Zero-Shot Performance of the Segment Anything Model (SAM) in 2D Medical Imaging: A Comprehensive Evaluation and Practical Guidelines

Author:

Mattjie Christian1,De Moura Luis Vinicius1,Ravazio Rafaela1,Kupssinskü Lucas1,Parraga Otávio1,Delucis Marcelo Mussi1,Barros Rodrigo C.1

Affiliation:

1. Pontifícia Universidade Católica do Rio Grande do Sul,Machine Learning Theory and Applications Lab (MALTA),Porto Alegre,Brazil

Funder

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - Brasil

Publisher

IEEE

Cited by 5 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. CropFinder: AI-based Detection and Tracking of Crops for Precision Agriculture;2024 IEEE 33rd International Symposium on Industrial Electronics (ISIE);2024-06-18

2. Evaluation of the Segment Anything Model (SAM) for Brain Tumor Segmentation;2024 IEEE International Symposium on Biomedical Imaging (ISBI);2024-05-27

3. Boosting Medical Image Classification with Segmentation Foundation Model;2024 IEEE International Symposium on Biomedical Imaging (ISBI);2024-05-27

4. Zero-Shot Segmentation of Eye Features Using the Segment Anything Model (SAM);Proceedings of the ACM on Computer Graphics and Interactive Techniques;2024-05-17

5. AFTer-SAM: Adapting SAM with Axial Fusion Transformer for Medical Imaging Segmentation;2024 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV);2024-01-03

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