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Document Title Author Full Name Author Short Name Index Corresponding Address ResearcherID ResearcherID Author Name ORCID ORCID Author Name Related Email
Prediction of Motor Recovery in Patients with Basal Ganglia Hemorrhage Using Diffusion Tensor Imaging Jung, Tae-Du Jung, TD 10 교신저자 Kyungpook Natl Univ Hosp, Dept Rehabil Med, Daegu 41944, South Korea 0000-0002-1636-8665 Jung, Tae-du ssuni119@gmail.com;soulmate907@naver.com;ehmdpark@naver.com;ryoung20@hanmail.net;kangmingu.ryan@gmail.com;dbstnone@naver.com;kjoohyun88@gmail.com;pyromyth@naver.com;jparkneurosurgery@gmail.com;teeed0522@hanmail.net;
Prediction of motor recovery using indirect connectivity in a lesion network after ischemic stroke Lee, Jungsoo Lee, J 1 Sungkyunkwan Univ, Samsung Med Ctr, Dept Phys & Rehabil Med, Sch Med, Seoul, South Korea yunkim@skku.edu;yun1225.kim@samsung.com;
Prediction of motor recovery using indirect connectivity in a lesion network after ischemic stroke Park, Eunhee Park, E 2 Kyungpook Natl Univ, Med Ctr, Dept Phys & Rehabil Med, Daegu, South Korea yunkim@skku.edu;yun1225.kim@samsung.com;
Prediction of motor recovery using indirect connectivity in a lesion network after ischemic stroke Lee, Ahee Lee, A 3 Sungkyunkwan Univ, Dept Hlth Sci & Technol, Dept Med Device Management & Res, Dept Digital Hlth,SAIHST, Seoul, South Korea yunkim@skku.edu;yun1225.kim@samsung.com;
Prediction of motor recovery using indirect connectivity in a lesion network after ischemic stroke Chang, Won Hyuk Chang, WH 4 Sungkyunkwan Univ, Samsung Med Ctr, Dept Phys & Rehabil Med, Sch Med, Seoul, South Korea yunkim@skku.edu;yun1225.kim@samsung.com;
Prediction of motor recovery using indirect connectivity in a lesion network after ischemic stroke Kim, Dae-Shik Kim, DS 5 Korea Adv Inst Sci & Technol, Sch Elect Engn, Daejeon, South Korea yunkim@skku.edu;yun1225.kim@samsung.com;
Prediction of motor recovery using indirect connectivity in a lesion network after ischemic stroke Kim, Yun-Hee Kim, YH 6 교신저자 Sungkyunkwan Univ, Dept Phys & Rehabil Med, Sch Med,Dept Hlth Sci & Technol,SAIHST,Ctr Preven, Dept Digital Hlth,Heart Vasc Stroke Inst,Samsung, 81 Irwon Ro, Seoul 06351, South Korea GVS-6426-2022 Kim, Yun-Hee 0000-0001-6101-8851 Kim, Yun-Hee yunkim@skku.edu;yun1225.kim@samsung.com;
Prediction of recurrence in locally advanced breast cancer using deep learning analysis Jo, I. Jo, I 1 Kyungpook Natl Univ, Sch Med, Dept Nucl Med, Daegu, South Korea
Prediction of recurrence in locally advanced breast cancer using deep learning analysis Jo, I. Jo, I 1 Kyungpook Natl Univ, Chilgok Hosp, Daegu, South Korea
Prediction of recurrence in locally advanced breast cancer using deep learning analysis Kim, J. Kim, J 2 Kyungpook Natl Univ, Sch Comp Sci & Engn, Daegu, South Korea
Prediction of recurrence in locally advanced breast cancer using deep learning analysis Jeong, S. Jeong, S 3 Kyungpook Natl Univ, Sch Med, Dept Nucl Med, Daegu, South Korea
Prediction of recurrence in locally advanced breast cancer using deep learning analysis Jeong, S. Jeong, S 3 Kyungpook Natl Univ, Chilgok Hosp, Daegu, South Korea
Prediction of recurrence in locally advanced breast cancer using deep learning analysis Chae, Y. Chae, Y 4 Kyungpook Natl Univ, Kyungpook Natl Univ Hosp, Dept Hematol Oncol, Sch Med, Daegu, South Korea
Prediction of recurrence in locally advanced breast cancer using deep learning analysis Lee, S. Lee, S 5 Kyungpook Natl Univ, Sch Med, Dept Nucl Med, Daegu, South Korea
Prediction of recurrence in locally advanced breast cancer using deep learning analysis Lee, S. Lee, S 5 Kyungpook Natl Univ, Chilgok Hosp, Daegu, South Korea
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