3 citations · 12 across the 34 of their papers we have counts for
11 papers · 1 filter
Multimodal Variational Autoencoder: a Barycentric View
Peijie Qiu, Wenhui Zhu, Sayantan Kumar +6
Multiple signal modalities, such as vision and sounds, are naturally present in real-world phenomena. Recently, there has been growing interest in learning generative models, in pa…
Many-MobileNet: Multi-Model Augmentation for Robust Retinal Disease Classification
Hao Wang, Wenhui Zhu, Xuanzhao Dong +9
In this work, we propose Many-MobileNet, an efficient model fusion strategy for retinal disease classification using lightweight CNN architecture. Our method addresses key challeng…
TPOT: Topology Preserving Optimal Transport in Retinal Fundus Image Enhancement
Xuanzhao Dong, Wenhui Zhu, Xin Li +4
Retinal fundus photography enhancement is important for diagnosing and monitoring retinal diseases. However, early approaches to retinal image enhancement, such as those based on G…
EViT-Unet: U-Net Like Efficient Vision Transformer for Medical Image Segmentation on Mobile and Edge Devices
Xin Li, Wenhui Zhu, Xuanzhao Dong +2
With the rapid development of deep learning, CNN-based U-shaped networks have succeeded in medical image segmentation and are widely applied for various tasks. However, their limit…
STA-Unet: Rethink the semantic redundant for Medical Imaging Segmentation
Vamsi Krishna Vasa, Wenhui Zhu, Xiwen Chen +3
In recent years, significant progress has been made in the medical image analysis domain using convolutional neural networks (CNNs). In particular, deep neural networks based on a…
CUNSB-RFIE: Context-aware Unpaired Neural Schrödinger Bridge in Retinal Fundus Image Enhancement
Xuanzhao Dong, Vamsi Krishna Vasa, Wenhui Zhu +7
Retinal fundus photography is significant in diagnosing and monitoring retinal diseases. However, systemic imperfections and operator/patient-related factors can hinder the acquisi…
Context-Aware Optimal Transport Learning for Retinal Fundus Image Enhancement
Vamsi Krishna Vasa, Peijie Qiu, Wenhui Zhu +3
Retinal fundus photography offers a non-invasive way to diagnose and monitor a variety of retinal diseases, but is prone to inherent quality glitches arising from systemic imperfec…
DGR-MIL: Exploring Diverse Global Representation in Multiple Instance Learning for Whole Slide Image Classification
Wenhui Zhu, Xiwen Chen, Peijie Qiu +3
Multiple instance learning (MIL) stands as a powerful approach in weakly supervised learning, regularly employed in histological whole slide image (WSI) classification for detectin…
SelfReg-UNet: Self-Regularized UNet for Medical Image Segmentation
Wenhui Zhu, Xiwen Chen, Peijie Qiu +4
Since its introduction, UNet has been leading a variety of medical image segmentation tasks. Although numerous follow-up studies have also been dedicated to improving the performan…
TimeMIL: Advancing Multivariate Time Series Classification via a Time-aware Multiple Instance Learning
Xiwen Chen, Peijie Qiu, Wenhui Zhu +5
Deep neural networks, including transformers and convolutional neural networks, have significantly improved multivariate time series classification (MTSC). However, these methods o…
Reconstructing Retinal Visual Images from 3T fMRI Data Enhanced by Unsupervised Learning
Yujian Xiong, Wenhui Zhu, Zhong-Lin Lu +1
The reconstruction of human visual inputs from brain activity, particularly through functional Magnetic Resonance Imaging (fMRI), holds promising avenues for unraveling the mechani…