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322 results on '"Park, Beom Jin"'

3. Multi-input Vision Transformer with Similarity Matching

Author

Lee, Seungeun, Hwang, Sung Ho, Oh, Saelin, Park, Beom Jin, Cho, Yongwon, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Rekik, Islem, editor, Adeli, Ehsan, editor, Park, Sang Hyun, editor, Cintas, Celia, editor, and Zamzmi, Ghada, editor

Published
2023
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21. Comparison of 2 curative treatment options for very early hepatocellular carcinoma: Efficacy, recurrence pattern, and retreatment

Author

Kim, Tae Hyung, Chang, Jung Mi, Um, Soon Ho, Jee, Heejung, Lee, Yoo Ra, Lee, Han Ah, Yim, Sun Young, Han, Na Yeon, Lee, Jae Min, Choi, Hyuk Soon, Kim, Eun Sun, Yu, Young-Dong, Keum, Bora, Kim, Min Ju, An, Hyunggin, Park, Beom Jin, Seo, Yeon Seok, Kim, Dong-Sik, Yim, Hyung Joon, Cho, Sung Bum, Jeen, Yoon Tae, Lee, Hong Sik, Chun, Hoon Jai, Kim, Yun Hwan, and Kim, Chang Duck

Published
2019
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22. Multidetector Computed Tomography Imaging Features of Inflammatory Myofibroblastic Tumors of the Gastrointestinal Tract in Adults: Radiological, Histopathological, and Immunohistochemical Features

Author

Choi, In Young, primary, Yeom, Suk Keu, additional, Park, Beom Jin, additional, Jae Sung, Deuk, additional, Kim, Min Ju, additional, Han, Na Yeon, additional, Park, Yang Shin, additional, Cha, Sang Hoon, additional, Kim, So Yeon, additional, and Choi, Jung-Woo, additional

Published
2023
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24. Diagnostic accuracy of deep learning-based T staging of colorectal cancer using CT images

Author

Park, Beom Jin

Subjects
Staging, Oncology, Neoplasia, Artificial Intelligence, Colon, Multidisciplinary cancer care, CT
Abstract

Purpose Methods and materials Results Conclusion Personal information and conflict of interest References, Purpose: To evaluate the diagnostic of the T-stage predictive (low or high) deep learning using CT in colorectal cancer...

Published
2023
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30. A Study on USN Technologies for Ships

Author

Cho, Seong-Rak, Lee, Dong-Kon, Paik, Bu-Geun, Yoo, Jae-Hoon, Park, Young-Ha, Park, Beom-Jin, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Indulska, Jadwiga, editor, Ma, Jianhua, editor, Yang, Laurence T., editor, Ungerer, Theo, editor, and Cao, Jiannong, editor

Published
2007
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31. Employment of Wireless Sensor Networks for Full-Scale Ship Application

Author

Paik, Bu-Geun, Cho, Seong-Rak, Park, Beom-Jin, Lee, Dongkon, Yun, Jong-Hwui, Bae, Byung-Dueg, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Kuo, Tei-Wei, editor, Sha, Edwin, editor, Guo, Minyi, editor, Yang, Laurence T., editor, and Shao, Zili, editor

Published
2007
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36. Using a convolutional neural network model to derive imaging landmarks for lumbar spine numbering on axial magnetic resonance images.

Author

Yoon, Heewon, Cho, Yongwon, Ahn, Kyung‐Sik, Lee, Hee‐Gone, Kang, Chang Ho, and Park, Beom Jin

Subjects
CONVOLUTIONAL neural networks, LUMBAR vertebrae, MAGNETIC resonance imaging, RECEIVER operating characteristic curves, BACK muscles
Abstract

Understanding the axial lumbar spine anatomy, including knowledge of the relationship between the lumbar spine level and other paraspinal structures, is important for diagnosing and treating diseases. The purpose of this study was to validate the accuracy of a convolutional neural network (CNN) model in lumbar spine level numbering on axial magnetic resonance (MR) images and to find the appropriate anatomic landmarks for numbering using a class activation map (CAM). A total of 6055 axial MR images of the lumbar spine from the L1‐2 to L5‐S1 disc levels were obtained to train and validate the CNN model. MR images were acquired using three 3‐Tesla machines. The algorithm was developed with three models, and the best‐performing model was selected. The external validation set (n = 493) was obtained from other institutions using various machines. The accuracy of the numbering was analyzed using a confusion matrix and receiver operating characteristic curves. The CAMs were reviewed, and the identified anatomic structures were investigated. A reader study was performed by three radiologists, and their accuracy was compared with that of the model. The overall accuracy of the best‐performing model for lumbar spine numbering was 0.98 on internal validation and 0.95 on external validation. For the CAM review, mappings concentrated on both paraspinal areas, including the kidney, back muscles, and ilium according to the level. Top‐1 and top‐2 accuracies of the reviewers ranged between 0.56–0.75, and 0.84–0.93, respectively. After reviewing the CAMs, the accuracy increased to 0.75–0.78 and 0.93–0.98, respectively. A CNN model can accurately determine the level of the lumbar spine on axial MR images, and the configuration of muscles can be used to determine the lumbar level. [ABSTRACT FROM AUTHOR]

Published
2023
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