FunnyNodules

Name: FunnyNodules
Creator: 乌尔姆大学医学中心实验放射学系

FunnyNodules是一个参数化合成肺结节医学影像数据集，用于评估可解释AI模型的属性推理正确性。

乌尔姆大学医学中心实验放射学系

arXiv

2025-11-19 更新

医学影像肺结节医学影像

基本信息

模态

医学影像

创建/更新时间

2025-11-19

资源简介

FunnyNodules是由乌尔姆大学医学中心开发的参数化合成医学影像数据集，专门用于评估可解释人工智能模型。该数据集通过可控算法生成模拟肺结节形态的灰度图像，包含圆形度、边缘锐度等六类视觉属性标注，支持自定义数据复杂度与分类规则。其生成过程基于参数化建模技术，通过几何变换与噪声注入确保样本多样性，同时保持属性精确控制。该数据集主要应用于医疗影像分析领域，旨在解决可解释AI模型在属性推理正确性、注意力对齐等维度的系统性评估难题。

原始链接

https://github.com/XRad-Ulm/FunnyNodules

arXiv 论文 →

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下载信息

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暂未开放

千方医数集，医疗数据集部分，是为社区服务的公开医疗数据集搜索引擎，并不存储或者下载原始的任何数据。如果您有其他医疗数据需求，可以和客服联系，或者下工单。我们有强大的三甲医疗机构帮助您提供个性化的医疗数据定制、采集、标注服务。

使用方式

数据集获取

git clone https://github.com/XRad-Ulm/FunnyNodules.git

curl -L -o repo.zip https://github.com/XRad-Ulm/FunnyNodules/archive/refs/heads/main.zip
unzip repo.zip

源站 README 摘录（使用方式）

FunnyNodules

FunnyNodules: A Customizable Medical Dataset Tailored for Evaluating Explainable AI. L. Gallée, Y. Xiong, M. Beer, M. Götz (2025)

FunnyNodules is a synthetic, vision-based dataset inspired by medical image interpretation. It is designed to support systematic, controlled, and model-agnostic evaluation of explainability methods. The dataset generation framework enables high customizability, scalability, and fine-grained manipulation of factors of variation, making it well suited for benchmarking how different models reason about image features.
The FunnyNodules dataset, employed in the experiments of this study, comprises abstract nodules described by six visual attributes:

roundness 1-round, 5-oval
spiculation 1-none, 5-marked
edge sharpness 1-sharp, 5-soft
size 1-small, 5-big
intensity 1-dark, 5-bright
internal structure 0-absent, 1-present
The target class is defined based on combinations of these attributes. However, a major advantage of the synthetic FunnyNodules framework is the ability to implement different scales and rules as desired (def calculate_target in dataset_generator.py).
Figure 1: Full control of image generation allows indepth analysis of attribute-target reasoning.

Key features of FunnyNodules

🔬 Explainability-Focused: Developed for analyzing AI reasoning methods, especially attribute-based models.
🎛️ Controllable: Easily adjust attributes.
🎯 Flexible Rules: Implement your own rules for class label.
🏷️ Fully Labeled: Each sample comes with complete, structured annotations for all attributes, also attribute ROIs:

Figure 2: Ground-truth masks are being created during image generation and enable evaluation of attention in attribute prediction.

Figure 3: Analysis of models’’ reasoning, see test_reasoning.py. Left

数据加载示例（图像类）

from PIL import Image
import glob, os

files = (glob.glob(os.path.join(path, "**", "*.png"), recursive=True)
       + glob.glob(os.path.join(path, "**", "*.jpg"), recursive=True)
       + glob.glob(os.path.join(path, "**", "*.tif"), recursive=True))
print("图像文件数:", len(files))
img = Image.open(files[0]); print("尺寸/模式:", img.size, img.mode)

# torchvision Dataset 方式:
# from torchvision import datasets
# ds = datasets.ImageFolder(path)  # 要求 子目录=类别