Cross-Modal Learning via Adversarial Loss and Covariate Shift for Enhanced Liver Segmentation-Reference-Cited by-同舟云学术

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Cross-Modal Learning via Adversarial Loss and Covariate Shift for Enhanced Liver Segmentation

Published:2024-08 Issue:4 Volume:8 Page:2723-2735
ISSN:2471-285X
Container-title:IEEE Transactions on Emerging Topics in Computational Intelligence
language:
Short-container-title:IEEE Trans. Emerg. Top. Comput. Intell.

Author:

Ozkan Savas¹^ORCID,Selver M. Alper²^ORCID,Baydar Bora¹^ORCID,Kavur Ali Emre³^ORCID,Candemir Cemre⁴^ORCID,Akar Gozde Bozdagi¹^ORCID

Affiliation:

1. Electrical and Electronics Engineering Department, Middle East Technical University, Ankara, Türkiye

2. Department of Electrical & Electronics Engineering, Dokuz Eylul University, Izmir, Türkiye

3. Institute of Natural and Applied Sciences, Dokuz Eylul University, Izmir, Türkiye

4. International Computer Institute, Ege University, Izmir, Türkiye

Funder

Türkiye Bilimsel ve Teknolojik Araştırma Kurumu

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/7433297/10607834/10463530.pdf?arnumber=10463530

Reference72 articles.

1. Discrete Iterative Partial Segmentation Technique (DIPS) for Tomographic Reconstruction

2. SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation

3. U-Net: Convolutional Networks for Biomedical Image Segmentation

4. Efficient multi-scale 3D CNN with fully connected CRF for accurate brain lesion segmentation

5. Multilevel Features Convolutional Neural Network for Multifocus Image Fusion

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. Automatic liver segmentation from CT volumes based on multi-view information fusion and condition random fields;Optics & Laser Technology;2024-12

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