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pro vyhledávání: '"Cheung, A. K."'
Integrating multi-modal clinical data, such as electronic health records (EHR) and chest X-ray images (CXR), is particularly beneficial for clinical prediction tasks. However, in a temporal setting, multi-modal data are often inherently asynchronous.
Externí odkaz:
http://arxiv.org/abs/2410.17918
As the importance of comprehensive evaluation in workshop courses increases, there is a growing demand for efficient and fair assessment methods that reduce the workload for faculty members. This paper presents an evaluation conducted with Large Lang
Externí odkaz:
http://arxiv.org/abs/2405.18632
The combination of electronic health records (EHR) and medical images is crucial for clinicians in making diagnoses and forecasting prognosis. Strategically fusing these two data modalities has great potential to improve the accuracy of machine learn
Externí odkaz:
http://arxiv.org/abs/2403.06197
Publikováno v:
2023 IEEE 33rd International Workshop on Machine Learning for Signal Processing (MLSP)
Low-count time series describe sparse or intermittent events, which are prevalent in large-scale online platforms that capture and monitor diverse data types. Several distinct challenges surface when modelling low-count time series, particularly low
Externí odkaz:
http://arxiv.org/abs/2308.12925
Autor:
Yan, Sixing, Cheung, William K., Chiu, Keith, Tong, Terence M., Cheung, Charles K., See, Simon
Automatic generation of medical reports from X-ray images can assist radiologists to perform the time-consuming and yet important reporting task. Yet, achieving clinically accurate generated reports remains challenging. Modeling the underlying abnorm
Externí odkaz:
http://arxiv.org/abs/2207.01208
Aligning users across networks using graph representation learning has been found effective where the alignment is accomplished in a low-dimensional embedding space. Yet, achieving highly precise alignment is still challenging, especially when nodes
Externí odkaz:
http://arxiv.org/abs/2212.14182
We show that the Unconstrained Traveling Tournament Problem (UTTP) is APX-complete by presenting an L-reduction from a version of metric (1,2)-TSP to UTTP. Keywords: Traveling Tournament Problem, APX-complete, Approximation algorithms, Traveling Sale
Externí odkaz:
http://arxiv.org/abs/2212.09165
Autor:
Cheung, Christabel K., Lee, Haelim, Francis-Levin, Nina, Choi, Eunju, Geng, Yimin, Thomas, Bria N., Roman, Valentina A., Roth, Michael E.
Publikováno v:
In PEC Innovation 15 December 2024 5
Publikováno v:
In Knowledge-Based Systems 25 October 2024 302
Autor:
Wong, Jessica Y., Lim, Wey Wen, Cheung, Justin K., Murphy, Caitriona, Shiu, Eunice Y.C., Xiao, Jingyi, Chen, Dongxuan, Xie, Yanmin, Li, Mingwei, Xin, Hualei, Szeto, Michelle, Choi, Sammi, Cowling, Benjamin J.
Publikováno v:
In International Journal of Infectious Diseases January 2025 150