1 citations · 2 across the 16 of their papers we have counts for
17 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…
Bridging Restoration and Diagnosis: A Comprehensive Benchmark for Retinal Fundus Enhancement
Xuanzhao Dong, Wenhui Zhu, Xiwen Chen +8
Over the past decade, generative models have demonstrated success in enhancing fundus images. However, the evaluation of these models remains a challenge. A benchmark for fundus im…
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…
EZBlender: Efficient 3D Editing with Plan-and-ReAct Agent
Hao Wang, Wenhui Zhu, Shao Tang +8
As a cornerstone of the modern digital economy, 3D modeling and rendering demand substantial resources and manual effort when scene editing is performed in the traditional manner.…
AHA: Aligning Large Audio-Language Models for Reasoning Hallucinations via Counterfactual Hard Negatives
Yanxi Chen, Wenhui Zhu, Xiwen Chen +9
Although Large Audio-Language Models (LALMs) deliver state-of-the-art (SOTA) performance, they frequently suffer from hallucinations, e.g. generating text not grounded in the audio…
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…
LLaDA-MedV: Exploring Large Language Diffusion Models for Biomedical Image Understanding
Xuanzhao Dong, Wenhui Zhu, Xiwen Chen +5
Autoregressive models (ARMs) have long dominated the landscape of biomedical vision-language models (VLMs). Recently, masked diffusion models such as LLaDA have emerged as promisin…
Toward Effective Reinforcement Learning Fine-Tuning for Medical VQA in Vision-Language Models
Wenhui Zhu, Xuanzhao Dong, Xin Li +6
Recently, reinforcement learning (RL)-based tuning has shifted the trajectory of Multimodal Large Language Models (MLLMs), particularly following the introduction of Group Relative…
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…
EyeBench: A Call for More Rigorous Evaluation of Retinal Image Enhancement
Wenhui Zhu, Xuanzhao Dong, Xin Li +8
Over the past decade, generative models have achieved significant success in enhancement fundus images.However, the evaluation of these models still presents a considerable challen…
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…
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…
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…