no code implementations • 19 Feb 2024 • Hejie Cui, Xinyu Fang, ran Xu, Xuan Kan, Joyce C. Ho, Carl Yang
While there has been a lot of research on representation learning of structured EHR data, the fusion of different types of EHR data (multimodal fusion) is not well studied.
1 code implementation • 1 Nov 2023 • ran Xu, Hejie Cui, Yue Yu, Xuan Kan, Wenqi Shi, Yuchen Zhuang, Wei Jin, Joyce Ho, Carl Yang
Clinical natural language processing requires methods that can address domain-specific challenges, such as complex medical terminology and clinical contexts.
1 code implementation • 5 Sep 2023 • Xuan Kan, Antonio Aodong Chen Gu, Hejie Cui, Ying Guo, Carl Yang
However, the conventional approach involving static brain network analysis offers limited potential in capturing the dynamism of brain function.
no code implementations • 7 Jun 2023 • Hejie Cui, Jiaying Lu, Shiyu Wang, ran Xu, Wenjing Ma, Shaojun Yu, Yue Yu, Xuan Kan, Chen Ling, Tianfan Fu, Liang Zhao, Joyce Ho, Fei Wang, Carl Yang
This work aims to serve as a valuable resource for understanding the potential and opportunities of HKG in health research.
no code implementations • 5 Jun 2023 • Xuan Kan, Zimu Li, Hejie Cui, Yue Yu, ran Xu, Shaojun Yu, Zilong Zhang, Ying Guo, Carl Yang
Biological networks are commonly used in biomedical and healthcare domains to effectively model the structure of complex biological systems with interactions linking biological entities.
1 code implementation • 6 May 2023 • Wei Dai, Hejie Cui, Xuan Kan, Ying Guo, Sanne van Rooij, Carl Yang
Brain networks, graphical models such as those constructed from MRI, have been widely used in pathological prediction and analysis of brain functions.
1 code implementation • 10 Jan 2023 • ran Xu, Yue Yu, Hejie Cui, Xuan Kan, Yanqiao Zhu, Joyce Ho, Chao Zhang, Carl Yang
Our further analysis demonstrates that our proposed data selection strategy reduces the noise of pseudo labels by 36. 8% and saves 57. 3% of the time when compared with the best baseline.
1 code implementation • 1 Nov 2022 • Yue Yu, Xuan Kan, Hejie Cui, ran Xu, Yujia Zheng, Xiangchen Song, Yanqiao Zhu, Kun Zhang, Razieh Nabi, Ying Guo, Chao Zhang, Carl Yang
To better adapt GNNs for fMRI analysis, we propose TBDS, an end-to-end framework based on \underline{T}ask-aware \underline{B}rain connectivity \underline{D}AG (short for Directed Acyclic Graph) \underline{S}tructure generation for fMRI analysis.
2 code implementations • 13 Oct 2022 • Xuan Kan, Wei Dai, Hejie Cui, Zilong Zhang, Ying Guo, Carl Yang
Human brains are commonly modeled as networks of Regions of Interest (ROIs) and their connections for the understanding of brain functions and mental disorders.
1 code implementation • 9 Jun 2022 • Yi Yang, Yanqiao Zhu, Hejie Cui, Xuan Kan, Lifang He, Ying Guo, Carl Yang
Specifically, we propose to meta-train the model on datasets of large sample sizes and transfer the knowledge to small datasets.
1 code implementation • 25 May 2022 • Xuan Kan, Hejie Cui, Joshua Lukemire, Ying Guo, Carl Yang
In particular, we formulate (1) prominent region of interest (ROI) features extraction, (2) brain networks generation, and (3) clinical predictions with GNNs, in an end-to-end trainable model under the guidance of particular prediction tasks.
1 code implementation • 17 Mar 2022 • Hejie Cui, Wei Dai, Yanqiao Zhu, Xuan Kan, Antonio Aodong Chen Gu, Joshua Lukemire, Liang Zhan, Lifang He, Ying Guo, Carl Yang
To bridge this gap, we present BrainGB, a benchmark for brain network analysis with GNNs.
no code implementations • 23 Jul 2021 • Xuan Kan, Hejie Cui, Ying Guo, Carl Yang
Recent studies in neuroscience show great potential of functional brain networks constructed from fMRI data for popularity modeling and clinical predictions.
1 code implementation • 11 Jul 2021 • Xuan Kan, Hejie Cui, Carl Yang
Relation prediction among entities in images is an important step in scene graph generation (SGG), which further impacts various visual understanding and reasoning tasks.
1 code implementation • 14 Aug 2019 • Chris Xiaoxuan Lu, Xuan Kan, Bowen Du, Changhao Chen, Hongkai Wen, Andrew Markham, Niki Trigoni, John Stankovic
Inspired by the fact that most people carry smart wireless devices with them, e. g. smartphones, we propose to use this wireless identifier as a supervisory label.