Search Results - "Jang, Miso"

Supervised representation learning based on various levels of pediatric radiographic views for transfer learning by Kyung, Sunggu, Jang, Miso, Park, Seungju, Yoon, Hee Mang, Hong, Gil-Sun, Kim, Namkug

“…Transfer learning plays a pivotal role in addressing the paucity of data, expediting training processes, and enhancing model performance. Nonetheless, the…”
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Association between long-term exposure to air pollutants and cardiopulmonary mortality rates in South Korea by Hwang, Jeongeun, Kwon, Jinhee, Yi, Hahn, Bae, Hyun-Jin, Jang, Miso, Kim, Namkug

“…The association between long-term exposure to air pollutants, including nitrogen dioxide (NO ), carbon monoxide (CO), sulfur dioxide (SO ), ozone (O ), and…”
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Synthesizing realistic high-resolution retina image by style-based generative adversarial network and its utilization by Kim, Mingyu, Kim, You Na, Jang, Miso, Hwang, Jeongeun, Kim, Hong-Kyu, Yoon, Sang Chul, Kim, Yoon Jeon, Kim, Namkug

“…Realistic image synthesis based on deep learning is an invaluable technique for developing high-performance computer aided diagnosis systems while protecting…”
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Image Turing test and its applications on synthetic chest radiographs by using the progressive growing generative adversarial network by Jang, Miso, Bae, Hyun-jin, Kim, Minjee, Park, Seo Young, Son, A-yeon, Choi, Se Jin, Choe, Jooae, Choi, Hye Young, Hwang, Hye Jeon, Noh, Han Na, Seo, Joon Beom, Lee, Sang Min, Kim, Namkug

“…The generative adversarial network (GAN) is a promising deep learning method for generating images. We evaluated the generation of highly realistic and…”
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Positive association between moderate altitude and chronic lower respiratory disease mortality in United States counties by Hwang, Jeongeun, Jang, Miso, Kim, Namkug, Choi, Seunghyun, Oh, Yeon-Mok, Seo, Joon Beom

“…For patients with chronic lower respiratory disease, hypobaric hypoxia at a high altitude is considered a risk factor for mortality. However, the effects of…”
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Effect of duration of diabetes on bone mineral density: a population study on East Asian males by Jang, Miso, Kim, Hyunkyung, Lea, Shorry, Oh, Sohee, Kim, Jong Seung, Oh, Bumjo

“…The aim of the present study is to evaluate the association between BMD and type 2 DM status in middle-aged and elderly men. To investigate a possible…”
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An Open Medical Platform to Share Source Code and Various Pre-Trained Weights for Models to Use in Deep Learning Research by Kim, Sungchul, Cho, Sungman, Cho, Kyungjin, Seo, Jiyeon, Nam, Yujin, Park, Jooyoung, Kim, Kyuri, Kim, Daeun, Hwang, Jeongeun, Yun, Jihye, Jang, Miso, Lee, Hyunna, Kim, Namkug

“…Deep learning-based applications have great potential to enhance the quality of medical services. The power of deep learning depends on open databases and…”
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Diagnosis of osteoporotic vertebral compression fractures and fracture level detection using multitask learning with U-Net in lumbar spine lateral radiographs by Ryu, Seung Min, Lee, Soyoung, Jang, Miso, Koh, Jung-Min, Bae, Sung Jin, Jegal, Seong Gyu, Shin, Keewon, Kim, Namkug

“…Recent studies of automatic diagnosis of vertebral compression fractures (VCFs) using deep learning mainly focus on segmentation and vertebral level detection…”
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Air Pollution and Subarachnoid Hemorrhage Mortality: A Stronger Association in Women than in Men by Hwang, Jeongeun, Yi, Hahn, Jang, Miso, Kim, Joong-Goo, Kwon, Sun U., Kim, Namkug, Lee, Eun-Jae

“…KCI Citation Count: 0…”
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Opportunistic Osteoporosis Screening Using Chest Radiographs With Deep Learning: Development and External Validation With a Cohort Dataset by Jang, Miso, Kim, Mingyu, Bae, Sung Jin, Lee, Seung Hun, Koh, Jung‐Min, Kim, Namkug

“…ABSTRACT Osteoporosis is a common, but silent disease until it is complicated by fractures that are associated with morbidity and mortality. Over the past few…”
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Enhancement of Non-Linear Deep Learning Model by Adjusting Confounding Variables for Bone Age Estimation in Pediatric Hand X-rays by Kim, Ki Duk, Kyung, Sunggu, Jang, Miso, Ji, Sunghwan, Lee, Dong Hee, Yoon, Hee Mang, Kim, Namkug

“…In medicine, confounding variables in a generalized linear model are often adjusted; however, these variables have not yet been exploited in a non-linear deep…”
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Overcoming the Challenges in the Development and Implementation of Artificial Intelligence in Radiology: A Comprehensive Review of Solutions Beyond Supervised Learning by Hong, Gil-Sun, Jang, Miso, Kyung, Sunggu, Cho, Kyungjin, Jeong, Jiheon, Lee, Grace Yoojin, Shin, Keewon, Kim, Ki Duk, Ryu, Seung Min, Seo, Joon Beom, Lee, Sang Min, Kim, Namkug

“…Artificial intelligence (AI) in radiology is a rapidly developing field with several prospective clinical studies demonstrating its benefits in clinical…”
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Impact of antiresorptive agents on mortality risk in postmenopausal women with osteoporosis: insights from a nationwide cohort study by Kim, Kyoung Jin, Ahn, Seong Hee, Park, So Young, Choi, Jimi, Bae, Gi Hwan, Kim, Ha-Young, Kim, Kyoung Min

“…Osteoporosis-related fractures are associated with increased mortality risk among postmenopausal women, yet the impact of antiosteoporotic medications on…”
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Machine learning models with time-series clinical features to predict radiographic progression in patients with ankylosing spondylitis by Koo, Bon San, Jang, Miso, Oh, Ji Seon, Shin, Keewon, Lee, Seunghun, Joo, Kyung Bin, Kim, Namkug, Kim, Tae-Hwan

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Assessment of the Robustness of Convolutional Neural Networks in Labeling Noise by Using Chest X-Ray Images From Multiple Centers by Jang, Ryoungwoo, Kim, Namkug, Jang, Miso, Lee, Kyung Hwa, Lee, Sang Min, Lee, Kyung Hee, Noh, Han Na, Seo, Joon Beom

“…Background: Computer-aided diagnosis on chest x-ray images using deep learning is a widely studied modality in medicine. Many studies are based on public…”
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Association between Weight Changes after Smoking Cessation and Cardiovascular Disease among the Korean Population by Kim, Eun Ha, Lee, Hyejin, Shin, Dong Wook, Yun, Jae Moon, Shin, Jung-Hyun, Lim, Yoo Kyoung, Koo, Hye Yeon, Jang, Miso

“…Cigarette smoking is a risk factor for cardiovascular disease (CVD) and has both beneficial and harmful effects in CVD. We hypothesized that weight gain…”
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