GastroHUN an Endoscopy Dataset of Complete Systematic Screening Protocol for the Stomach
Abstract Endoscopy is vital for detecting and diagnosing gastrointestinal diseases. Systematic examination protocols are key to enhancing detection, particularly for the early identification of premalignant conditions. Publicly available endoscopy image databases are crucial for machine learning res...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-01-01
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| Series: | Scientific Data |
| Online Access: | https://doi.org/10.1038/s41597-025-04401-5 |
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| author | Diego Bravo Juan Frias Felipe Vera Juan Trejos Carlos Martínez Martín Gómez Fabio González Eduardo Romero |
| author_facet | Diego Bravo Juan Frias Felipe Vera Juan Trejos Carlos Martínez Martín Gómez Fabio González Eduardo Romero |
| author_sort | Diego Bravo |
| collection | DOAJ |
| description | Abstract Endoscopy is vital for detecting and diagnosing gastrointestinal diseases. Systematic examination protocols are key to enhancing detection, particularly for the early identification of premalignant conditions. Publicly available endoscopy image databases are crucial for machine learning research, yet challenges persist, particularly in identifying upper gastrointestinal anatomical landmarks to ensure effective and precise endoscopic procedures. However, many existing datasets have inconsistent labeling and limited accessibility, leading to biased models and reduced generalizability. This paper introduces GastroHUN, an open dataset documenting stomach screening procedures based on a systematic protocol. GastroHUN includes 8,834 images from 387 patients and 4,729 labeled video sequences, all annotated by four experts. The dataset covers 22 anatomical landmarks in the stomach and includes an additional category for unqualified images, making it a valuable resource for AI model development. By providing a robust public dataset and baseline deep learning models for image and sequence classification, GastroHUN serves as a benchmark for future research and aids in the development of more effective algorithms. |
| format | Article |
| id | doaj-art-46303df4f05a4d908ce9805eadb28425 |
| institution | Kabale University |
| issn | 2052-4463 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Data |
| spelling | doaj-art-46303df4f05a4d908ce9805eadb284252025-01-19T12:09:50ZengNature PortfolioScientific Data2052-44632025-01-0112111410.1038/s41597-025-04401-5GastroHUN an Endoscopy Dataset of Complete Systematic Screening Protocol for the StomachDiego Bravo0Juan Frias1Felipe Vera2Juan Trejos3Carlos Martínez4Martín Gómez5Fabio González6Eduardo Romero7Universidad Nacional de ColombiaUniversidad Nacional de Colombia, Medicina InternaUniversidad Nacional de Colombia, Medicina InternaUniversidad Nacional de Colombia, Medicina InternaUniversidad Nacional de Colombia, Medicina InternaUniversidad Nacional de Colombia, Medicina InternaUniversidad Nacional de ColombiaUniversidad Nacional de ColombiaAbstract Endoscopy is vital for detecting and diagnosing gastrointestinal diseases. Systematic examination protocols are key to enhancing detection, particularly for the early identification of premalignant conditions. Publicly available endoscopy image databases are crucial for machine learning research, yet challenges persist, particularly in identifying upper gastrointestinal anatomical landmarks to ensure effective and precise endoscopic procedures. However, many existing datasets have inconsistent labeling and limited accessibility, leading to biased models and reduced generalizability. This paper introduces GastroHUN, an open dataset documenting stomach screening procedures based on a systematic protocol. GastroHUN includes 8,834 images from 387 patients and 4,729 labeled video sequences, all annotated by four experts. The dataset covers 22 anatomical landmarks in the stomach and includes an additional category for unqualified images, making it a valuable resource for AI model development. By providing a robust public dataset and baseline deep learning models for image and sequence classification, GastroHUN serves as a benchmark for future research and aids in the development of more effective algorithms.https://doi.org/10.1038/s41597-025-04401-5 |
| spellingShingle | Diego Bravo Juan Frias Felipe Vera Juan Trejos Carlos Martínez Martín Gómez Fabio González Eduardo Romero GastroHUN an Endoscopy Dataset of Complete Systematic Screening Protocol for the Stomach Scientific Data |
| title | GastroHUN an Endoscopy Dataset of Complete Systematic Screening Protocol for the Stomach |
| title_full | GastroHUN an Endoscopy Dataset of Complete Systematic Screening Protocol for the Stomach |
| title_fullStr | GastroHUN an Endoscopy Dataset of Complete Systematic Screening Protocol for the Stomach |
| title_full_unstemmed | GastroHUN an Endoscopy Dataset of Complete Systematic Screening Protocol for the Stomach |
| title_short | GastroHUN an Endoscopy Dataset of Complete Systematic Screening Protocol for the Stomach |
| title_sort | gastrohun an endoscopy dataset of complete systematic screening protocol for the stomach |
| url | https://doi.org/10.1038/s41597-025-04401-5 |
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