Update app.py

app.py

@@ -213,9 +213,7 @@ if __name__ == "__main__":
 
         gr.Markdown("""
                     # Chest X-ray HybridGNet Segmentation.
-                    
-                    Demo of the HybridGNet model introduced in "Improving anatomical plausibility in medical image segmentation via hybrid graph neural networks: applications to chest x-ray analysis."
-                    
+                          
                     Instructions:
                     1. Upload a chest X-ray image (PA or AP) in PNG or JPEG format.
                     2. Click on "Segment Image".
@@ -240,47 +238,7 @@ if __name__ == "__main__":
                     image_output = gr.Image(type="filepath", height=750)
                     results = gr.File()       
         
-        gr.Markdown("""
-                    If you use this code, please cite:
-                    
-                    ```
-                    @article{gaggion2022TMI,
-                        doi = {10.1109/tmi.2022.3224660},
-                        url = {https://doi.org/10.1109%2Ftmi.2022.3224660},
-                        year = 2022,
-                        publisher = {Institute of Electrical and Electronics Engineers ({IEEE})},
-                        author = {Nicolas Gaggion and Lucas Mansilla and Candelaria Mosquera and Diego H. Milone and Enzo Ferrante},
-                        title = {Improving anatomical plausibility in medical image segmentation via hybrid graph neural networks: applications to chest x-ray analysis},
-                        journal = {{IEEE} Transactions on Medical Imaging}
-                    }
-                    ```
-                    
-                    This model was trained following the procedure explained on:
-                    
-                    ```
-                    @INPROCEEDINGS{gaggion2022ISBI,
-                        author={Gaggion, Nicolás and Vakalopoulou, Maria and Milone, Diego H. and Ferrante, Enzo},
-                        booktitle={2023 IEEE 20th International Symposium on Biomedical Imaging (ISBI)}, 
-                        title={Multi-Center Anatomical Segmentation with Heterogeneous Labels Via Landmark-Based Models}, 
-                        year={2023},
-                        volume={},
-                        number={},
-                        pages={1-5},
-                        doi={10.1109/ISBI53787.2023.10230691}
-                    }
-                    ```
-
-                    Example images extracted from Wikipedia, released under:
-                    1. CC0 Universial Public Domain. Source: https://commons.wikimedia.org/wiki/File:Normal_posteroanterior_(PA)_chest_radiograph_(X-ray).jpg
-                    2. Creative Commons Attribution-Share Alike 4.0 International. Source: https://commons.wikimedia.org/wiki/File:Chest_X-ray.jpg
-                    3. Creative Commons Attribution 3.0 Unported. Source https://commons.wikimedia.org/wiki/File:Implantable_cardioverter_defibrillator_chest_X-ray.jpg
-                    4. Creative Commons Attribution-Share Alike 3.0 Unported. Source: https://commons.wikimedia.org/wiki/File:Medical_X-Ray_imaging_PRD06_nevit.jpg
-                    
-                    Author: Nicolás Gaggion
-                    Website: [ngaggion.github.io](https://ngaggion.github.io/)
-                    
-                    """)
-        
+       
 
         clear_button.click(lambda: None, None, image_input, queue=False)
         clear_button.click(lambda: None, None, image_output, queue=False)