most citedRevSAM2: Prompt SAM2 for Medical Image Segmentation via Reverse-Propagation without Fine-tuning

1 citations · 1 across the 5 of their papers we have counts for

collaborators

5 papers

eess.IV2025

Anatomy-Aware Low-Dose CT Denoising via Pretrained Vision Models and Semantic-Guided Contrastive Learning

Runze Wang, Zeli Chen, Zhiyun Song +8

To reduce radiation exposure and improve the diagnostic efficacy of low-dose computed tomography (LDCT), numerous deep learning-based denoising methods have been developed to mitig…

eess.IV2025

Boosting Vision Semantic Density with Anatomy Normality Modeling for Medical Vision-language Pre-training

Weiwei Cao, Jianpeng Zhang, Zhongyi Shui +8

Vision-language pre-training (VLP) has great potential for developing multifunctional and general medical diagnostic capabilities. However, aligning medical images with a low signa…

cs.CV2025

UniReg: Conditional Unified Model for Medical Image Registration

Zi Li, Jianpeng Zhang, Tai Ma +7

Learning-based medical image registration has matched the accuracy of conventional methods while offering superior computational efficiency. However, existing approaches suffer fro…

eess.IV2024

Leveraging Semantic Asymmetry for Precise Gross Tumor Volume Segmentation of Nasopharyngeal Carcinoma in Planning CT

Zi Li, Ying Chen, Zeli Chen +12

In the radiation therapy of nasopharyngeal carcinoma (NPC), clinicians typically delineate the gross tumor volume (GTV) using non-contrast planning computed tomography to ensure ac…

cs.CV2024★ 1 cited

RevSAM2: Prompt SAM2 for Medical Image Segmentation via Reverse-Propagation without Fine-tuning

Yunhao Bai, Boxiang Yun, Zeli Chen +3

The Segment Anything Model 2 (SAM2) has recently demonstrated exceptional performance in zero-shot prompt segmentation for natural images and videos. However, when the propagation…