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MedSAM 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

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