CVPR 2024: Segment Anything In Medical Images On Laptop

Accepted papers (14)

Fetched from OpenReview (v2) on 2026-06-10.

1. A Light-weight Universal Medical Segmentation Network for Laptops Based on Knowledge Distillation

   SONGXIAO YANG, Yizhou Li, Ye Chen, Zhuofeng Wu, Masatoshi Okutomi · PDF

2. DAFT: Data-Aware Fine-Tuning of Foundation Models for Efficient and Effective Medical Image Segmentation

   Alexander Pfefferle, Lennart Purucker, Frank Hutter · PDF

3. Efficient Quantization-Aware Training on Segment Anything Model in Medical Images and Its Deployment

   Haisheng Lu, Yujie Fu, Fan Zhang, Le Zhang · PDF

4. Filters, Thresholds, and Geodesic Distances for Scribble-based Interactive Segmentation of Medical Images

   Zdravko Marinov, Alexander Jaus, Jens Kleesiek, Rainer Stiefelhagen · PDF

5. Gray’s Anatomy for Segment Anything Model: Optimizing Grayscale Medical Images for Fast and Lightweight Segmentation

   YoungHwan Choi, In Kyu Lee, Jonghoe Ku · PDF

6. MedficientSAM: A Robust Medical Segmentation Model with Optimized Inference Pipeline for Limited Clinical Settings

   Bao-Hiep Le, Dang-Khoa Nguyen-Vu, Trong-Hieu Nguyen-Mau, Hai-Dang Nguyen, Minh-Triet Tran · PDF

7. Modality-Specific Strategies for Medical Image Segmentation using Lightweight SAM Architectures

   Thuy Thanh Dao, Xincheng Ye, Joshua Scarsbrook, Gowrienanthan Balarupan, Fernanda Lenita Ribeiro, Steffen Bollmann · PDF

8. Rep-MedSAM: Towards Real-time and Universal Medical Image Segmentation

   Muxin Wei, Shuqing Chen, Silin Wu, Dabin Xu · PDF

9. RepMedSAM: Segment Anything in Medical Images with Lightweight CNN

   Zehan Zhang, Rui Huang, Ning Huang · PDF

10. RepViT-MedSAM: Efficient Segment Anything in the Medical Images

    Muhammad Qasim Ali, Alexander Wong, Yuhao Chen · PDF

11. Segment Anything in Medical Images with nnUNet

    Raphael Stock, Yannick Kirchhoff, Maximilian Rouven Rokuss, Ashis Ravindran, Klaus Maier-Hein · PDF

12. SwiftMedSAM: An Ultra-Lightweight Prompt-based Universal Medical Image Segmentation Model for Highly Constrained Environments

    Youngbin Kong, Kwangtai Kim, Seoi Jeong, Kyu Eun Lee, HyounJoong Kong · PDF

13. Swin-LiteMedSAM: A Lightweight Box-Based Segment Anything Model for Large-Scale Medical Image Datasets

    Ruochen Gao, Donghang Lyu, Marius Staring · PDF

14. Taking a Step Back: Revisiting Classical Approaches for Efficient Interactive Segmentation of Medical Images

    Zdravko Marinov, Alexander Jaus, Jens Kleesiek, Rainer Stiefelhagen · PDF