Deep Learning Model for Diagnosing Gastric Mucosal Lesions Using Endoscopic Images: Development, Validation, and Method Comparison
Joon Yeul Nam, Hyungjin Chung, Kyu Sung Choi, Hyuk Lee, Seung Jun Han, Tae Jun Kim, Hosim Soh, Eun Kang, Soo_jeong Cho, Jong Chul Ye, Jong Pil Im, Sang Gyun Kim, Yoon Jun Kim, Joo Sung Kim, Jung-Hwan Yoo, Hyunsoo Chung, Jeong-Hoon Lee
December 2021
Abstract
Endoscopic differential diagnoses of gastric mucosal lesions remain challenging. We aimed to develop and validate convolutional neural network-based artificial intelligence models-lesion detection, differential diagnosis, and invasion-depth models. The AI-DDx showed good diagnostic performance for both internal and external validation. The performance of the AI-DDx was better than that of the novice and intermediate endoscopists, but was comparable to the experts in the external validation set. The AI-ID showed fair performances in both internal and external validation sets, which were significantly better than EUS results performed by experts.
Publication
in Gastrointestinal Endoscopy
Hyungjin Chung
Ph.D. student - Generative Models & Inverse Problems
My research interests include, but is not restricted to developing efficient, modular deep generative models (diffusion models), and solving real-world inverse problems (MRI, tomography, microscopy, phase retrieval, etc.) with deep generative priors.