Jihye Yun, PhD

Department of Radiology, Asan Medical Center, Seoul, South Korea

<Recent Representative Paper>

Deep Learning for Automated Triaging of Stable Chest Radiographs in a Follow-up Setting

Jihye Yun, Yura Ahn, Kyungjin Cho, Sang Young Oh, Sang Min Lee, Namkug Kim, and Joon Beom Seo

Background: Most artificial intelligence algorithms that interpret chest radiographs are restricted to an image from a single time point. However, in clinical practice, multiple radiographs are used for longitudinal follow-up, especially in intensive care units (ICUs).

Purpose: To develop and validate a deep learning algorithm using thoracic cage registration and subtraction to triage pairs of chest radiographs showing no change by using longitudinal follow-up data.

Materials and Methods: A deep learning algorithm was retrospectively developed using baseline and follow-up chest radiographs in adults from January 2011 to December 2018 at a tertiary referral hospital. Two thoracic radiologists reviewed randomly selected pairs of “change” and “no change” images to establish the ground truth, including normal or abnormal status. Algorithm performance was evaluated using area under the receiver operating characteristic curve (AUC) analysis in a validation set and temporally separated internal test sets (January 2019 to August 2021) from the emergency department (ED) and ICU. Threshold calibration for the test sets was conducted, and performance with 40% and 60% triage thresholds was assessed.

Results: This study included 3 304 996 chest radiographs in 329 036 patients (mean age, 59 years ± 14 [SD]; 170 433 male patients). The training set included 550 779 pairs of radiographs. The validation set included 1620 pairs (810 no change, 810 change). The test sets included 533 pairs (ED; 265 no change, 268 change) and 600 pairs (ICU; 310 no change, 290 change). The algorithm had AUCs of 0.77 (validation), 0.80 (ED), and 0.80 (ICU). With a 40% triage threshold, specificity was 88.4% (237 of 268 pairs) and 90.0% (261 of 290 pairs) in the ED and ICU, respectively. With a 60% triage threshold, specificity was 79.9% (214 of 268 pairs) and 79.3% (230 of 290 pairs) in the ED and ICU, respectively. For urgent findings (consolidation, pleural effusion, pneumothorax), specificity was 78.6%–100% (ED) and 85.5%–93.9% (ICU) with a 40% triage threshold.

Conclusion: The deep learning algorithm could triage pairs of chest radiographs showing no change while detecting urgent interval changes during longitudinal follow-up.

Influence of content-based image retrieval on the accuracy and inter-reader agreement of usual interstitial pneumonia CT pattern classification

Sohee Park, Hye Jeon Hwang, Jihye Yun, Eun Jin Chae, Jooae Choe, Sang Min Lee, Han Na Lee, So Youn Shin, Heejun Park, Hana Jeong, Min Jee Kim, Jang Ho Lee, Kyung-Wook Jo, Seunghee Baek, Joon Beom Seo, European Radiology, May 2025.

Leveraging deep learning-based kernel conversion for more precise airway quantification on CT

Jooae Choe, Jihye Yun, Myeong Jun Kim, Yu Jin Oh, Seungbin Bae, Donghoon Yu, Joon Beom Seo, Sang Min Lee, Ho Yun Lee, European Radiology, May 2025.

Improving functional correlation of quantification of interstitial lung disease by reducing the vendor difference of CT using generative adversarial network (GAN) style conversion

Jooae Choe, Hye Jeon Hwang, Min Seon Kim, Jong Chul Ye, Gyutaek Oh, Sang Min Lee, Jihye Yun, Ho Yun Lee, Joo Sung Sun, Seulgi You, Jaeyoun Yi, Joon Beom Seo, European Journal of Radiology, Feburary 2025.

Screening patient misidentification errors using a deep learning model of chest radiography: a seven reader study

Kiduk Kim, Kyungjin Cho, Yujeong Eo, Jeeyoung Kim, Jihye Yun, Yura Ahn, Joon Beom Seo, Gil-Sun Hong, Namkug Kim, Journal of Imaging Informatics in Medicine, September 2024.

Enhancement of semantic segmentation by image-level fine-tuning to overcome image pattern imbalance in HRCT of diffuse Infiltrative lung diseases

Sungwon Ham, Beomhee Park, Jihye Yun, Sang Min Lee, Joon Beom Seo, Namkug Kim, International Journal of Imaging Systems and Technology, September 2024.

Deep learning-based prediction of post-treatment survival in hepatocellular carcinoma patients using pre-treatment CT images and clinical data

Kyung Hwa Lee, Jungwook Lee, Gwang Hyeon Choi, Jihye Yun, Jiseon Kang, Jonggi Choi, Kang Mo Kim, Namkug Kim , Journal of Imaging Informatics in Medicine, August2024.

Assessment of treatment response in patients with severe asthma using visual and quantitative analysis of chest CT

Han Na Lee, Jin An, Miji Lee, Hye Jeon Hwang, Jooae Choe, Jihye Yun, Ji-Hyang Lee, Min-Hye Kim, Young-Joo Cho, Sang Min Lee, Tae-Bum Kim, Joon Beom Seo, Korean Journal of Radiology, June 2024.

Evaluation of retrieval accuracy and visual similarity in content-based image retrieval of chest CT for obstructive lung disease

Jooae Choe, Hye Young Choi, Sang Min Lee, Sang Young Oh, Hye Jeon Hwang, Namkug Kim, Jihye Yun, Jae Seung Lee, Yeon-Mok Oh, Donghoon Yu, Byeongsoo Kim, Joon Beom Seo, Scientific Reports, February 2024.

Machine learning-based clinical decision support system for treatment recommendation and overall survival prediction of hepatocellular carcinoma: a multi-center study

Kyung Hwa Lee, Gwang Hyeon Choi, Jihye Yun, Jonggi Choi, Myung Ji Goh, Dong Hyun Sinn, Young Joo Jin, Minseok Albert Kim, Su Jong Yu, Sangmi Jang, Soon Kyu Lee, Jeong Won Jang, Jae Seung Lee, Do Young Kim, Young Youn Cho, Hyung Joon Kim, Sehwa Kim, Ji Hoon Kim, Namkug Kim, Kang Mo Kim , NPJ Digital Medicine, January 2024.

Technical feasibility of quantitative measurement of various degrees of small bowel motility using cine magnetic resonance imaging

Ji Young Choi, Jihye Yun, Subin Heo, Dong Wook Kim, Sang Hyun Choi, Jiyoung Yoon, Kyuwon Kim, Kee Wook Jung, and Seung-Jae Myung, Korean Journal of Radiology, November 2023.

Deep learning for automated triaging of stable chest radiographs in a follow-up setting

Jihye Yun, Yura Ahn, Kyungjin Cho, Sang Young Oh, Sang Min Lee, Namkug Kim, and Joon Beom Seo, Radiology, October 2023.

MuSiC-ViT: Multi-task Siamese convolutional vision transformer for differentiating change or no-change in follow-up chest X-rays

Kyungjin Cho, Jeeyoung Kim, Ki Duk Kim, Seungju Park, Junsik Kim, Jihye Yun, Yura Ahn, Sangyoung Oh, Sang Min Lee, Joon Beom Seo, and Namkug Kim, Medical Image Analysis, August 2023.

Generative adversarial network-based image conversion among different computed tomography protocols and vendors: Effects on accuracy and variability in quantifying regional disease patterns of interstitial lung disease

Hye Jeon Hwang, Hyunjong Kim, Joon Beom Seo, Jong Chul Ye, Gyutaek Oh, Sang Min Lee, Ryoungwoo Jang, Jihye Yun, Namkug Kim, Hee Jun Park, Ho Yun Lee, Soon Ho Yoon, Kyung Eun Shin, Jae Wook Lee, Woocheol Kwon, Joo Sung Sun, Seulgi You, Myung Hee Chung, Bo Mi Gil, Jae-Kwang Lim, Youkyung Lee, Su Jin Hong, Yo Won Choi, Korean Journal of Radiology, August 2023.

Automated detection of intracranial aneurysms using skeleton-based 3D patches, semantic segmentation, and auxiliary classification for overcoming data imbalance in brain TOF-MRA

Sungwon Ham, Jiyeon Seo, Jihye Yun, Yun Jung Bae, Tackeun Kim, Leonard Sunwoo, Sooyoung Yoo, Seung Chai Jung, Jeong-Whun Kim, Namkug Kim, Scientific Reports, July 2023.

Deep learning of time-signal intensity curves from dynamic susceptibility contrast imaging enables tissue labeling and prediction of survival in glioblastoma

Jihye Yun, Su Young Yun, Ji Eun Park, E-Nae Cheong, Seo Young Park, Namkug Kim, Ho Sung Kim, American Journal of Neuroradiology, April 2023.

Deep learning–based approach to predict pulmonary function at chest CT

Hyunjung Park, Jihye Yun, Sang Min Lee, Hye Jeon Hwang, Joon Beom Seo, Young Ju Jung, Jeongeun Hwang, Se Hee Lee, Sei Won Lee, Namkug Kim, Radiology, February 2023.

Federated learning enables big data for rare cancer boundary detection

Sarthak Pati et al., Nature communications, December 2022.

Interstitial lung abnormalities (ILA) on routine chest CT: Comparison of radiologists’ visual evaluation and automated quantification

Min Seon Kim, Jooae Choe, Hye Jeon Hwang, Sang Min Lee, Jihye Yun, Namkug Kim, Myung-Su Ko, Jaeyoun Yi, Donghoon Yu, Joon Beom Seo, European Journal of Radiology, December 2022.

Artificial Intelligence in Lung Imaging

Min Seon Kim, Jooae Choe, Hye Jeon Hwang, Sang Min Lee, Jihye Yun, Namkug Kim, Myung-Su Ko, Jaeyoun Yi, Donghoon Yu, Joon Beom Seo, European Journal of Radiology, December 2022.

Development and validation of a model using radiomics features from an apparent diffusion coefficient map to diagnose local tumor recurrence in patients treated for head and neck squamous cell carcinoma

Minjae Kim, Jeong Hyun Lee, Leehi Joo, Boryeong Jeong, Seonok Kim, Sungwon Ham, Jihye Yun, NamKug Kim, Sae Rom Chung, Young Jun Choi, Jung Hwan Baek, Ji Ye Lee, Ji-hoon Kim, Korean Journal of Radiology, November 2022.

Multi-domain CT translation by a routable translation network

Hyunjong Kim, Gyutaek Oh, Joon Beom Seo, Hye Jeon Hwang, Sang Min Lee, Jihye Yun, Jong Chul Ye, Physics in Medicine & Biology, October 2022.

Content-based image retrieval by using deep learning for interstitial lung disease diagnosis with chest CT

Jooae Choe, Hye Jeon Hwang , Joon Beom Seo, Sang Min Lee, Jihye Yun, Min-Ju Kim, Jewon Jeong, Youngsoo Lee, Kiok Jin, Rohee Park, Jihoon Kim, Howook Jeon, Namkug Kim, Jaeyoun Yi, Donghoon Yu, Byeongsoo Kim, Radiology, January 2022.

An open medical platform to share source code and various pre-trained weights for models to use in deep learning research

Sungchul Kim, Sungman Cho, Kyungjin Cho, Jiyeon Seo, Yujin Nam, Jooyoung Park, Kyuri Kim, Daeun Kim, Jeongeun Hwang, Jihye Yun, Miso Jang, Hyunna Lee, and Namkug Kim, Korean Journal of Radiology, December 2021.

Deep radiomics-based survival prediction in patients with chronic obstructive plumonary disease

Jihye Yun, Young Hoon Cho, Sang Min Lee, Jeongeun Hwang, Jae Seung Lee, Yeon-Mok Oh, Sang-Do Lee, Li-Cher Loh, Choo-Khoon Ong, Joon Beom Seo, Namkug Kim, Scientific Reports, July 2021

Diffusion and perfusion MRI radiomics obtained from deep learning segmentation provides reproducible and comparable diagnostic model to human in post-treatment glioblastoma

Ji Eun Park, Sungwon Ham, Ho Sung Kim, Seo Young Park, Jihye Yun, Hyunna Lee, Seung Hong Choi, Namkug Kim, European Radiology, May 2021.

Radiomics approach for survival prediction in chronic obstructive pulmonary disease

Young Hoon Cho, Joon Beom Seo, Sang Min Lee, Namkug Kim, Jihye Yun, Jeong Eun Hwang, Jae Seung Lee, Yeon-Mok Oh, Sang Do Lee, Li-Cher Loh, Choo-Khoom Ong, European Radiology, April 2021.

Realistic high-resolution body computed tomography image synthesis by using progressive growing generative adversarial network: visual Turing test

Ho Young Park, Hyun-Jin Bae, Gil-Sun Hong, Minjee Kim, Jihye Yun, Sungwon Park, Won Jung Chung, NamKug Kim, JMIR Medical Informatics, March 2021.

Machine learning approach for differentiating cytomegalovirus esophagitis from herpes simplex virus esophagitis

Jung Su Lee, Jihye Yun, Sungwon Ham, Hyunjung Park, Hyunsu Lee, Jeongseok Kim, Jeong-Sik Byeon, Hwoon-Yong Jung, Namkug Kim, Do Hoon Kim, Scientific Reports, February 2021

Value of bone suppression software in chest radiographs for improving image quality and reducing radiation dose

Gil-Sun Hong, Kyung-Hyun Do, A-Yeon Son, Kyung-Wook Jo, Kwang Pyo Kim, Jihye Yun, Choong Wook Lee, European Radiology, January 2021.

Oropharyngeal squamous cell carcinoma: radiomics machine-learning classifiers from multiparametric MR images for determination of HPV infection status

Chong Hyun Suh, Kyung Hwa Lee, Young Jun Choi, Sae Rom Chung, Jung Hwan Baek, Jeong Hyun Lee, Jihye Yun, Sungwon Ham, Namkug Kim, Scientific Reports, October 2020.

Development of machine learning-based clinical decision support system for hepatocellular carcinoma

Gwang Hyeon Choi, Jihye Yun, Jonggi Choi, Danbi Lee, Ju Hyun Shim, Han Chu Lee, Young-Hwa Chung, Yung Sang Lee, Beomhee Park, Namkug Kim, Kang Mo Kim, Scientific Reports, September 2020.

Fully automated lung lobe segmentation in volumetric chest CT with 3D U-Net: validation with intra- and extra-datasets

Jongha Park, Jihye Yun, Namkug Kim, Beomhee Park, Yongwon Cho, Hee Jun Park, Mijeong Song, Minho Lee, Joon Beom Seo, Journal of Digital Imaging, February 2020.

Deep learning in medical imaging

Mingyu Kim, Jihye Yun, Yongwon Cho, Keewon Shin, Ryoungwoo Jang, Hyun-jin Bae, Namkug Kim, Neurospine, December 2019.

Radiomics features and multilayer perceptron network classifier: a robust MRI classification strategy for distinguishing glioblastoma from primary central nervous system lymphoma

Jihye Yun, Ji Eun Park, Hyunna Lee, Sungwon Ham, Namkug Kim, Ho Sung Kim, Scientific Reports, April 2019.

Deep learning applications in chest radiography and computed tomography

Sang Min Lee, Joon Beom Seo, Jihye Yun, Young-Hoon Cho, Jens Vogel-Claussen, Mark L Schiebler, Warren B Gefter, Edwin JR Van Beek, Jin Mo Goo, Kyung Soo Lee, Hiroto Hatabu, James Gee, Namkug Kim, Journal of thoracic imaging, March 2019.

Improvement of fully automated airway segmentation on volumetric computed tomographic images using a 2.5 dimensional convolutional neural net

Jihye Yun, Jinkon Park, Donghoon Yu, Jaeyoun Yi, Minho Lee, Hee Jun Park, June-Goo Lee, Joon Beom Seo, Namkug Kim, Medical Image Analysis, January 2019.

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