caps demonstrated superior performance compared to existing explainable AI approaches.
The model's explanations, based on visual prototypes, were found to be truthful and aligned with predictions.
Optimal model settings were identified through extensive hyperparameter studies.
Incorporating explainability did not compromise model performance.

Conclusions:

Proto-Caps offers a promising approach to intrinsically explainable AI in medical image classification.
The use of visual prototypes enhances understanding and trust in AI diagnostic tools.
Combining xAI with high performance is achievable, paving the way for safer clinical AI adoption.