50 citations · 111 across the 13 of their papers we have counts for
7 papers · 1 filter
SCRNet: a Retinex Structure-based Low-light Enhancement Model Guided by Spatial Consistency
Miao Zhang, Yiqing Shen, Shenghui Zhong
Images captured under low-light conditions are often plagued by several challenges, including diminished contrast, increased noise, loss of fine details, and unnatural color reprod…
MoViT: Memorizing Vision Transformers for Medical Image Analysis
Yiqing Shen, Pengfei Guo, Jingpu Wu +5
The synergy of long-range dependencies from transformers and local representations of image content from convolutional neural networks (CNNs) has led to advanced architectures and…
Federated Learning for Chronic Obstructive Pulmonary Disease Classification with Partial Personalized Attention Mechanism
Yiqing Shen, Baiyun Liu, Ruize Yu +4
Chronic Obstructive Pulmonary Disease (COPD) is the fourth leading cause of death worldwide. Yet, COPD diagnosis heavily relies on spirometric examination as well as functional air…
RandStainNA: Learning Stain-Agnostic Features from Histology Slides by Bridging Stain Augmentation and Normalization
Yiqing Shen, Yulin Luo, Dinggang Shen +1
Stain variations often decrease the generalization ability of deep learning based approaches in digital histopathology analysis. Two separate proposals, namely stain normalization…
Mixed Sample Augmentation for Online Distillation
Yiqing Shen, Liwu Xu, Yuzhe Yang +2
Mixed Sample Regularization (MSR), such as MixUp or CutMix, is a powerful data augmentation strategy to generalize convolutional neural networks. Previous empirical analysis has il…
CD-pFed: Cyclic Distillation-guided Channel Decoupling for Model Personalization in Federated Learning
Yiqing Shen, Yuyin Zhou, Lequan Yu
Federated learning (FL) is a distributed learning paradigm that enables multiple clients to collaboratively learn a shared global model. Despite the recent progress, it remains cha…
Self-Distillation from the Last Mini-Batch for Consistency Regularization
Yiqing Shen, Liwu Xu, Yuzhe Yang +2
Knowledge distillation (KD) shows a bright promise as a powerful regularization strategy to boost generalization ability by leveraging learned sample-level soft targets. Yet, emplo…