No More Training: SAM’s Zero-Shot Transfer Capabilities for Cost-Efficient Medical Image Segmentation

Author:

Gutiérrez Juan D.1ORCID,Rodriguez-Echeverria Roberto2ORCID,Delgado Emilio2ORCID,Rodrigo Miguel Ángel Suero3ORCID,Sánchez-Figueroa Fernando2ORCID

Affiliation:

1. Department of Electronics and Computer Science, Universidad de Santiago de Compostela, Lugo, Spain

2. Department of Computer Systems Engineering and Telematics, i3 Laboratory @ Quercus Research Group, Universidad de Extremadura, Cáceres, Spain

3. Servicio Extremeño de Salud, Hospital Universitario de Cáceres, Cáceres, Spain

Funder

Ministerio de Ciencia e Innovaci?n

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Reference27 articles.

1. Segment Anything

2. On the opportunities and risks of foundation models;Bommasani;arXiv:2108.07258,2021

3. Importance of semantic representation: Dataless classification;Chang

4. SELDNet: Sequenced encoder and lightweight decoder network for COVID-19 infection region segmentation

5. Great moments in the history of anesthesiology

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1. DETR-SAM: Automated Few-Shot Segmentation With Detection Transformer and Keypoint Matching;2024 IEEE International Conference on Omni-layer Intelligent Systems (COINS);2024-07-29

2. Utilizing grounded SAM for self-supervised frugal camouflaged human detection;Automatic Target Recognition XXXIV;2024-06-07

3. Exploring Segment Anything Model Applications for Medical Imaging;2024 4th International Conference on Innovative Research in Applied Science, Engineering and Technology (IRASET);2024-05-16

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