FunnyNodules: A Customizable Medical Dataset Tailored for Evaluating Explainable AI

• URL: http://arxiv.org/abs/2511.15481v2
• Date: Thu, 20 Nov 2025 14:00:08 GMT
• Title: FunnyNodules: A Customizable Medical Dataset Tailored for Evaluating Explainable AI
• Authors: Luisa Gallée, Yiheng Xiong, Meinrad Beer, Michael Götz,
• Abstract summary: We introduce FunnyNodules, a fully parameterized synthetic dataset for analysis of attribute-based reasoning in medical AI models.<n>The dataset generates lung nodule-like shapes with controllable visual attributes such as roundness, margin sharpness, and spiculation.<n>We demonstrate how FunnyNodules can be used in model-agnostic evaluations to assess whether models learn correct attribute-target relations.
• Score: 0.5249805590164902
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Densely annotated medical image datasets that capture not only diagnostic labels but also the underlying reasoning behind these diagnoses are scarce. Such reasoning-related annotations are essential for developing and evaluating explainable AI (xAI) models that reason similarly to radiologists: making correct predictions for the right reasons. To address this gap, we introduce FunnyNodules, a fully parameterized synthetic dataset designed for systematic analysis of attribute-based reasoning in medical AI models. The dataset generates abstract, lung nodule-like shapes with controllable visual attributes such as roundness, margin sharpness, and spiculation. Target class is derived from a predefined attribute combination, allowing full control over the decision rule that links attributes to the diagnostic class. We demonstrate how FunnyNodules can be used in model-agnostic evaluations to assess whether models learn correct attribute-target relations, to interpret over- or underperformance in attribute prediction, and to analyze attention alignment with attribute-specific regions of interest. The framework is fully customizable, supporting variations in dataset complexity, target definitions, class balance, and beyond. With complete ground truth information, FunnyNodules provides a versatile foundation for developing, benchmarking, and conducting in-depth analyses of explainable AI methods in medical image analysis.

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