ViConEx-Med: Visual Concept Explainability via Multi-Concept Token Transformer for Medical Image Analysis

• URL: http://arxiv.org/abs/2510.10174v1
• Date: Sat, 11 Oct 2025 11:24:47 GMT
• Title: ViConEx-Med: Visual Concept Explainability via Multi-Concept Token Transformer for Medical Image Analysis
• Abstract summary: ViConEx-Med is a transformer-based framework for visual concept explainability.<n>Our method produces concept-level localization maps while maintaining high predictive accuracy.<n> Experiments on both synthetic and real-world medical datasets demonstrate that ViConEx-Med outperforms prior concept-based models.
• Score: 5.887969742827489
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Concept-based models aim to explain model decisions with human-understandable concepts. However, most existing approaches treat concepts as numerical attributes, without providing complementary visual explanations that could localize the predicted concepts. This limits their utility in real-world applications and particularly in high-stakes scenarios, such as medical use-cases. This paper proposes ViConEx-Med, a novel transformer-based framework for visual concept explainability, which introduces multi-concept learnable tokens to jointly predict and localize visual concepts. By leveraging specialized attention layers for processing visual and text-based concept tokens, our method produces concept-level localization maps while maintaining high predictive accuracy. Experiments on both synthetic and real-world medical datasets demonstrate that ViConEx-Med outperforms prior concept-based models and achieves competitive performance with black-box models in terms of both concept detection and localization precision. Our results suggest a promising direction for building inherently interpretable models grounded in visual concepts. Code is publicly available at https://github.com/CristianoPatricio/viconex-med.

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