S
Sangheum Hwang
Researcher at Seoul National University of Science and Technology
Publications - 56
Citations - 1289
Sangheum Hwang is an academic researcher from Seoul National University of Science and Technology. The author has contributed to research in topics: Computer science & Supervised learning. The author has an hindex of 15, co-authored 51 publications receiving 860 citations. Previous affiliations of Sangheum Hwang include Samsung & KAIST.
Papers
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Journal ArticleDOI
Development and Validation of Deep Learning-based Automatic Detection Algorithm for Malignant Pulmonary Nodules on Chest Radiographs.
Ju Gang Nam,Sunggyun Park,Eui Jin Hwang,Jong Hyuk Lee,Kwang Nam Jin,Kun Young Lim,Thienkai Huy Vu,Jae Ho Sohn,Sangheum Hwang,Jin Mo Goo,Chang Min Park +10 more
TL;DR: This deep learning-based automatic detection algorithm outperformed physicians in radiograph classification and nodule detection performance for malignant pulmonary nodules on chest radiographs, and it enhanced physicians' performances when used as a second reader.
Journal ArticleDOI
Predicting breast tumor proliferation from whole-slide images: The TUPAC16 challenge.
Mitko Veta,Yujing J. Heng,Nikolas Stathonikos,Babak Ehteshami Bejnordi,Francisco Beca,Thomas Wollmann,Karl Rohr,Manan Shah,Dayong Wang,Mikael Rousson,Martin Hedlund,David Tellez,Francesco Ciompi,Erwan Zerhouni,David Lanyi,Matheus P. Viana,Vassili Kovalev,Vitali Liauchuk,Hady Ahmady Phoulady,Talha Qaiser,Simon Graham,Nasir M. Rajpoot,Erik Sjöblom,Jesper Molin,Kyunghyun Paeng,Sangheum Hwang,Sunggyun Park,Zhipeng Jia,Eric Chang,Yan Xu,Andrew H. Beck,Paul J. van Diest,Josien P. W. Pluim +32 more
TL;DR: The achieved results are promising given the difficulty of the tasks and weakly‐labeled nature of the ground truth, however, further research is needed to improve the practical utility of image analysis methods for this task.
Proceedings ArticleDOI
A novel approach for tuberculosis screening based on deep convolutional neural networks
TL;DR: This work designed CAD system based on deep CNN for automatic TB screening based on large-scale chest X-rays, which achieved viable TB screening performance of 0.96, 0.93 and 0.88 in terms of AUC for three real field datasets, respectively, by exploiting the effect of transfer learning.
Book ChapterDOI
Self-Transfer Learning for Weakly Supervised Lesion Localization
Sangheum Hwang,Hyo-Eun Kim +1 more
TL;DR: This work presents a novel weakly supervised learning framework for lesion localization named as self-transfer learning (STL), which jointly optimizes both classification and localization networks to help the localization network focus on correct lesions without any types of priors.
Book ChapterDOI
A Unified Framework for Tumor Proliferation Score Prediction in Breast Histopathology
TL;DR: In this article, a unified framework was proposed to predict tumor proliferation scores from breast histopathology whole slide images, which achieved the first place in all three tasks in Tumor Proliferation Assessment Challenge 2016.