default search action
Google
Google Scholar
Semantic Scholar
Internet Archive Scholar
CiteSeerX
ORCIDMedSAM on Laptop@CVPR 2024: Seattle, WA, USA
- Jun Ma
, Yuyin Zhou, Bo Wang:
Medical Image Segmentation Foundation Models. CVPR 2024 Challenge: Segment Anything in Medical Images on Laptop - MedSAM on Laptop 2024, Held in Conjunction with CVPR 2024, Seattle, WA, USA, June 17-21, 2024, Proceedings. Lecture Notes in Computer Science 15458, Springer 2025, ISBN 978-3-031-81853-0 - Bao-Hiep Le, Dang-Khoa Nguyen-Vu, Trong-Hieu Nguyen Mau, Hai-Dang Nguyen, Minh-Triet Tran:
MedficientSAM: A Robust Medical Segmentation Model with Optimized Inference Pipeline for Limited Clinical Settings. 1-14 - Alexander Pfefferle, Lennart Purucker, Frank Hutter:
DAFT: Data-Aware Fine-Tuning of Foundation Models for Efficient and Effective Medical Image Segmentation. 15-38 - Zdravko Marinov, Alexander Jaus, Jens Kleesiek, Rainer Stiefelhagen:
Filters, Thresholds, and Geodesic Distances for Scribble-Based Interactive Segmentation of Medical Images. 39-56 - Muxin Wei, Shuqing Chen, Silin Wu, Dabin Xu:
Rep-MedSAM: Towards Real-Time and Universal Medical Image Segmentation. 57-69 - Ruochen Gao, Donghang Lyu, Marius Staring:
Swin-LiteMedSAM: A Lightweight Box-Based Segment Anything Model for Large-Scale Medical Image Datasets. 70-82 - Songxiao Yang, Yizhou Li, Ye Chen, Zhuofeng Wu, Masatoshi Okutomi:
A Light-Weight Universal Medical Segmentation Network for Laptops Based on Knowledge Distillation. 83-100 - Zdravko Marinov, Alexander Jaus, Jens Kleesiek, Rainer Stiefelhagen:
Taking a Step Back: Revisiting Classical Approaches for Efficient Interactive Segmentation of Medical Images. 101-125 - Li Zhi, Yaqi Wang, Shuai Wang:
ExpertsMedSAM: Faster Medical Image Segment Anything with Mixture-of-Experts. 126-136 - Haisheng Lu, Yujie Fu, Fan Zhang, Le Zhang:
Efficient Quantization-Aware Training on Segment Anything Model in Medical Images and Its Deployment. 137-150 - Haotian Guan, Bingze Dai, Jiajing Zhang:
Lite Class-Prompt Tiny-VIT for Multi-modality Medical Image Segmentation. 151-166 - Raphael Stock, Yannick Kirchhoff, Maximilian Rokuss, Ashis Ravindran, Klaus H. Maier-Hein:
Segment Anything in Medical Images with nnUNet. 167-179 - Youngbin Kong, Kwangtai Kim, Seoi Jeong, Kyu Eun Lee, Hyoun-Joong Kong:
SwiftMedSAM: An Ultra-lightweight Prompt-Based Universal Medical Image Segmentation Model for Highly Constrained Environments. 180-194 - Qasim Ali, Yuhao Chen, Alexander Wong:
RepViT-MedSAM: Efficient Segment Anything in the Medical Images. 195-205 - Xin Wang, Xiaoyu Liu, Peng Huang, Pu Huang, Shu Hu, Hongtu Zhu:
U-MedSAM: Uncertainty-Aware MedSAM for Medical Image Segmentation. 206-217 - Thuy Thanh Dao, Xincheng Ye, Joshua D. Scarsbrook, Balarupan Gowrienanthan, Fernanda L. Ribeiro, Steffen Bollmann:
Modality-Specific Strategies for Medical Image Segmentation Using Lightweight SAM Architectures. 218-231 - In Kyu Lee, Jonghoe Ku, Younghwan Choi:
Gray's Anatomy for Segment Anything Model: Optimizing Grayscale Medical Images for Fast and Lightweight Segmentation. 232-245
manage site settings
To protect your privacy, all features that rely on external API calls from your browser are turned off by default. You need to opt-in for them to become active. All settings here will be stored as cookies with your web browser. For more information see our F.A.Q.
last updated on 2025-03-11 20:14 CET by the dblp team
all metadata released as open data under CC0 1.0 license
see also: Terms of Use | Privacy Policy | Imprint
dblp was originally created in 1993 at:
since 2018, dblp has been operated and maintained by:
the dblp computer science bibliography is funded and supported by:
