activity
20232026
most citedSA-Med2D-20M Dataset: Segment Anything in 2D Medical Imaging with 20 Million masks

14 citations · 45 across the 7 of their papers we have counts for

collaborators

7 papers

cs.CV2026

RAFM: Retrieval-Augmented Flow Matching for Unpaired CBCT-to-CT Translation

Xianhao Zhou, Jianghao Wu, Lanfeng Zhong +4

Cone-beam CT (CBCT) is routinely acquired in radiotherapy but suffers from severe artifacts and unreliable Hounsfield Unit (HU) values, limiting its direct use for dose calculation…

cs.CV2026

CT-Flow: Orchestrating CT Interpretation Workflow with Model Context Protocol Servers

Yannian Gu, Xizhuo Zhang, Linjie Mu +4

Recent advances in Large Vision-Language Models (LVLMs) have shown strong potential for multi-modal radiological reasoning, particularly in tasks like diagnostic visual question an…

eess.IV2024★ 8 cited

GMAI-MMBench: A Comprehensive Multimodal Evaluation Benchmark Towards General Medical AI

Pengcheng Chen, Jin Ye, Guoan Wang +15

Large Vision-Language Models (LVLMs) are capable of handling diverse data types such as imaging, text, and physiological signals, and can be applied in various fields. In the medic…

cs.CL2024★ 2 cited

OlympicArena: Benchmarking Multi-discipline Cognitive Reasoning for Superintelligent AI

Zhen Huang, Zengzhi Wang, Shijie Xia +25

The evolution of Artificial Intelligence (AI) has been significantly accelerated by advancements in Large Language Models (LLMs) and Large Multimodal Models (LMMs), gradually showc…

eess.IV2023★ 10 cited

SegRap2023: A Benchmark of Organs-at-Risk and Gross Tumor Volume Segmentation for Radiotherapy Planning of Nasopharyngeal Carcinoma

Xiangde Luo, Jia Fu, Yunxin Zhong +39

Radiation therapy is a primary and effective NasoPharyngeal Carcinoma (NPC) treatment strategy. The precise delineation of Gross Tumor Volumes (GTVs) and Organs-At-Risk (OARs) is c…

eess.IV2023★ 14 cited

SA-Med2D-20M Dataset: Segment Anything in 2D Medical Imaging with 20 Million masks

Jin Ye, Junlong Cheng, Jianpin Chen +12

Segment Anything Model (SAM) has achieved impressive results for natural image segmentation with input prompts such as points and bounding boxes. Its success largely owes to massiv…

cs.CV2023★ 11 cited

SAM-Med3D: Towards General-purpose Segmentation Models for Volumetric Medical Images

Haoyu Wang, Sizheng Guo, Jin Ye +11

Existing volumetric medical image segmentation models are typically task-specific, excelling at specific target but struggling to generalize across anatomical structures or modalit…