This project, ContrastCTGAN, is an end-to-end deep learning framework designed to enhance non-contrast computed tomography (CT) images for improved pulmonary embolism identification. By using a generative adversarial network (GAN), the framework generates enhanced CT images to facilitate more accurate diagnostic capabilities and clinical research advancements in medical imaging.
This project and its underlying technology are currently under patent application. As such, any use, reproduction, modification, distribution, or disclosure of this code and associated documentation is strictly prohibited without explicit, written permission from the authors or authorized representatives.
Unauthorized usage, distribution, or replication of any part of this code may result in legal action, including but not limited to infringement of pending intellectual property rights.
For further inquiries, including licensing and authorized usage, please contact:
- Prof. Yunsik Son, PhD
Division of AI Software Convergence, Dongguk University, Seoul, Republic of Korea
Email: sonbug@dongguk.edu - Prof. Jinkyeong Park, PhD
Department of Pulmonary, Allergy and Critical Care Medicine, Kyung Hee University Hospital at Gangdong, School of Medicine, Kyung Hee University, Seoul, Republic of Korea
Email: pjk3318@gmail.com