2 citations · 6 across the 15 of their papers we have counts for
16 papers
Does Your ViT Still Need U-Net for Segmentation?
Xin Li, Wenhui Zhu, Xuanzhao Dong +6
Medical image segmentation is dominated by U-Net-style encoder-decoder architectures. Vision Transformers (ViTs) overcome the limited receptive field of convolutional networks thro…
SGW-GAN: Sliced Gromov-Wasserstein Guided GANs for Retinal Fundus Image Enhancement
Yujian Xiong, Xuanzhao Dong, Wenhui Zhu +3
Retinal fundus photography is indispensable for ophthalmic screening and diagnosis, yet image quality is often degraded by noise, artifacts, and uneven illumination. Recent GAN- an…
nnMobileNet++: Towards Efficient Hybrid Networks for Retinal Image Analysis
Xin Li, Wenhui Zhu, Xuanzhao Dong +4
Retinal imaging is a critical, non-invasive modality for the early detection and monitoring of ocular and systemic diseases. Deep learning, particularly convolutional neural networ…
VAOT: Vessel-Aware Optimal Transport for Retinal Fundus Enhancement
Xuanzhao Dong, Wenhui Zhu, Yujian Xiong +8
Color fundus photography (CFP) is central to diagnosing and monitoring retinal disease, yet its acquisition variability (e.g., illumination changes) often degrades image quality, w…
A BERT-Style Self-Supervised Learning CNN for Disease Identification from Retinal Images
Xin Li, Wenhui Zhu, Peijie Qiu +3
In the field of medical imaging, the advent of deep learning, especially the application of convolutional neural networks (CNNs) has revolutionized the analysis and interpretation…
RetinalGPT: A Retinal Clinical Preference Conversational Assistant Powered by Large Vision-Language Models
Wenhui Zhu, Xin Li, Xiwen Chen +8
Recently, Multimodal Large Language Models (MLLMs) have gained significant attention for their remarkable ability to process and analyze non-textual data, such as images, videos, a…
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…
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…
RBAD: A Dataset and Benchmark for Retinal Vessels Branching Angle Detection
Hao Wang, Wenhui Zhu, Jiayou Qin +5
Detecting retinal image analysis, particularly the geometrical features of branching points, plays an essential role in diagnosing eye diseases. However, existing methods used for…
PDL: Regularizing Multiple Instance Learning with Progressive Dropout Layers
Wenhui Zhu, Peijie Qiu, Xiwen Chen +2
Multiple instance learning (MIL) was a weakly supervised learning approach that sought to assign binary class labels to collections of instances known as bags. However, due to thei…
nnMobileNet: Rethinking CNN for Retinopathy Research
Wenhui Zhu, Peijie Qiu, Xiwen Chen +4
Over the past few decades, convolutional neural networks (CNNs) have been at the forefront of the detection and tracking of various retinal diseases (RD). Despite their success, th…
Optimal Transport Guided Unsupervised Learning for Enhancing low-quality Retinal Images
Wenhui Zhu, Peijie Qiu, Mohammad Farazi +3
Real-world non-mydriatic retinal fundus photography is prone to artifacts, imperfections and low-quality when certain ocular or systemic co-morbidities exist. Artifacts may result…
OTRE: Where Optimal Transport Guided Unpaired Image-to-Image Translation Meets Regularization by Enhancing
Wenhui Zhu, Peijie Qiu, Oana M. Dumitrascu +5
Non-mydriatic retinal color fundus photography (CFP) is widely available due to the advantage of not requiring pupillary dilation, however, is prone to poor quality due to operator…
Self-Supervised Equivariant Regularization Reconciles Multiple Instance Learning: Joint Referable Diabetic Retinopathy Classification and Lesion Segmentation
Wenhui Zhu, Peijie Qiu, Natasha Lepore +2
Lesion appearance is a crucial clue for medical providers to distinguish referable diabetic retinopathy (rDR) from non-referable DR. Most existing large-scale DR datasets contain o…