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pro vyhledávání: '"Lee, Byeong Tak"'
Domain generalization aim to train models to effectively perform on samples that are unseen and outside of the distribution. Adversarial data augmentation (ADA) is a widely used technique in domain generalization. It enhances the model robustness by
Externí odkaz:
http://arxiv.org/abs/2407.15174
Autor:
Song, Junho, Jang, Jong-Hwan, Lee, Byeong Tak, Hong, DongGyun, Kwon, Joon-myoung, Jo, Yong-Yeon
Using foundation models enhanced by self-supervised learning (SSL) methods presents an innovative approach to electrocardiogram (ECG) analysis, which is crucial for cardiac health monitoring and diagnosis. This study comprehensively evaluates foundat
Externí odkaz:
http://arxiv.org/abs/2407.07110
Autor:
Kim, Kyung Geun, Lee, Byeong Tak
Many diverse phenomena in nature often inherently encode both short- and long-term temporal dependencies, which especially result from the direction of the flow of time. In this respect, we discovered experimental evidence suggesting that interrelati
Externí odkaz:
http://arxiv.org/abs/2310.18932
We study scaling convolutional neural networks (CNNs), specifically targeting Residual neural networks (ResNet), for analyzing electrocardiograms (ECGs). Although ECG signals are time-series data, CNN-based models have been shown to outperform other
Externí odkaz:
http://arxiv.org/abs/2308.12492
Autor:
Kim, Kyung Geun, Lee, Byeong Tak
In this paper, we propose a novel graph-based data augmentation method that can generally be applied to medical waveform data with graph structures. In the process of recording medical waveform data, such as electrocardiogram (ECG) or electroencephal
Externí odkaz:
http://arxiv.org/abs/2205.14619
Publikováno v:
Critical Care Medicine; Nov2020, Vol. 48 Issue 11, pe1106-e1111, 6p
Autor:
Han, Changho1 (AUTHOR), Song, Youngjae2 (AUTHOR), Lim, Hong-Seok3 (AUTHOR), Tae, Yunwon2 (AUTHOR), Jang, Jong-Hwan1 (AUTHOR), Lee, Byeong Tak2 (AUTHOR), Lee, Yeha2 (AUTHOR), Bae, Woong2 (AUTHOR), Yoon, Dukyong1,4 (AUTHOR)
Publikováno v:
Journal of Medical Internet Research. Sep2021, Vol. 23 Issue 9, pN.PAG-N.PAG. 1p. 3 Charts.
Publikováno v:
Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference [Annu Int Conf IEEE Eng Med Biol Soc] 2021 Nov; Vol. 2021, pp. 591-594.