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O1.
Á¦¸ñ: Comprehensive Comparative Assessment of the Whole
Slide Images of Cytologic Samples
for Quality Assurance Program in Korea
¼º¸í: Yosep Chong1,*, Soon Auck Hong2, Hoon Kyu Oh3, Soo Jin
Jung4, Bo-Sung Kim5, Ji Yun Jeong6, Ho Chang Lee7, Gyungyub Gong8, The
Committee of Quality Improvement of Korean Society for Cytopathology
¼Ò¼Ó: 1Department of Hospital Pathology, The Catholic University of Korea College of Medicine, Seoul; 2Department of Pathology, Chung-Ang University College of Medicine, Seoul; 3Department of Pathology, Daegu Catholic University School of Medicine, Daegu; 4Department of Pathology, Inje University Busan Paik Hospital, Busan; 5Department of Pathology, Green Cross Laboratories, Yongin; 6Department of Pathology, Kyungpook National University Chilgok Hospital, School of Medicine, Kyungpook National University, Daegu; 7Department of Pathology, Chungbuk National University College of Medicine, Cheongju; 8Department of Pathology, Asan Medical Center, University of Ulsan Col lege of Medicine, Seoul, Republic of Korea.
O2.
Á¦¸ñ:
Establishment
of Non-face-to-face Image Training Platform for Korean Association of Cytotechnologists (KAC)
¼º¸í: Youngjoo Kim, C.T1., Kang Jeong-Eun, C.T2., Gyeong-Su Ko,
C.T3., Young-Jae Oh1, C.T., Kwang Pyo Hong1, C.T., Ji Yoon Eum1, C.T., Kyung
Hee Shon1, C.T., Myong Ja Jeon1, C.T., Eung-Soo Eo1, C.T., Jae-Nam Ko1, C.T.,
Gyungyub Gong1, M.D., Kyung-Ja Cho1, M.D.
¼Ò¼Ó: 1Department of Pathology, University of Ulsan College of Medicine,
Asan Medical Center, Seoul, Korea, 2Department of Pathology Yonsei University
College of Medicine, 3Department of Pathology Samsung Medical Center
O3.
Á¦¸ñ:
Deep
learning-based detection of metastatic ovarian cancer from ascitic fluids
¼º¸í:
Kwangil Yim1
, Jamshid ABDUL-GHAFAR1, Kyung Jin SEO1, Ok Ran Shin 1, Gisu
HWANG2, Sohyun JEONG2, Gyungyub GONG3*, Nam Hoon CHO4, Chong woo YOO5, and
Yosep CHONG1
¼Ò¼Ó:
1Department of Hospital Pathology, The Catholic University of
Korea College of Medicine, Seoul; 2AI Team, DeepNoid Inc., Seoul; 3Department
of Pathology, Asan Medical Center, Seoul; 4Department of Pathology, Yonsei
University College of Medicine, Seoul; 5Department of Pathology, National
Cancer Center, Ilsan, Gyeonggi-do, REPUBLIC OF KOREA
O4.
Á¦¸ñ:
Feasibility of a deep learning algorithm to
distinguish malignancy in pleural fluids cytology specimens
¼º¸í:
Kyung Jin SEO1
, Kwangil Yim1, Jamshid ABDUL-GHAFAR1, Ok Ran Shin 1,
Gisu HWANG2, Sohyun JEONG2, Gyungyub GONG3, Nam Hoon CHO4, Chong woo YOO5, and
Yosep CHONG1*
¼Ò¼Ó:
1Department of Hospital Pathology, The Catholic University of
Korea College of Medicine, Seoul; 2AI Team, DeepNoid Inc., Seoul; 3Department
of Pathology, Asan Medical Center, Seoul; 4Department of Pathology, Yonsei
University College of Medicine, Seoul; 5Department of Pathology, National
Cancer Center, Ilsan, Gyeonggi-do, REPUBLIC OF KOREA
O5.
Á¦¸ñ:
Applying Shorr Staining Method in Rapid
On-site Evaluation for Solid Pancreatic Lesions: a Pilot Study
¼º¸í:
Hee Young Na1
, Jong-Chan
Lee2, Jin-Hyeok Hwang2, Jaihwan Kim2
¼Ò¼Ó
1Department of Pathology, Seoul
National University Bundang Hospital, Seoul National University College of Medicine,
Seongnam, Korea 2 Department of Internal Medicine, Seoul National University
Bundang Hospital, Seoul National University College of Medicine, Seongnam,
Korea
O6.
Á¦¸ñ:
Improvement
of Diagnostic Accuracy of Pancreas EUS-guided needle biopsy by Liquid-Based
Preparation Cytology
¼º¸í:
Ji Yoon Eum, C.T.,
Youngjoo Kim, C.T., Kwang Pyo Hong, C.T., Hwajeong Shin, C.T., Kyung Hee Shon,
C.T.,Myong Ja Jeon, C.T., Eung-Soo Eo, C.T., Young-Jae Oh, C.T., Jae-Nam Ko,
C.T., Seung-Mo Hong, M.D., Gyungyub Gong, M.D., Kyung-Ja Cho, M.D.
¼Ò¼Ó: Department of Pathology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Korea
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