most citedU-Bench: A Comprehensive Understanding of U-Net through 100-Variant Benchmarking

1 citations · 1 across the 3 of their papers we have counts for

collaborators

6 papers

cs.CV2025

Equivariant Sampling for Improving Diffusion Model-based Image Restoration

Chenxu Wu, Qingpeng Kong, Peiang Zhao +5

Recent advances in generative models, especially diffusion models, have significantly improved image restoration (IR) performance. However, existing problem-agnostic diffusion mode…

cs.CV20251 cited

U-Bench: A Comprehensive Understanding of U-Net through 100-Variant Benchmarking

Fenghe Tang, Chengqi Dong, Wenxin Ma +7

Over the past decade, U-Net has been the dominant architecture in medical image segmentation, leading to the development of thousands of U-shaped variants. Despite its widespread a…

cs.CL2025

A General Knowledge Injection Framework for ICD Coding

Xu Zhang, Kun Zhang, Wenxin Ma +4

ICD Coding aims to assign a wide range of medical codes to a medical text document, which is a popular and challenging task in the healthcare domain. To alleviate the problems of l…

cs.CV2025

AA-CLIP: Enhancing Zero-shot Anomaly Detection via Anomaly-Aware CLIP

Wenxin Ma, Xu Zhang, Qingsong Yao +6

Anomaly detection (AD) identifies outliers for applications like defect and lesion detection. While CLIP shows promise for zero-shot AD tasks due to its strong generalization capab…

cs.CV2025

Hi-End-MAE: Hierarchical encoder-driven masked autoencoders are stronger vision learners for medical image segmentation

Fenghe Tang, Qingsong Yao, Wenxin Ma +3

Medical image segmentation remains a formidable challenge due to the label scarcity. Pre-training Vision Transformer (ViT) through masked image modeling (MIM) on large-scale unlabe…

eess.IV2025

Self-Supervised Diffusion MRI Denoising via Iterative and Stable Refinement

Chenxu Wu, Qingpeng Kong, Zihang Jiang +1

Magnetic Resonance Imaging (MRI), including diffusion MRI (dMRI), serves as a ``microscope'' for anatomical structures and routinely mitigates the influence of low signal-to-noise…