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Merge pull request #47 from raidionics/readme_update
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README.md
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This repository contains the AeroPath dataset described in ["_AeroPath: An airway segmentation benchmark dataset with challenging pathology_"](https://arxiv.org/abs/2311.01138). A web application was also developed in the study, to enable users to easily test our deep learning model on their own data. The application was developed using [Gradio](https://www.gradio.app) for the frontend and the segmentation is performed using the [Raidionics](https://raidionics.github.io/) backend.
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The dataset can be accessed
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## [Dataset structure](https://github.com/raidionics/AeroPath#data-structure)
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βββ AeroPath.zip
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βββ README.md
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βββ AeroPath/
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```
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## [Demo](https://github.com/raidionics/AeroPath#demo) <a target="_blank" href="https://huggingface.co/spaces/andreped/AeroPath"><img src="https://img.shields.io/badge/π€%20Hugging%20Face-Spaces-yellow.svg"></a>
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This repository contains the AeroPath dataset described in ["_AeroPath: An airway segmentation benchmark dataset with challenging pathology_"](https://arxiv.org/abs/2311.01138). A web application was also developed in the study, to enable users to easily test our deep learning model on their own data. The application was developed using [Gradio](https://www.gradio.app) for the frontend and the segmentation is performed using the [Raidionics](https://raidionics.github.io/) backend.
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The dataset can be accessed [here](https://zenodo.org/records/10069289).
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## [Dataset structure](https://github.com/raidionics/AeroPath#data-structure)
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βββ AeroPath.zip
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βββ README.md
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βββ AeroPath/
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βββ 1/
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β βββ 1_CT_HR.nii.gz
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β βββ 1_CT_HR_label_airways.nii.gz
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β βββ 1_CT_HR_label_lungs.nii.gz
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βββ [...]
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βββ 27/
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βββ 27_CT_HR.nii.gz
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βββ 27_CT_HR_label_airways.nii.gz
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βββ 27_CT_HR_label_lungs.nii.gz
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```
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## [Demo](https://github.com/raidionics/AeroPath#demo) <a target="_blank" href="https://huggingface.co/spaces/andreped/AeroPath"><img src="https://img.shields.io/badge/π€%20Hugging%20Face-Spaces-yellow.svg"></a>
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