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README.md

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+# GastroNet-5M: A Multicenter Dataset for Developing Foundation Models in Gastrointestinal Endoscopy
+
+![Sample Images](figures/GastroNetSamples.jpg)  
+*Representative endoscopic images from the GastroNet-5M dataset.*
+
+---
+
+## 📖 Overview
+
+**GastroNet-5M** is the largest publicly available dataset of gastrointestinal endoscopic images to date.  
+It contains **4,820,653 unlabeled images** derived from approximately **500,000 unique endoscopic procedures**, collected across **eight Dutch hospitals** between **2012 and 2020**.  
+
+The dataset covers a wide range of procedures in both the **upper** and **lower gastrointestinal (GI) tract**, acquired using endoscopy systems from **all major manufacturers**.
+
+GastroNet-5M was created to **accelerate the development of deep learning systems** for gastrointestinal endoscopy, especially as a **pretraining dataset** for foundation or representation learning. It is expected to:
+- Improve diagnostic accuracy  
+- Enhance robustness to heterogeneous imaging data  
+- Reduce dependence on scarce annotated datasets  
+
+---
+
+## 📦 Dataset Structure
+
+- Images are provided in **PNG** format.  
+- Stored and subdivided into **zipped folders** of up to **10,000 images** each.  
+- A **representative subset of 1,000 images** is available for **direct download**.  
+- Each image has been **anonymized** to remove patient-identifying information and metadata.  
+- Central quality control included **manual review** to exclude irrelevant or sensitive images.  
+
+---
+
+## 🔗 Download
+
+The dataset can be accessed and requested via the official dataset portal:  
+👉 [https://cortex.thetavision.nl/dataset-provider/listing/1/](https://cortex.thetavision.nl/dataset-provider/listing/1/)
+
+The corresponding research article describing the dataset is available at:  
+📄 [ScienceDirect: GastroNet-5M Paper](https://www.sciencedirect.com/science/article/pii/S001650852505797X#fig1)
+
+---
+
+## 🧠 Applications
+
+GastroNet-5M serves as a foundation for:
+- **Pretraining** visual models for gastrointestinal endoscopy  
+- **Transfer learning** in downstream diagnostic tasks  
+- **Benchmarking** model performance under varied imaging conditions  
+- **Research** on data heterogeneity, robustness, and quality control  
+
+---
+
+## ⚙️ Data Anonymization
+
+All images were anonymized **on-site** at each hospital using proprietary software that:
+- Masks patient-identifying text and metadata  
+- Ensures no visual identifiers remain  
+
+Some images may contain **anonymization artifacts**.  
+A **central manual review** was conducted to maintain dataset integrity and compliance.
+
+---
+
+## 🧾 Citation
+
+Researchers using **GastroNet-5M** or related pretrained weights **must cite** the following papers:
+
+**Primary Dataset Paper**  
+> Jong, M. R., Boers, T. G. W., Fockens, K. N., Jukema, J. B., Kusters, C. H. J., Jaspers, T. J. M., van Eijck van Heslinga, R. A. H., Slooter, F. C., Struyvenberg, M. R., Bisschops, R., van der Putten, J. A., de With, P. H. N., van der Sommen, F., de Groof, A. J., & Bergman, J. J. (2025). **GastroNet-5M: A Multicenter Dataset for Developing Foundation Models in Gastrointestinal Endoscopy.** *Gastroenterology.* [https://doi.org/10.1053/j.gastro.2025.07.030](https://doi.org/10.1053/j.gastro.2025.07.030)
+
+**Foundation Model Pretraining Paper**  
+> Boers, T. G. W., Fockens, K. N., van der Putten, J. A., Jaspers, T. J. M., Kusters, C. H. J., Jukema, J. B., Jong, M. R., Struyvenberg, M. R., de Groof, J., Bergman, J. J., de With, P. H. N., & van der Sommen, F. (2024). **Foundation models in gastrointestinal endoscopic AI: Impact of architecture, pre-training approach and data efficiency.** *Medical Image Analysis, 98*, 103298. [https://doi.org/10.1016/j.media.2024.103298](https://doi.org/10.1016/j.media.2024.103298)
+
+---
+
+## 📚 Recommended Related Works
+
+Researchers are encouraged to consult these downstream studies leveraging **GastroNet-5M**:
+
+1. **Evaluation of an improved computer-aided detection system for Barrett's neoplasia**  
+   *Endoscopy (2025)* – [https://doi.org/10.1055/a-2642-7584](https://doi.org/10.1055/a-2642-7584)
+
+2. **Computer-aided quality control system for Barrett's esophagus endoscopy**  
+   *Endoscopy, 57(7), 709–716 (2025)* – [https://doi.org/10.1055/a-2537-3510](https://doi.org/10.1055/a-2537-3510)
+
+3. **Impact of standard enhancement settings of endoscopy systems on AI performance**  
+   *Endoscopy, 57(6), 602–610 (2025)* – [https://doi.org/10.1055/a-2530-1845](https://doi.org/10.1055/a-2530-1845)
+
+---
+
+## 🪪 License & Usage
+
+Use of GastroNet-5M is subject to the terms specified on the dataset portal.  
+Please review and comply with the **data access agreement** before downloading.