most citedSAM on Medical Images: A Comprehensive Study on Three Prompt Modes

56 citations · 75 across the 5 of their papers we have counts for

collaborators

5 papers

cs.CV20248 cited

OpenMEDLab: An Open-source Platform for Multi-modality Foundation Models in Medicine

Xiaosong Wang, Xiaofan Zhang, Guotai Wang +17

The emerging trend of advancing generalist artificial intelligence, such as GPTv4 and Gemini, has reshaped the landscape of research (academia and industry) in machine learning and…

cs.CV2023

Deblurring Masked Autoencoder is Better Recipe for Ultrasound Image Recognition

Qingbo Kang, Jun Gao, Kang Li +1

Masked autoencoder (MAE) has attracted unprecedented attention and achieves remarkable performance in many vision tasks. It reconstructs random masked image patches (known as proxy…

cs.CV2023

ConES: Concept Embedding Search for Parameter Efficient Tuning Large Vision Language Models

Huahui Yi, Ziyuan Qin, Wei Xu +5

Large pre-trained vision-language models have shown great prominence in transferring pre-acquired knowledge to various domains and downstream tasks with appropriate prompting or tu…

cs.CV202356 cited

SAM on Medical Images: A Comprehensive Study on Three Prompt Modes

Dongjie Cheng, Ziyuan Qin, Zekun Jiang +3

The Segment Anything Model (SAM) made an eye-catching debut recently and inspired many researchers to explore its potential and limitation in terms of zero-shot generalization capa…

cs.CV202311 cited

Towards General Purpose Medical AI: Continual Learning Medical Foundation Model

Huahui Yi, Ziyuan Qin, Qicheng Lao +5

Inevitable domain and task discrepancies in real-world scenarios can impair the generalization performance of the pre-trained deep models for medical data. Therefore, we audaciousl…