license: cc-by-2.5
task_categories:
- image-classification
- image-segmentation
language:
- en
tags:
- biology
- X-Ray
size_categories:
- 1K<n<10K
Dataset Card for FracAtlas
The "FracAtlas" dataset is a collection of musculoskeletal radiographs for fracture classification, localization, and segmentation. It includes 4,083 X-Ray images with annotations in multiple formats.The annotations include labels, classes, and etc. The dataset is intended for use in deep learning tasks in medical imaging, specifically targeting the understanding of bone fractures. It is freely available under a CC-BY 4.0 license.
Dataset Details
Dataset Description
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- License: cc-by-2.5
Dataset Sources [optional]
- Repository: https://figshare.com/articles/dataset/The_dataset/22363012
- Paper [optional]: https://www.nature.com/articles/s41597-023-02432-4
Uses
Direct Use
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Dataset Structure
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Dataset Creation
Curation Rationale
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Source Data
The data is originally authored by authored by Iftekharul Abedeen, Md. Ashiqur Rahman, Fatema Zohra Prottyasha, Tasnim Ahmed, Tareque Mohmud Chowdhury, Swakkhar Shatabda.
Data Collection and Processing
The FracAtlas dataset was accumulatively collected over 14,000 X-ray scans from several medical facilities across Bangladesh, with a substantial portion sourced from Lab-Aid Medical Center. Following collection, a meticulous data cleaning phase was undertaken to ensure the integrity and usability of the scans. Finally, the dataset was enhanced with detailed annotations. Ethical approval was secured, ensuring the confidentiality of patient data, and all participants provided informed consent. The collection process was designed to be non-intrusive to the standard diagnostic and treatment protocols of the involved hospitals.
Annotations
The dataset includes 4,083 images that have been manually annotated for bone fracture classification, localization, and segmentation with the help of 2 expert radiologists. Annotations have later been verified and merged by an orthopedist using the open-source labeling platform, makesense.ai. The primary type of annotation generated for bone fracture was in Common Objects in Context (COCO) format. Other information were loosely saved in YOLO, Pascal VOC, and Visual Geometry Group (VGG) format.
Personal and Sensitive Information
All personally identifiable information in the gathered data has been removed, and theprocess was administered according to the Institutional Research Ethics Board of United International University.
Bias, Risks, and Limitations
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Recommendations
Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.
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