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Byungho Oh

Showing results (1-10 of 11) with videos related to

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IEEE Journal of Biomedical and Health Informatics|March 3, 2025
Class-Agnostic Feature-Learning-Based Deep-Learning Model for Robust Melanoma PredictionYuseong Chu, Solam Lee, Byungho Oh, et al.
Plos One|February 12, 2026
Development of a multi-task learning framework with gradnorm for precise wound tissue analysisHyunyoung Kang, Byungho Oh, Solam Lee, et al.
Sensors (Basel, Switzerland)|September 9, 2023
Deep Learning-Based Evaluation of Ultrasound Images for Benign Skin TumorsHyunwoo Lee, Yerin Lee, Seung-Won Jung, et al.
Plos One|March 8, 2018
Acral melanoma detection using a convolutional neural network for dermoscopy imagesChanki Yu, Sejung Yang, Wonoh Kim, et al.
Plos One|September 21, 2016
Sequential Change of Wound Calculated by Image Analysis Using a Color Patch Method during a Secondary Intention HealingSejung Yang, Junhee Park, Hanuel Lee, et al.
Plos One|April 25, 2018
Correction: Acral melanoma detection using a convolutional neural network for dermoscopy imagesChanki Yu, Sejung Yang, Wonoh Kim, et al.
Biomedical Optics Express|July 1, 2017
Dermoscopy guided dark-field multi-functional optical coherence tomographySoonjae Kwon, Yeoreum Yoon, Bumju Kim, et al.
The Journal of Investigative Dermatology|January 15, 2022
Deep Learning Algorithms for Predicting Breslow Thickness from Dermoscopic Images of Acral Lentiginous MelanomasYu Seong Chu, Solam Lee, Sang Gyun Lee, et al.
BMC Cancer|November 3, 2022
A nomogram combining clinical factors and biomarkers for predicting the recurrence of high-risk cutaneous squamous cell carcinomaYeongjoo Oh, Zhenlong Zheng, Ki-Yeol Kim, et al.
Cancer Research and Treatment|May 13, 2026
Risk Prediction Based on Clinicopathologic Features in Korean Melanoma Patients by Machine LearningTaeho Yuh, Hyunwook Kim, Eun Sil Baek, et al.
Pageof 2

Showing results (1-10 of 11) with videos related to

Sort By:
Pageof 2
IEEE Journal of Biomedical and Health Informatics|March 3, 2025
Class-Agnostic Feature-Learning-Based Deep-Learning Model for Robust Melanoma PredictionYuseong Chu, Solam Lee, Byungho Oh, et al.
Plos One|February 12, 2026
Development of a multi-task learning framework with gradnorm for precise wound tissue analysisHyunyoung Kang, Byungho Oh, Solam Lee, et al.
Sensors (Basel, Switzerland)|September 9, 2023
Deep Learning-Based Evaluation of Ultrasound Images for Benign Skin TumorsHyunwoo Lee, Yerin Lee, Seung-Won Jung, et al.
Plos One|March 8, 2018
Acral melanoma detection using a convolutional neural network for dermoscopy imagesChanki Yu, Sejung Yang, Wonoh Kim, et al.
Plos One|September 21, 2016
Sequential Change of Wound Calculated by Image Analysis Using a Color Patch Method during a Secondary Intention HealingSejung Yang, Junhee Park, Hanuel Lee, et al.
Plos One|April 25, 2018
Correction: Acral melanoma detection using a convolutional neural network for dermoscopy imagesChanki Yu, Sejung Yang, Wonoh Kim, et al.
Biomedical Optics Express|July 1, 2017
Dermoscopy guided dark-field multi-functional optical coherence tomographySoonjae Kwon, Yeoreum Yoon, Bumju Kim, et al.
The Journal of Investigative Dermatology|January 15, 2022
Deep Learning Algorithms for Predicting Breslow Thickness from Dermoscopic Images of Acral Lentiginous MelanomasYu Seong Chu, Solam Lee, Sang Gyun Lee, et al.
BMC Cancer|November 3, 2022
A nomogram combining clinical factors and biomarkers for predicting the recurrence of high-risk cutaneous squamous cell carcinomaYeongjoo Oh, Zhenlong Zheng, Ki-Yeol Kim, et al.
Cancer Research and Treatment|May 13, 2026
Risk Prediction Based on Clinicopathologic Features in Korean Melanoma Patients by Machine LearningTaeho Yuh, Hyunwook Kim, Eun Sil Baek, et al.
Pageof 2