Datasets:

Digital-Dermatology
/

CleanPatrick

@@ -19,140 +19,74 @@ configs:
 - config_name: off_topic_samples
   data_files: "off_topic_samples.csv"
 ---
-# CleanPatrick - Data Cleaning Benchmark
-<!-- Provide a quick summary of the dataset. -->
-This dataset card aims to be a base template for new datasets. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/datasetcard_template.md?plain=1).
-## Dataset Details
-### Dataset Description
-<!-- Provide a longer summary of what this dataset is. -->
-- **Curated by:** [More Information Needed]
-- **Funded by [optional]:** [More Information Needed]
-- **Shared by [optional]:** [More Information Needed]
-- **Language(s) (NLP):** [More Information Needed]
-- **License:** [More Information Needed]
-### Dataset Sources [optional]
-<!-- Provide the basic links for the dataset. -->
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
-## Uses
-<!-- Address questions around how the dataset is intended to be used. -->
-### Direct Use
-<!-- This section describes suitable use cases for the dataset. -->
-[More Information Needed]
-### Out-of-Scope Use
-<!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. -->
-[More Information Needed]
-## Dataset Structure
-<!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. -->
-[More Information Needed]
-## Dataset Creation
-### Curation Rationale
-<!-- Motivation for the creation of this dataset. -->
-[More Information Needed]
-### Source Data
-<!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). -->
-#### Data Collection and Processing
-<!-- This section describes the data collection and processing process such as data selection criteria, filtering and normalization methods, tools and libraries used, etc. -->
-[More Information Needed]
-#### Who are the source data producers?
-<!-- This section describes the people or systems who originally created the data. It should also include self-reported demographic or identity information for the source data creators if this information is available. -->
-[More Information Needed]
-### Annotations [optional]
-<!-- If the dataset contains annotations which are not part of the initial data collection, use this section to describe them. -->
-#### Annotation process
-<!-- This section describes the annotation process such as annotation tools used in the process, the amount of data annotated, annotation guidelines provided to the annotators, interannotator statistics, annotation validation, etc. -->
-[More Information Needed]
-#### Who are the annotators?
-<!-- This section describes the people or systems who created the annotations. -->
-[More Information Needed]
-#### Personal and Sensitive Information
-<!-- State whether the dataset contains data that might be considered personal, sensitive, or private (e.g., data that reveals addresses, uniquely identifiable names or aliases, racial or ethnic origins, sexual orientations, religious beliefs, political opinions, financial or health data, etc.). If efforts were made to anonymize the data, describe the anonymization process. -->
-[More Information Needed]
-## Bias, Risks, and Limitations
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
-[More Information Needed]
-### Recommendations
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
-Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.
-## Citation [optional]
-<!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. -->
-**BibTeX:**
-[More Information Needed]
-**APA:**
-[More Information Needed]
-## Glossary [optional]
-<!-- If relevant, include terms and calculations in this section that can help readers understand the dataset or dataset card. -->
-[More Information Needed]
-## More Information [optional]
-[More Information Needed]
-## Dataset Card Authors [optional]
-[More Information Needed]
-## Dataset Card Contact
-[More Information Needed]

 - config_name: off_topic_samples
   data_files: "off_topic_samples.csv"
 ---
+# CleanPatrick: A Benchmark for Data Cleaning in Dermatology Images
+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**.
+![CleanPatrick Teaser](./assets/CleanPatrick_Teaser_compressed.png)
+## 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.
+![Data Quality Issues](./assets/DataQualityIssues.png)
+## 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.
+![Benchmark](./assets/benchmark_results_barplot.png)
+## 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.