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| Item Type: | Dataset |
|---|---|
| Title: | Image Dataset: A generative AI framework enables disease-specific tissue bioengineering |
| Creators Name: | Pestoni, Jeanine Chantal, Bahry, Ella, Hirzel, Kai, Heymann, Michael, Burgstaller, Gerald and Schmidt, Deborah |
| Abstract: | 1. Dataset Overview: This dataset accompanies the publication “A generative AI framework enables disease-specific tissue bioengineering” It contains original microscopy data, diffusion model–generated image datasets, and 3D-printable mesh files used in the study. 2. File Structure: The dataset consists of three compressed archives: original_images.zip – Raw microscopy images (60 TIFF files) used for quantitative and qualitative analysis.; generated_images.zip – 100 synthetic images per condition (healthy, fibrotic, emphysematous) generated by the diffusion model for validation and comparison.; stl_files.zip – STL meshes derived from original and generated data, prepared for two-photon bioprinting. 4. Creators and Citation: Creators: Ella Bahry, Jeanine C. Pestoni, Kai Hirzel, Taras Savchyn, Diana Porras-Gonzalez, Vera Getmanchuk, Martin Gregor, Tom Conlon, Ali-Önder Yildirim, Kyle Harrington, Dagmar Kainmüller, Michael Heymann, Deborah Schmidt, and Gerald Burgstaller. Citation: Please cite this Zenodo record using its specific version DOI. 5. License: Distributed under the Creative Commons Attribution 4.0 International License (CC BY 4.0). |
| Source: | Zenodo |
| Publisher: | CERN |
| Date: | 16 December 2025 |
| Official Publication: | https://doi.org/10.5281/zenodo.17280675 |
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