Zobrazeno 1 - 10
of 309
pro vyhledávání: '"KIM, BYUNG HAK"'
Autor:
Qiu, Jielin, Huang, Peide, Nakashima, Makiya, Lee, Jaehyun, Zhu, Jiacheng, Tang, Wilson, Chen, Pohao, Nguyen, Christopher, Kim, Byung-Hak, Kwon, Debbie, Weber, Douglas, Zhao, Ding, Chen, David
Self-supervised learning is crucial for clinical imaging applications, given the lack of explicit labels in healthcare. However, conventional approaches that rely on precise vision-language alignment are not always feasible in complex clinical imagin
Externí odkaz:
http://arxiv.org/abs/2304.07675
Recent advances in self-supervised learning (SSL) using large models to learn visual representations from natural images are rapidly closing the gap between the results produced by fully supervised learning and those produced by SSL on downstream vis
Externí odkaz:
http://arxiv.org/abs/2211.01165
Autor:
Kim, Byung-Hak
Prediction of medical codes from clinical notes is a practical and essential need for every healthcare delivery organization within current medical systems. Automating annotation will save significant time and excessive effort that human coders spend
Externí odkaz:
http://arxiv.org/abs/2210.16850
The medical codes prediction problem from clinical notes has received substantial interest in the NLP community, and several recent studies have shown the state-of-the-art (SOTA) code prediction results of full-fledged deep learning-based methods. Ho
Externí odkaz:
http://arxiv.org/abs/2210.15882
Autor:
Kim, Byung-Hak, Ganapathi, Varun
Prediction of medical codes from clinical notes is both a practical and essential need for every healthcare delivery organization within current medical systems. Automating annotation will save significant time and excessive effort spent by human cod
Externí odkaz:
http://arxiv.org/abs/2107.10650
Each year, almost 10% of claims are denied by payers (i.e., health insurance plans). With the cost to recover these denials and underpayments, predicting payer response (likelihood of payment) from claims data with a high degree of accuracy and preci
Externí odkaz:
http://arxiv.org/abs/2007.06229
Autor:
Kim, Eun-A, Lee, Ye-Rim, Lee, Eun-Hyeong, Jeong, Hyun-Mo, Kang, Byung Sik, Kim, Byung-Hak, Park, Sang Jae, Shim, Jae-Hoon
Publikováno v:
In International Journal of Biological Macromolecules 1 October 2023 250
Autor:
Kim, Byung-Hak, Ganapathi, Varun
We present Lumi\`ereNet, a simple, modular, and completely deep-learning based architecture that synthesizes, high quality, full-pose headshot lecture videos from instructor's new audio narration of any length. Unlike prior works, Lumi\`ereNet is ent
Externí odkaz:
http://arxiv.org/abs/1907.02253
Autor:
Kim, Byung-Hak
The increasingly fast development cycle for online course contents, along with the diverse student demographics in each online classroom, make real-time student outcomes prediction an interesting topic for both industrial research and practical needs
Externí odkaz:
http://arxiv.org/abs/1905.02530