Dual-Guided Prototype Alignment Network for Few-Shot Medical Image Segmentation-Reference-Cited by-同舟云学术

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Dual-Guided Prototype Alignment Network for Few-Shot Medical Image Segmentation

Published:2024 Issue: Volume:73 Page:1-13
ISSN:0018-9456
Container-title:IEEE Transactions on Instrumentation and Measurement
language:
Short-container-title:IEEE Trans. Instrum. Meas.

Author:

Shen Yue¹^ORCID,Fan Wanshu¹^ORCID,Wang Cong²^ORCID,Liu Wenfei³^ORCID,Wang Wei⁴^ORCID,Zhang Qiang¹^ORCID,Zhou Dongsheng¹^ORCID

Affiliation:

1. National and Local Joint Engineering Laboratory of Computer Aided Design, School of Software Engineering, Dalian University, Dalian, China

2. Department of Computing, The Hong Kong Polytechnic University, Hong Kong, China

3. Department of Radiology, Affiliated Zhongshan Hospital of Dalian University, Dalian, China

4. School of Cyber Science and Technology, Sun Yat-Sen University, Shenzhen, China

Funder

National Key Research and Development Program of China

National Natural Science Foundation of China

Program for Innovative Research Team in University of Liaoning Province

Support Plan for Key Field Innovation Team of Dalian

Support Plan for Leading Innovation Team of Dalian University

Higher Education Discipline Innovation Project

Program for the Liaoning Province Doctoral Research Starting Fund

Interdisciplinary Project of Dalian University

Dalian Major Projects of Basic Research

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx8/19/10367905/10551867.pdf?arnumber=10551867

Reference54 articles.

1. Deep Learning Techniques for Medical Image Segmentation: Achievements and Challenges

2. Generalizing Deep Learning for Medical Image Segmentation to Unseen Domains via Deep Stacked Transformation

3. Sleep Apnea Detection From Single-Lead ECG: A Comprehensive Analysis of Machine Learning and Deep Learning Algorithms

4. A Review of Deep-Learning-Based Medical Image Segmentation Methods

5. DS-TransUNet: Dual Swin Transformer U-Net for Medical Image Segmentation

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