most citedMedSAM2: Segment Anything in 3D Medical Images and Videos

7 citations · 9 across the 6 of their papers we have counts for

collaborators

6 papers

cs.CV2025

Which Layer Causes Distribution Deviation? Entropy-Guided Adaptive Pruning for Diffusion and Flow Models

Changlin Li, Jiawei Zhang, Zeyi Shi +3

Large-scale vision generative models, including diffusion and flow models, have demonstrated remarkable performance in visual generation tasks. However, transferring these pre-trai…

cs.LG2025

A Weakly Supervised Transformer for Rare Disease Diagnosis and Subphenotyping from EHRs with Pulmonary Case Studies

Kimberly F. Greco, Zongxin Yang, Mengyan Li +6

Rare diseases affect an estimated 300-400 million people worldwide, yet individual conditions remain underdiagnosed and poorly characterized due to their low prevalence and limited…

cs.CV2025

SurgBench: A Unified Large-Scale Benchmark for Surgical Video Analysis

Jianhui Wei, Zikai Xiao, Danyu Sun +4

Surgical video understanding is pivotal for enabling automated intraoperative decision-making, skill assessment, and postoperative quality improvement. However, progress in develop…

eess.IV2025★ 7 cited

MedSAM2: Segment Anything in 3D Medical Images and Videos

Jun Ma, Zongxin Yang, Sumin Kim +6

Medical image and video segmentation is a critical task for precision medicine, which has witnessed considerable progress in developing task or modality-specific and generalist mod…

cs.CV2024★ 1 cited

Efficient Training of Large Vision Models via Advanced Automated Progressive Learning

Changlin Li, Jiawei Zhang, Sihao Lin +4

The rapid advancements in Large Vision Models (LVMs), such as Vision Transformers (ViTs) and diffusion models, have led to an increasing demand for computational resources, resulti…

cs.CV2024★ 1 cited

Controllable 3D Face Generation with Conditional Style Code Diffusion

Xiaolong Shen, Jianxin Ma, Chang Zhou +1

Generating photorealistic 3D faces from given conditions is a challenging task. Existing methods often rely on time-consuming one-by-one optimization approaches, which are not effi…