no code implementations • 1 Jan 2025 • Haoyang Li, Yuming Xu, Chen Jason Zhang, Alexander Zhou, Lei Chen, Qing Li
Graph-level tasks, which predict properties or classes for the entire graph, are critical for applications, such as molecular property prediction and subgraph counting.
no code implementations • 26 Nov 2024 • Yufan Zheng, Wei Jiang, Alexander Zhou, Nguyen Quoc Viet Hung, Choujun Zhan, Tong Chen
With the time-varying mechanistic affinity graphs computed with the epidemiology-informed location embeddings, a heterogeneous transmission graph network is designed to encode the mechanistic heterogeneity among locations, providing additional predictive signals to facilitate accurate forecasting.
1 code implementation • 1 Feb 2024 • Zelong Liu, Andrew Tieu, Nikhil Patel, Georgios Soultanidis, Louisa Deyer, Ying Wang, Sean Huver, Alexander Zhou, Yunhao Mei, Zahi A. Fayad, Timothy Deyer, Xueyan Mei
VIS-MAE represents a significant advancement in medical imaging AI, offering a generalizable and robust solution for improving segmentation and classification tasks while reducing the data annotation workload.
no code implementations • 1 Feb 2024 • Alexander Zhou, Zelong Liu, Andrew Tieu, Nikhil Patel, Sean Sun, Anthony Yang, Peter Choi, Valentin Fauveau, George Soultanidis, Mingqian Huang, Amish Doshi, Zahi A. Fayad, Timothy Deyer, Xueyan Mei
The external dataset was used to evaluate nnU-Net model generalizability and performance in all classes on independent imaging data.
no code implementations • 10 Dec 2023 • Zelong Liu, Alexander Zhou, Arnold Yang, Alara Yilmaz, Maxwell Yoo, Mikey Sullivan, Catherine Zhang, James Grant, Daiqing Li, Zahi A. Fayad, Sean Huver, Timothy Deyer, Xueyan Mei
We showed that using synthetic auto-labeled data from RadImageGAN can significantly improve performance on four diverse downstream segmentation datasets by augmenting real training data and/or developing pre-trained weights for fine-tuning.
no code implementations • 2 Apr 2021 • Qinyong Wang, Hongzhi Yin, Tong Chen, Junliang Yu, Alexander Zhou, Xiangliang Zhang
In the mobile Internet era, the recommender system has become an irreplaceable tool to help users discover useful items, and thus alleviating the information overload problem.