Segment anything model for few-shot medical image segmentation with domain tuning
Abstract Medical image segmentation constitutes a crucial step in the analysis of medical images, possessing extensive applications and research significance within the realm of medical research and practice. Convolutional neural network achieved great success in medical image segmentation. However,...
Saved in:
Main Authors: | Weili Shi, Penglong Zhang, Yuqin Li, Zhengang Jiang |
---|---|
Format: | Article |
Language: | English |
Published: |
Springer
2024-11-01
|
Series: | Complex & Intelligent Systems |
Subjects: | |
Online Access: | https://doi.org/10.1007/s40747-024-01625-7 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
ClassWise-SAM-Adapter: Parameter-Efficient Fine-Tuning Adapts Segment Anything to SAR Domain for Semantic Segmentation
by: Xinyang Pu, et al.
Published: (2025-01-01) -
Few-Shot Contrail Segmentation in Remote Sensing Imagery With Loss Function in Hough Space
by: Junzi Sun, et al.
Published: (2025-01-01) -
DSMF-Net: Dual Semantic Metric Learning Fusion Network for Few-Shot Aerial Image Semantic Segmentation
by: Xiyu Qi, et al.
Published: (2025-01-01) -
Tongue-LiteSAM: A Lightweight Model for Tongue Image Segmentation With Zero-Shot
by: Daiqing Tan, et al.
Published: (2025-01-01) -
Gaze Assistance for Efficient Segmentation Correction of Medical Images
by: Leila Khaertdinova, et al.
Published: (2025-01-01)