Segment Anything Model (SAM) for Medical Image Segmentation: A Preliminary Review
Author:
Affiliation:
1. Shenzhen Technology University,College of Big Data and Internet,Shenzhen,China
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/10385250/10385251/10386032.pdf?arnumber=10386032
Reference100 articles.
1. Mixed-Net: A Mixed Architecture for Medical Image Segmentation
2. Deep learning-based automatic segmentation of images in cardiac radiography: A promising challenge
3. Segment Anything;Kirillov,2023
4. OneFormer: One Transformer to Rule Universal Image Segmentation
5. SegGPT: Towards Segmenting Everything In Context
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1. Harnessing the Potential of Advanced Large Vision Models to Enhance the Detection of Optoelectronic Imaging Signals;2024 22nd International Conference on Optical Communications and Networks (ICOCN);2024-07-26
2. G-SAM: GMM-based segment anything model for medical image classification and segmentation;Cluster Computing;2024-07-17
3. Text Knowledge-Guided Segment Anything Model for Medical Image Segmentation;2024 International Technical Conference on Circuits/Systems, Computers, and Communications (ITC-CSCC);2024-07-02
4. Assessing Test-Time Variability for Interactive 3D Medical Image Segmentation with Diverse Point Prompts;2024 IEEE International Symposium on Biomedical Imaging (ISBI);2024-05-27
5. Evaluation of the Segment Anything Model (SAM) for Brain Tumor Segmentation;2024 IEEE International Symposium on Biomedical Imaging (ISBI);2024-05-27
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