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Confidence-Aware Learning for Deep Neural Networks


Jul 07, 2020
Jooyoung Moon, Jihyo Kim, Younghak Shin, Sangheum Hwang

* ICML 2020. The first two authors contributed equally 

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Self-Knowledge Distillation: A Simple Way for Better Generalization


Jun 22, 2020
Kyungyul Kim, ByeongMoon Ji, Doyoung Yoon, Sangheum Hwang

* Under review 

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Predicting breast tumor proliferation from whole-slide images: the TUPAC16 challenge


Jul 22, 2018
Mitko Veta, Yujing J. Heng, Nikolas Stathonikos, Babak Ehteshami Bejnordi, Francisco Beca, Thomas Wollmann, Karl Rohr, Manan A. Shah, Dayong Wang, Mikael Rousson, Martin Hedlund, David Tellez, Francesco Ciompi, Erwan Zerhouni, David Lanyi, Matheus Viana, Vassili Kovalev, Vitali Liauchuk, Hady Ahmady Phoulady, Talha Qaiser, Simon Graham, Nasir Rajpoot, Erik Sjöblom, Jesper Molin, Kyunghyun Paeng, Sangheum Hwang, Sunggyun Park, Zhipeng Jia, Eric I-Chao Chang, Yan Xu, Andrew H. Beck, Paul J. van Diest, Josien P. W. Pluim

* Overview paper of the TUPAC16 challenge: http://tupac.tue-image.nl/ 

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A Unified Framework for Tumor Proliferation Score Prediction in Breast Histopathology


Aug 11, 2017
Kyunghyun Paeng, Sangheum Hwang, Sunggyun Park, Minsoo Kim

* Accepted to the 3rd Workshop on Deep Learning in Medical Image Analysis (DLMIA 2017), MICCAI 2017 

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Accurate Lung Segmentation via Network-Wise Training of Convolutional Networks


Aug 02, 2017
Sangheum Hwang, Sunggyun Park

* Accepted to the 3rd Workshop on Deep Learning in Medical Image Analysis (DLMIA 2017), MICCAI 2017 

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Semantic Noise Modeling for Better Representation Learning


Nov 04, 2016
Hyo-Eun Kim, Sangheum Hwang, Kyunghyun Cho


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Deconvolutional Feature Stacking for Weakly-Supervised Semantic Segmentation


Mar 12, 2016
Hyo-Eun Kim, Sangheum Hwang


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Self-Transfer Learning for Fully Weakly Supervised Object Localization


Feb 04, 2016
Sangheum Hwang, Hyo-Eun Kim

* 9 pages, 4 figures 

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