Multiplanar Data Augmentation and Lightweight Skip Connection Design for Deep-Learning-Based Abdominal CT Image Segmentation-Reference-Cited by-同舟云学术

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Multiplanar Data Augmentation and Lightweight Skip Connection Design for Deep-Learning-Based Abdominal CT Image Segmentation

Published:2023 Issue: Volume:72 Page:1-11
ISSN:0018-9456
Container-title:IEEE Transactions on Instrumentation and Measurement
language:
Short-container-title:IEEE Trans. Instrum. Meas.

Author:

Zhang Wenyuan¹^ORCID,Zhang Yu¹^ORCID,Zhang Liming¹^ORCID

Affiliation:

1. Faculty of Science and Technology, University of Macau, Macau, China

Funder

Science and Technology Development Fund of Macau SAR

Multi-Year Research

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Instrumentation

Link

http://xplorestaging.ieee.org/ielx7/19/10012124/10301567.pdf?arnumber=10301567

Reference43 articles.

1. Deep learning of the sectional appearances of 3D CT images for anatomical structure segmentation based on an FCN voting method

2. TransUNet＋: Redesigning the skip connection to enhance features in medical image segmentation

3. Data Augmentation for Brain-Tumor Segmentation: A Review

4. TransClaw U-Net: Claw U-Net with transformers for medical image segmentation;chang;arXiv 2107 05188,2021

5. The Importance of Skip Connections in Biomedical Image Segmentation

Cited by 1 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. Enhancing Image Annotation With Object Tracking and Image Retrieval: A Systematic Review;IEEE Access;2024

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