26 citations · 58 across the 12 of their papers we have counts for
12 papers
Distilling High Diagnostic Value Patches for Whole Slide Image Classification Using Attention Mechanism
Tianhang Nan, Hao Quan, Yong Ding +3
Multiple Instance Learning (MIL) has garnered widespread attention in the field of Whole Slide Image (WSI) classification as it replaces pixel-level manual annotation with diagnost…
DPSeq: A Novel and Efficient Digital Pathology Classifier for Predicting Cancer Biomarkers using Sequencer Architecture
Min Cen, Xingyu Li, Bangwei Guo +3
In digital pathology tasks, transformers have achieved state-of-the-art results, surpassing convolutional neural networks (CNNs). However, transformers are usually complex and reso…
Time to Embrace Natural Language Processing (NLP)-based Digital Pathology: Benchmarking NLP- and Convolutional Neural Network-based Deep Learning Pipelines
Min Cen, Xingyu Li, Bangwei Guo +3
NLP-based computer vision models, particularly vision transformers, have been shown to outperform CNN models in many imaging tasks. However, most digital pathology artificial-intel…
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…
Learning with Instance-Dependent Label Noise: A Sample Sieve Approach
Hao Cheng, Zhaowei Zhu, Xingyu Li +3
Human-annotated labels are often prone to noise, and the presence of such noise will degrade the performance of the resulting deep neural network (DNN) models. Much of the literatu…