Development of Computer-Aided Diagnostic (CADx) System for Distinguishing Neoplastic from Nonneoplastic Lesions in CT Colonography (CTC): Toward CTC beyond Detection. - Kenji Suzuki 001; Mohammadamin Zarshenas; Junchi Liu; Yonghui Fan; Nazanin Makkinejad; Paul Forti; Abraham H. Dachman (SMC, 2015)

Neural Network Convolution (NNC) for Converting Ultra-Low-Dose to "Virtual" High-Dose CT Images. - Kenji Suzuki 001; Junchi Liu; Amin Zarshenas; Toru Higaki; Wataru Fukumoto; Kazuo Awai (MLMI@MICCAI, 2017)

Radiation dose reduction in digital breast tomosynthesis (DBT) by means of neural network convolution (NNC) deep learning. - Junchi Liu; Amin Zarshenas; Syed Ammar Qadir; Limin Yang; Laurie Lee Fajardo; Kenji Suzuki 001 (IWBI, 2018)

Deep neural network convolution (NNC) for three-class classification of diffuse lung disease opacities in high-resolution CT (HRCT): Consolidation, ground-glass opacity (GGO), and normal opacity. - Noriaki Hashimoto; Kenji Suzuki 001; Junchi Liu; Yasushi Hirano; Heber MacMahon; Shoji Kido (Computer-Aided Diagnosis, 2018)

Radiation dose reduction in digital breast tomosynthesis (DBT) by means of deep-learning-based supervised image processing. - Junchi Liu; Amin Zarshenas; Ammar Qadir; Zheng Wei; Limin Yang; Laurie Lee Fajardo; Kenji Suzuki 001 (Image Processing, 2018)

Mixture of Deep-Learning Experts for Separation of Bones from Soft Tissue in Chest Radiographs. - Amin Zarshenas; Junchi Liu; Paul Forti; Kenji Suzuki 001 (SMC, 2018)

Mixed L1 norm and L2 norm regularized sparsity adaptive matching pursuit algorithm. - Nian Cai; Qian Ye; Jing Wang; Guandong Cen; Junchi Liu; Han Wang 017; Bingo Wing-Kuen Ling (Signal Image Video Process., 2018)

Artifact reduction in brain magnetic resonance imaging (MRI) by means of a dense residual network with K-space blending (DRN-KB). - Junchi Liu; Jie Deng (Image Processing, 2020)

PageRank Centrality based Graph Convolutional Networks for Semi-supervised Node Classification. - Lei Chen; Junchi Liu; Xueyuan Zhang; Qifeng Zhou (ICCSE, 2021)

Accounting For Inter-Subject Variations in Deep Learning for Reduced-Dose Studies in Cardiac SPECT. - Junchi Liu; Yongyi Yang; Miles N. Wernick; P. Hendrik Pretorius; Michael A. King (ICIP, 2021)

Improving Diagnostic Accuracy Of Reduced-Dose Studies With Full-Dose Noise-To-Noise Learning In Cardiac Spect. - Junchi Liu; Yongyi Yang; Miles N. Wernick; P. Hendrik Pretorius; Michael A. King (ISBI, 2021)

Dose-Blind Denoising With Deep Learning in Cardiac Spect. - Junchi Liu; Yongyi Yang; Miles N. Wernick; P. Hendrik Pretorius; Michael A. King (ICIP, 2022)

Dim Space Target Detection via Convolutional Neural Network in Single Optical Image. - Xiangji Guo; Tao Chen; Junchi Liu; Yuan Liu; Qichang An (IEEE Access, 2022)

Nonuniform Correction of Ground-Based Optical Telescope Image Based on Conditional Generative Adversarial Network. - Xiangji Guo; Tao Chen; Junchi Liu; Yuan Liu; Qichang An; Chunfeng Jiang (Sensors, 2023)

Restoration of Spatially Variant Blurred Images with Wide-Field Telescope Based on Deep Learning. - Yingmei Tian; Jianli Wang; Junchi Liu; Xiangji Guo (Sensors, 2023)

Atmospheric Light Estimation Using Polarization Degree Gradient for Image Dehazing. - Shuai Liu; Hang Li; Jinyu Zhao; Junchi Liu; Youqiang Zhu; Zhenduo Zhang (Sensors, 2024)

TransConv: Transformer Meets Contextual Convolution for Unsupervised Domain Adaptation. - Junchi Liu; Xiang Zhang; Zhigang Luo (Entropy, 2024)

SA-GAT-SR: Self-Adaptable Graph Attention Networks with Symbolic Regression for high-fidelity material property prediction. - Junchi Liu; Ying Tang; Sergei Tretiak; Wenhui Duan; Liujiang Zhou (CoRR, 2025)