Výsledky vyhledávání - "Oh, Seong Je"

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Enhancing Generative Networks for Chest Anomaly Localization through Automatic Registration-Based Unpaired-to-Pseudo-Paired Training Data Translation

Autor: Kim, Kyungsu, Oh, Seong Je, Lim, Chae Yeon, Lee, Ju Hwan, Kim, Tae Uk, Chung, Myung Jin

Image translation based on a generative adversarial network (GAN-IT) is a promising method for the precise localization of abnormal regions in chest X-ray images (AL-CXR) even without the pixel-level annotation. However, heterogeneous unpaired datase

Externí odkaz: http://arxiv.org/abs/2207.10324

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AI-based computer-aided diagnostic system of chest digital tomography synthesis: Demonstrating comparative advantage with X-ray-based AI systems

Autor: Kim, Kyung-Su, Lee, Ju Hwan, Oh, Seong Je, Chung, Myung Jin

Compared with chest X-ray (CXR) imaging, which is a single image projected from the front of the patient, chest digital tomosynthesis (CDTS) imaging can be more advantageous for lung lesion detection because it acquires multiple images projected from

Externí odkaz: http://arxiv.org/abs/2206.13504

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3D unsupervised anomaly detection and localization through virtual multi-view projection and reconstruction: Clinical validation on low-dose chest computed tomography

Autor: Kim, Kyung-Su, Oh, Seong Je, Lee, Ju Hwan, Chung, Myung Jin

Computer-aided diagnosis for low-dose computed tomography (CT) based on deep learning has recently attracted attention as a first-line automatic testing tool because of its high accuracy and low radiation exposure. However, existing methods rely on s

Externí odkaz: http://arxiv.org/abs/2206.13385

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3D unsupervised anomaly detection through virtual multi-view projection and reconstruction: Clinical validation on low-dose chest computed tomography

Autor: Kim, Kyungsu, Oh, Seong Je, Lee, Ju Hwan, Chung, Myung Jin

Publikováno v: In Expert Systems With Applications February 2024 236

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Artificial intelligence-based classification of bone tumors in the proximal femur on plain radiographs: System development and validation.

Autor: Park, Chan-Woo¹ (AUTHOR), Oh, Seong-Je^2,3 (AUTHOR), Kim, Kyung-Su² (AUTHOR), Jang, Min-Chang¹ (AUTHOR), Kim, Il Su¹ (AUTHOR), Lee, Young-Keun¹ (AUTHOR), Chung, Myung Jin^2,4 (AUTHOR), Cho, Baek Hwan^2,5 (AUTHOR) sungwseo@gmail.com, Seo, Sung-Wook¹ (AUTHOR) sungwseo@gmail.com

Publikováno v: PLoS ONE. 2/24/2022, p1-14. 14p.

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Improved Chest Anomaly Localization without Pixel-level Annotation via Image Translation Network Application in Pseudo-paired Registration Domain

Autor: Kim, Kyungsu, Oh, Seong Je, Kim, Tae Uk, Chung, Myung Jin

Image translation based on a generative adversarial network (GAN-IT) is a promising method for the precise localization of abnormal regions in chest X-ray images (AL-CXR) even without pixel-level annotation. However, heterogeneous unpaired datasets u

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::b6702e033d5d0ff15c08d9f7e9376dc9

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