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

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

Semantic segmentation of medical images presents an enormous potential for diagnosis and surgery. However, achieving precise results involves designing and training complex Deep Learning (DL) models specifically for this task, which is only available to some. SAM is a model developed by Meta capable...

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Bibliographic Details
Main Authors: Juan D. Gutierrez, Roberto Rodriguez-Echeverria, Emilio Delgado, Miguel Angel Suero Rodrigo, Fernando Sanchez-Figueroa
Format: Article
Language:English
Published: IEEE 2024-01-01
Series:IEEE Access
Subjects:
Image segmentation
deep learning
zero-shot learning
medical imaging
semantic segmentation
Online Access:https://ieeexplore.ieee.org/document/10388320/

Internet

https://ieeexplore.ieee.org/document/10388320/

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