serag-ai/Synthetic-Ophthalmology-Images

Investigating Diffusion Models Across Diverse Imaging Modalities: Synthetic Data for Retinal OCT Images

This repository contains a fully synthetic dataset used for the study, Is Synthetic Data Generation Effective in Maintaining Clinical Biomarkers? Investigating Diffusion Models Across Diverse Imaging Modalities.

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Abstract:

The integration of recent technologies in medical imaging has become a cornerstone of modern healthcare, facilitating detailed analysis of internal anatomy and pathology. Traditional methods, however, often grapple with data-sharing restrictions due to privacy concerns. Emerging techniques in artificial intelligence offer innovative solutions to overcome these constraints, with synthetic data generation enabling the creation of realistic medical imaging datasets, but the preservation of critical hidden medical biomarkers is an open question.

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Data

This dataset is fully synthetic and was generated using diffusion models for the Retinal OCT Images (optical coherence tomography) classification task.

• Class CNV
• Class DME
• Class DRUSEN
• Class NORMAL

The source real dataset that served as the basis for this work can be found Retinal OCT Images (optical coherence tomography).

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Code

The code and detailed instructions for using this dataset are available on GitHub: I-SynMed.

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Other Datasets

We have made additional synthetic datasets publicly available for research purposes. Explore them below:

1. Synthetic Data for Radiology(Pneumonia Classifcation): Synthetic Data in Pneumonia
2. Synthetic Data for Breast Cancer Histopathology: Synthetic Data in Breast Cancer Histopathology

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Citation

If you use the data or code from this repository, please cite the following publication:

@article{Hosseini2024,
  author = {Hosseini, A. and Serag, A.},
  title = {Is Synthetic Data Generation Effective in Maintaining Clinical Biomarkers? Investigating Diffusion Models Across Diverse Imaging Modalities},
  journal = {Frontiers in Artificial Intelligence},
  year = {2024},
  volume = {7},
  doi = {10.3389/frai.2024.1454441},
  url = {https://www.frontiersin.org/journals/artificial-intelligence/articles/10.3389/frai.2024.1454441/abstract}
}

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🙏 Acknowledgements

We extend our sincere gratitude to the developers of 2D Medical Image Synthesis Using Transformer-Based Denoising Diffusion Probabilistic Model, whose open-source contributions provided a critical foundation for this project.