Zobrazeno 1 - 10 of 87 pro vyhledávání: '"Kyu Hwan Jung"'
1
Akademický článek
Development of a Spine X-Ray-Based Fracture Prediction Model Using a Deep Learning Algorithm
Autor: Sung Hye Kong, Jae-Won Lee, Byeong Uk Bae, Jin Kyeong Sung, Kyu Hwan Jung, Jung Hee Kim, Chan Soo Shin
Publikováno v: Endocrinology and Metabolism, Vol 37, Iss 4, Pp 674-683 (2022)
Background Since image-based fracture prediction models using deep learning are lacking, we aimed to develop an X-ray-based fracture prediction model using deep learning with longitudinal data. Methods This study included 1,595 participants aged 50 t
Externí odkaz: https://doaj.org/article/6e055d55c047410aa56e3096187996d1
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2
Akademický článek
A large multi-focus dataset for white blood cell classification
Autor: Seongjin Park, Hyunghun Cho, Bo Mee Woo, Seung Min Lee, Dayeong Bae, Adam Balint, Yoon Jeong Seo, Chae Yun Bae, Kyung-Hak Choi, Kyu-Hwan Jung
Publikováno v: Scientific Data, Vol 11, Iss 1, Pp 1-6 (2024)
Abstract The White Blood Cell (WBC) differential test ranks as the second most frequently performed diagnostic assay. It requires manual confirmation of the peripheral blood smear by experts to identify signs of abnormalities. Automated digital micro
Externí odkaz: https://doaj.org/article/6f7b154e6d794ae7b6a6689a82c46fbb
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3
Akademický článek
Computationally efficient and stable real-world synthetic emergency room electronic health record data generation: high similarity and privacy preserving diffusion model approach: A retrospective cohort study
Autor: Javier Aguirre, Jae Yong Yu, Kyu-Hwan Jung, Jinsung Yoon, Won Chul Cha
Publikováno v: Precision and Future Medicine, Vol 8, Iss 3, Pp 92-104 (2024)
Purpose This study aimed to develop real-world synthetic electronic health record (EHR) for emergency departments using computationally efficient and stable diffusion probabilistic models. Methods In this study, we compared the performance of diffusi
Externí odkaz: https://doaj.org/article/b75f6401e8fc44da84b2875946b44219
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4
Akademický článek
An interpretable and interactive deep learning algorithm for a clinically applicable retinal fundus diagnosis system by modelling finding-disease relationship
Autor: Jaemin Son, Joo Young Shin, Seo Taek Kong, Jeonghyuk Park, Gitaek Kwon, Hoon Dong Kim, Kyu Hyung Park, Kyu-Hwan Jung, Sang Jun Park
Publikováno v: Scientific Reports, Vol 13, Iss 1, Pp 1-13 (2023)
Abstract The identification of abnormal findings manifested in retinal fundus images and diagnosis of ophthalmic diseases are essential to the management of potentially vision-threatening eye conditions. Recently, deep learning-based computer-aided d
Externí odkaz: https://doaj.org/article/db40dd2c407843a68edf21b13b516105
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5
Akademický článek
Aggregation of cohorts for histopathological diagnosis with deep morphological analysis
Autor: Jeonghyuk Park, Yul Ri Chung, Seo Taek Kong, Yeong Won Kim, Hyunho Park, Kyungdoc Kim, Dong-Il Kim, Kyu-Hwan Jung
Publikováno v: Scientific Reports, Vol 11, Iss 1, Pp 1-11 (2021)
Abstract There have been substantial efforts in using deep learning (DL) to diagnose cancer from digital images of pathology slides. Existing algorithms typically operate by training deep neural networks either specialized in specific cohorts or an a
Externí odkaz: https://doaj.org/article/cc3a6ff7b5f74169a3a27530a75eb747
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8
Utilizing Synthetic Nodules for Improving Nodule Detection in Chest Radiographs
Autor: Minki Chung, Seo Taek Kong, Beomhee Park, Younjoon Chung, Kyu-Hwan Jung, Joon Beom Seo
Publikováno v: Journal of Digital Imaging. 35:1061-1068
Algorithms that automatically identify nodular patterns in chest X-ray (CXR) images could benefit radiologists by reducing reading time and improving accuracy. A promising approach is to use deep learning, where a deep neural network (DNN) is trained
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::9fbf8b951ecaa9069addd173dbb450a9
https://doi.org/10.1007/s10278-022-00608-9
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9
Akademický článek
Leveraging the Generalization Ability of Deep Convolutional Neural Networks for Improving Classifiers for Color Fundus Photographs
Autor: Jaemin Son, Jaeyoung Kim, Seo Taek Kong, Kyu-Hwan Jung
Publikováno v: Applied Sciences, Vol 11, Iss 2, p 591 (2021)
Deep learning demands a large amount of annotated data, and the annotation task is often crowdsourced for economic efficiency. When the annotation task is delegated to non-experts, the dataset may contain data with inaccurate labels. Noisy labels not
Externí odkaz: https://doaj.org/article/8f9ba39fb2f442eaaa0f8831dcaf0cb1
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