FunnyNodules: A Customizable Medical Dataset Tailored for Evaluating Explainable AI

TL;DR

FunnyNodules is a synthetic, customizable dataset of lung nodule-like shapes designed to evaluate and improve explainable AI models by providing detailed attribute annotations and controllable decision rules.

Contribution

We introduce FunnyNodules, a fully parameterized synthetic dataset that enables systematic analysis of attribute-based reasoning in medical AI models.

Findings

Supports evaluation of attribute-target relation learning

Facilitates interpretation of model performance issues

Enables analysis of attention alignment with attributes

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. The 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.