|
|
--- |
|
|
license: cc-by-nc-4.0 |
|
|
language: |
|
|
- en |
|
|
tags: |
|
|
- data-centric |
|
|
- data-cleaning |
|
|
- quality-assessement |
|
|
size_categories: |
|
|
- 100K<n<1M |
|
|
task_categories: |
|
|
- image-classification |
|
|
pretty_name: CleanPatrick - Data Cleaning Benchmark |
|
|
configs: |
|
|
- config_name: Combined Annotations |
|
|
data_files: |
|
|
- split: test |
|
|
path: "combined_annotations.csv" |
|
|
- config_name: Label Errors |
|
|
data_files: |
|
|
- split: aggregated |
|
|
path: "label_errors.csv" |
|
|
- split: metadata |
|
|
path: "label_errors_meta.csv" |
|
|
- config_name: Near Duplicates |
|
|
data_files: |
|
|
- split: aggregated |
|
|
path: "near_duplicates.csv" |
|
|
- split: metadata |
|
|
path: "near_duplicates_meta.csv" |
|
|
- config_name: Off-Topic Samples |
|
|
data_files: |
|
|
- split: aggregated |
|
|
path: "off_topic_samples.csv" |
|
|
- split: metadata |
|
|
path: "off_topic_samples_meta.csv" |
|
|
--- |
|
|
# CleanPatrick: A Benchmark for Data Cleaning |
|
|
|
|
|
Welcome to **CleanPatrick**, the first large-scale benchmark designed for data cleaning in the image domain. |
|
|
Built on the Fitzpatrick17k dermatology dataset, CleanPatrick is a dataset for measuring the performance in detecting three major data quality issues: |
|
|
**off-topic samples**, **near-duplicates**, and **label errors**. |
|
|
|
|
|
 |
|
|
|
|
|
## Overview |
|
|
|
|
|
CleanPatrick consists of dermatological images annotated with over **500,000 binary labels** across **three data quality issues**: |
|
|
|
|
|
1. **Off-topic Samples**: Images that are irrelevant to the dataset, such as non-dermatological content or images with no visible skin diseases. |
|
|
2. **Near-Duplicates**: Highly similar images that may be caused by transformations, resolutions, or multiple views of the same condition. |
|
|
3. **Label Errors**: Images with incorrect labels, including mislabeling and rare conditions mistakenly classified. |
|
|
|
|
|
This dataset provides a realistic test bed to benchmark data cleaning strategies for image datasets, particularly in the medical domain. |
|
|
|
|
|
 |
|
|
|
|
|
## Key Features |
|
|
|
|
|
- **Real-World Contamination**: Unlike synthetic datasets with artificially induced errors, CleanPatrick contains naturally occurring issues that reflect true real-world contamination found in dermatology image datasets. |
|
|
- **Expert Annotations**: The dataset was annotated by medical crowd workers with expertise, and results were validated by medical professionals to ensure high-quality ground truth. |
|
|
- **Evaluation Framework**: Along with the dataset, CleanPatrick provides an evaluation framework for benchmarking methods to detect data quality issues, offering standardized metrics to compare various cleaning strategies. |
|
|
|
|
|
 |
|
|
|
|
|
## Dataset Details |
|
|
|
|
|
- **Total Number of Images**: 17,000 dermatology images |
|
|
- **Annotation Volume**: 500,000 annotations from 933 medical crowd workers |
|
|
- **Categories of Data Quality Issues**: |
|
|
- **Off-Topic**: 4% of the images |
|
|
- **Near-Duplicates**: 21% of the images |
|
|
- **Label Errors**: 22% of the images |
|
|
|
|
|
The dataset is available as a set of image-label pairs, with each image labeled according to whether it suffers from one or more of the three data quality issues. |
|
|
|
|
|
## Installation |
|
|
|
|
|
To load the dataset using the HuggingFace `datasets` library: |
|
|
|
|
|
```python |
|
|
from datasets import load_dataset |
|
|
|
|
|
dataset = load_dataset("Digital-Dermatology/CleanPatrick") |
|
|
``` |
|
|
|
|
|
## Citation |
|
|
If you use this dataset in your research, please cite the following paper: |
|
|
|
|
|
```bib |
|
|
@article{ |
|
|
groeger2025cleanpatrick, |
|
|
title={CleanPatrick: A Benchmark for Data Cleaning in Medical Imaging}, |
|
|
author={Gr\"oger, Fabian and Lionetti, Simone and Gottfrois, Philippe and Gonzalez-Jimenez, Alvaro |
|
|
and Amruthalingam, Ludovic and Goessinger, Elisabeth V. and Lindemann, Hanna and Bargiela, Marie |
|
|
and Hofbauer, Marie and Badri, Omar and Tschandl, Philipp and Koochek, Arash and Groh, Matthew |
|
|
and Navarini, Alexander A. and Pouly, Marc}, |
|
|
year={2025}, |
|
|
} |
|
|
``` |
|
|
|
|
|
## Acknowledgements |
|
|
We thank the medical crowd workers and domain experts who contributed to this dataset. |
|
|
The creation of CleanPatrick was made possible by their efforts and the valuable annotations provided. |
|
|
Additionally we want to thank Centaur Labs for their help in collecting large amounts of crowdsourced annotations. |
|
|
|
|
|
## License |
|
|
This dataset is released under the CC BY-NC-SA 3.0 license. |
|
|
|
