Zero-Shot Performance of the Segment Anything Model (SAM) in 2D Medical Imaging: A Comprehensive Evaluation and Practical Guidelines
Author:
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
Link
http://xplorestaging.ieee.org/ielx7/10431814/10431802/10431886.pdf?arnumber=10431886
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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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