Domain Adaptation Meets Zero-Shot Learning: An Annotation-Efficient Approach to Multi-Modality Medical Image Segmentation-Reference-Cited by-同舟云学术

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Domain Adaptation Meets Zero-Shot Learning: An Annotation-Efficient Approach to Multi-Modality Medical Image Segmentation

Published:2021 Issue: Volume: Page:1-1
ISSN:0278-0062
Container-title:IEEE Transactions on Medical Imaging
language:
Short-container-title:IEEE Trans. Med. Imaging

Author:

Bian Cheng,Yuan Chenglang,Ma Kai,Yu Shuang,Wei Dong,Zheng Yefeng

Funder

National Key R and D Program of China

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Computer Science Applications,Radiological and Ultrasound Technology,Software

Link

http://xplorestaging.ieee.org/ielx7/42/4359023/09627926.pdf?arnumber=9627926

Cited by 22 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Unsupervised domain adaptation multi-level adversarial learning-based crossing-domain retinal vessel segmentation;Computers in Biology and Medicine;2024-08

2. Learning De-biased prototypes for Few-shot Medical Image Segmentation;Pattern Recognition Letters;2024-07

3. A comprehensive review on zero-shot-learning techniques;Intelligent Decision Technologies;2024-06-07

4. Abdominal multi-organ segmentation in Multi-sequence MRIs based on visual attention guided network and knowledge distillation;Physica Medica;2024-06

5. A comprehensive review on zero-shot-learning techniques;Intelligent Decision Technologies;2024-04-17

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