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Tae-Yeong Kwak

Showing results (1-10 of 9) with videos related to

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Bioengineering (Basel, Switzerland)|November 25, 2023
RCKD: Response-Based Cross-Task Knowledge Distillation for Pathological Image AnalysisHyunil Kim, Tae-Yeong Kwak, Hyeyoon Chang, et al.
Prostate International|December 31, 2025
Artificial intelligence-driven digital pathology in urological cancers: current trends and future directionsInyoung Paik, Geongyu Lee, Joonho Lee, et al.
NPJ Digital Medicine|June 15, 2021
Yet Another Automated Gleason Grading System (YAAGGS) by weakly supervised deep learningYechan Mun, Inyoung Paik, Su-Jin Shin, et al.
Scientific Reports|October 8, 2025
Assessing the risk of recurrence in early-stage breast cancer through H&E stained whole slide imagesGeongyu Lee, Joonho Lee, Tae-Yeong Kwak, et al.
Bioengineering (Basel, Switzerland)|May 25, 2024
MurSS: A Multi-Resolution Selective Segmentation Model for Breast CancerJoonho Lee, Geongyu Lee, Tae-Yeong Kwak, et al.
Journal of Pathology and Translational Medicine|January 3, 2019
Artificial Intelligence in PathologyHye Yoon Chang, Chan Kwon Jung, Junwoo Isaac Woo, et al.
Journal of Pathology and Translational Medicine|February 13, 2020
Introduction to digital pathology and computer-aided pathologySoojeong Nam, Yosep Chong, Chan Kwon Jung, et al.
Cancers|November 27, 2019
Automated Gleason Scoring and Tumor Quantification in Prostate Core Needle Biopsy Images Using Deep Neural Networks and Its Comparison with Pathologist-Based AssessmentHan Suk Ryu, Min-Sun Jin, Jeong Hwan Park, et al.
Scientific Reports|March 31, 2025
Clinical implications of deep learning based image analysis of whole radical prostatectomy specimensTae-Yeong Kwak, Chan Ho Lee, Won Young Park, et al.
Pageof 1

Showing results (1-10 of 9) with videos related to

Sort By:
Pageof 1
Bioengineering (Basel, Switzerland)|November 25, 2023
RCKD: Response-Based Cross-Task Knowledge Distillation for Pathological Image AnalysisHyunil Kim, Tae-Yeong Kwak, Hyeyoon Chang, et al.
Prostate International|December 31, 2025
Artificial intelligence-driven digital pathology in urological cancers: current trends and future directionsInyoung Paik, Geongyu Lee, Joonho Lee, et al.
NPJ Digital Medicine|June 15, 2021
Yet Another Automated Gleason Grading System (YAAGGS) by weakly supervised deep learningYechan Mun, Inyoung Paik, Su-Jin Shin, et al.
Scientific Reports|October 8, 2025
Assessing the risk of recurrence in early-stage breast cancer through H&E stained whole slide imagesGeongyu Lee, Joonho Lee, Tae-Yeong Kwak, et al.
Bioengineering (Basel, Switzerland)|May 25, 2024
MurSS: A Multi-Resolution Selective Segmentation Model for Breast CancerJoonho Lee, Geongyu Lee, Tae-Yeong Kwak, et al.
Journal of Pathology and Translational Medicine|January 3, 2019
Artificial Intelligence in PathologyHye Yoon Chang, Chan Kwon Jung, Junwoo Isaac Woo, et al.
Journal of Pathology and Translational Medicine|February 13, 2020
Introduction to digital pathology and computer-aided pathologySoojeong Nam, Yosep Chong, Chan Kwon Jung, et al.
Cancers|November 27, 2019
Automated Gleason Scoring and Tumor Quantification in Prostate Core Needle Biopsy Images Using Deep Neural Networks and Its Comparison with Pathologist-Based AssessmentHan Suk Ryu, Min-Sun Jin, Jeong Hwan Park, et al.
Scientific Reports|March 31, 2025
Clinical implications of deep learning based image analysis of whole radical prostatectomy specimensTae-Yeong Kwak, Chan Ho Lee, Won Young Park, et al.
Pageof 1