Deep convolution neural networks to differentiate between COVID-19 and other pulmonary abnormalities on chest radiographs: Evaluation using internal and external datasets. - Yongwon Cho; Sung Ho Hwang; Yu-Whan Oh; Byung-Joo Ham; Min Ju Kim; Beom Jin Park (Int. J. Imaging Syst. Technol., 2021)

Active Learning for Efficient Segmentation of Liver with Convolutional Neural Network-Corrected Labeling in Magnetic Resonance Imaging-Derived Proton Density Fat Fraction. - Yongwon Cho; Min Ju Kim; Beom Jin Park; Ki Choon Sim; Yeom Suk Keu; Yeo Eun Han; Deuk Jae Sung; Na Yeon Han (J. Digit. Imaging, 2021)

Computer-aided hepatocellular carcinoma detection on the hepatobiliary phase of gadoxetic acid-enhanced magnetic resonance imaging using a convolutional neural network: Feasibility evaluation with multi-sequence data. - Yongwon Cho; Yeo Eun Han; Min Ju Kim; Beom Jin Park; Ki Choon Sim; Deuk Jae Sung; Na Yeon Han; Yang Shin Park (Comput. Methods Programs Biomed., 2022)

Multi-input Vision Transformer with Similarity Matching. - Seungeun Lee; Sung Ho Hwang; Saelin Oh; Beom Jin Park; Yongwon Cho (PRIME@MICCAI, 2023)

Using a convolutional neural network model to derive imaging landmarks for lumbar spine numbering on axial magnetic resonance images. - Heewon Yoon; Yongwon Cho; Kyung-Sik Ahn; Hee-Gone Lee; Chang Ho Kang; Beom Jin Park (Int. J. Imaging Syst. Technol., 2023)