26 citations · 58 across the 12 of their papers we have counts for
8 papers · 1 filter
The hydrostatic limit of the Beris-Edwards system in dimension two
Xingyu Li, Marius Paicu, Arghir Zarnescu
We study the scaled anisotropic co-rotational Beris-Edwards system modeling the hydrodynamic motion of nematic liquid crystals in dimension two. We prove the global well-posedness…
Common Corruption Robustness of Point Cloud Detectors: Benchmark and Enhancement
Shuangzhi Li, Zhijie Wang, Felix Juefei-Xu +3
Object detection through LiDAR-based point cloud has recently been important in autonomous driving. Although achieving high accuracy on public benchmarks, the state-of-the-art dete…
A robust and lightweight deep attention multiple instance learning algorithm for predicting genetic alterations
Bangwei Guo, Xingyu Li, Miaomiao Yang +2
Deep-learning models based on whole-slide digital pathology images (WSIs) become increasingly popular for predicting molecular biomarkers. Instance-based models has been the mainst…
Global Contrast Masked Autoencoders Are Powerful Pathological Representation Learners
Hao Quan, Xingyu Li, Weixing Chen +7
Based on digital pathology slice scanning technology, artificial intelligence algorithms represented by deep learning have achieved remarkable results in the field of computational…
SHAPE: An Unified Approach to Evaluate the Contribution and Cooperation of Individual Modalities
Pengbo Hu, Xingyu Li, Yi Zhou
As deep learning advances, there is an ever-growing demand for models capable of synthesizing information from multi-modal resources to address the complex tasks raised from real-l…
Adversarial Fine-tune with Dynamically Regulated Adversary
Pengyue Hou, Ming Zhou, Jie Han +2
Adversarial training is an effective method to boost model robustness to malicious, adversarial attacks. However, such improvement in model robustness often leads to a significant…
Optimize Deep Learning Models for Prediction of Gene Mutations Using Unsupervised Clustering
Zihan Chen, Xingyu Li, Miaomiao Yang +2
Deep learning has become the mainstream methodological choice for analyzing and interpreting whole-slide digital pathology images (WSIs). It is commonly assumed that tumor regions…
Colorectal cancer survival prediction using deep distribution based multiple-instance learning
Xingyu Li, Jitendra Jonnagaddala, Min Cen +2
Several deep learning algorithms have been developed to predict survival of cancer patients using whole slide images (WSIs).However, identification of image phenotypes within the W…