Deeply Self-Supervising Edge-to-Contour Neural Network Applied to Liver Segmentation. - Minyoung Chung; Jingyu Lee; Minkyung Lee; Jeongjin Lee; Yeong-Gil Shin (CoRR, 2018)

Automatic Registration between Cone-Beam CT and Scanned Surface via Deep-Pose Regression Neural Networks and Clustered Similarities. - Minyoung Chung; Jingyu Lee; Wisoo Song; Youngchan Song; Il-Hyung Yang; Jeongjin Lee; Yeong-Gil Shin (CoRR, 2019)

2-D chemical structure image-based in silico model to predict agonist activity for androgen receptor. - Myeong-Sang Yu; Jingyu Lee; Yongmin Lee; Dokyun Na (BMC Bioinform., 2020)

Pose-aware instance segmentation framework from cone beam CT images for tooth segmentation. - Minyoung Chung; Minkyung Lee; Jioh Hong; Sanguk Park; Jusang Lee; Jingyu Lee; Il-Hyung Yang; Jeongjin Lee; Yeong-Gil Shin (Comput. Biol. Medicine, 2020)

Deeply self-supervised contour embedded neural network applied to liver segmentation. - Minyoung Chung; Jingyu Lee; Minkyung Lee; Jeongjin Lee; Yeong-Gil Shin (Comput. Methods Programs Biomed., 2020)

Pose-Aware Instance Segmentation Framework from Cone Beam CT Images for Tooth Segmentation. - Minyoung Chung; Minkyung Lee; Jioh Hong; Sanguk Park; Jusang Lee; Jingyu Lee; Jeongjin Lee; Yeong-Gil Shin (CoRR, 2020)

Liver Segmentation in Abdominal CT Images via Auto-Context Neural Network and Self-Supervised Contour Attention. - Minyoung Chung; Jingyu Lee; Jeongjin Lee; Yeong-Gil Shin (CoRR, 2020)

Automatic Registration Between Dental Cone-Beam CT and Scanned Surface via Deep Pose Regression Neural Networks and Clustered Similarities. - Minyoung Chung; Jingyu Lee; Wisoo Song; Youngchan Song; Il-Hyung Yang; Jeongjin Lee; Yeong-Gil Shin (IEEE Trans. Medical Imaging, 2020)

ParallelFusion: Towards Maximum Utilization of Mobile GPU for DNN Inference. - Jingyu Lee; Yunxin Liu 001; Youngki Lee 001 (EMDL@MobiSys, 2021)

Liver segmentation in abdominal CT images via auto-context neural network and self-supervised contour attention. - Minyoung Chung; Jingyu Lee; Sanguk Park; Chae-Eun Lee; Jeongjin Lee; Yeong-Gil Shin (Artif. Intell. Medicine, 2021)

In silico methods and tools for drug discovery. - Bilal Shaker; Sajjad Ahmad; Jingyu Lee; Chanjin Jung; Dokyun Na (Comput. Biol. Medicine, 2021)

Band: coordinated multi-DNN inference on heterogeneous mobile processors. - Joo Seong Jeong; Jingyu Lee; Donghyun Kim; Changmin Jeon; Changjin Jeong; Youngki Lee 001; Byung-Gon Chun (MobiSys, 2022)

A machine learning-based quantitative model (LogBB_Pred) to predict the blood-brain barrier permeability (logBB value) of drug compounds. - Bilal Shaker; Jingyu Lee; Yunhyeok Lee; Myeong-Sang Yu; Hyang-Mi Lee; Eunee Lee; Hoon-Chul Kang; Kwang-Seok Oh; Hyung Wook Kim; Dokyun Na (Bioinform., 2023)

XRMan: Towards Real-time Hand-Object Pose Tracking in eXtended Reality. - Dongho Han; Jingyu Lee; Minjae Kim 001; Youngki Lee 001 (MobiCom, 2024)

Logan: Loss-tolerant Live Video Analytics System. - Kichang Yang; Minkyung Jeong; Juheon Yi; Jingyu Lee; KyoungSoo Park; Youngki Lee 001 (MobiCom, 2024)

Maestro: The Analysis-Simulation Integrated Framework for Mixed Reality. - Jingyu Lee; Hyunsoo Kim; Minjae Kim 001; Byung-Gon Chun; Youngki Lee 001 (MobiSys, 2024)

Poster: Maestro: The Analysis-Simulation Integrated Framework for Mixed Reality. - Jingyu Lee; Hyunsoo Kim; Minjae Kim 001; Byung-Gon Chun; Youngki Lee 001 (MobiSys, 2024)

A computational clinical decision-supporting system to suggest effective anti-epileptic drugs for pediatric epilepsy patients based on deep learning models using patient's medical history. - Daeahn Cho; Myeong-Sang Yu; Jeongyoon Shin; Jingyu Lee; Yubin Kim 003; Hoon-Chul Kang; Se Hee Kim; Dokyun Na (BMC Medical Informatics Decis. Mak., 2024)

hERGBoost: A gradient boosting model for quantitative IC50 prediction of hERG channel blockers. - Myeong-Sang Yu; Jingyu Lee; Yunhyeok Lee; Daeahn Cho; Kwang-Seok Oh; Jidon Jang; Nuong Thi Nong; Hyang-Mi Lee; Dokyun Na (Comput. Biol. Medicine, 2025)