Zobrazeno 1 - 10
of 119
pro vyhledávání: '"Yoo, Donggeun"'
Autor:
Brattoli, Biagio, Mostafavi, Mohammad, Lee, Taebum, Jung, Wonkyung, Ryu, Jeongun, Park, Seonwook, Park, Jongchan, Pereira, Sergio, Shin, Seunghwan, Choi, Sangjoon, Kim, Hyojin, Yoo, Donggeun, Ali, Siraj M., Paeng, Kyunghyun, Ock, Chan-Young, Cho, Soo Ick, Kim, Seokhwi
Despite advancements in methodologies, immunohistochemistry (IHC) remains the most utilized ancillary test for histopathologic and companion diagnostics in targeted therapies. However, objective IHC assessment poses challenges. Artificial intelligenc
Externí odkaz:
http://arxiv.org/abs/2407.20643
Bayesian optimization (BO) has contributed greatly to improving model performance by suggesting promising hyperparameter configurations iteratively based on observations from multiple training trials. However, only partial knowledge (i.e., the measur
Externí odkaz:
http://arxiv.org/abs/2304.12666
Autor:
Ryu, Jeongun, Puche, Aaron Valero, Shin, JaeWoong, Park, Seonwook, Brattoli, Biagio, Lee, Jinhee, Jung, Wonkyung, Cho, Soo Ick, Paeng, Kyunghyun, Ock, Chan-Young, Yoo, Donggeun, Pereira, Sérgio
Cell detection is a fundamental task in computational pathology that can be used for extracting high-level medical information from whole-slide images. For accurate cell detection, pathologists often zoom out to understand the tissue-level structures
Externí odkaz:
http://arxiv.org/abs/2303.13110
Computational pathology can lead to saving human lives, but models are annotation hungry and pathology images are notoriously expensive to annotate. Self-supervised learning has shown to be an effective method for utilizing unlabeled data, and its ap
Externí odkaz:
http://arxiv.org/abs/2212.04690
Autor:
Yoo, Donggeun1 (AUTHOR) ehdrms6832@gmail.com, Park, Sujung1 (AUTHOR), Oh, Sohyeong1 (AUTHOR), Kim, Minsoo P.1 (AUTHOR) mspkim@scnu.ac.kr, Park, Kwonpil1 (AUTHOR) mspkim@scnu.ac.kr
Publikováno v:
Materials (1996-1944). Sep2024, Vol. 17 Issue 17, p4425. 13p.
Despite the evolution of Convolutional Neural Networks (CNNs), their performance is surprisingly dependent on the choice of hyperparameters. However, it remains challenging to efficiently explore large hyperparameter search space due to the long trai
Externí odkaz:
http://arxiv.org/abs/2209.12499
Autor:
Lee, Chunggi, Park, Seonwook, Song, Heon, Ryu, Jeongun, Kim, Sanghoon, Kim, Haejoon, Pereira, Sérgio, Yoo, Donggeun
Annotating tens or hundreds of tiny objects in a given image is laborious yet crucial for a multitude of Computer Vision tasks. Such imagery typically contains objects from various categories, yet the multi-class interactive annotation setting for th
Externí odkaz:
http://arxiv.org/abs/2203.15266
Publikováno v:
In International Journal of Hydrogen Energy 11 November 2024 90:635-645
Finding diseases from an X-ray image is an important yet highly challenging task. Current methods for solving this task exploit various characteristics of the chest X-ray image, but one of the most important characteristics is still missing: the nece
Externí odkaz:
http://arxiv.org/abs/2007.07506