UNETR++: Delving Into Efficient and Accurate 3D Medical Image Segmentation-Reference-Cited by-同舟云学术

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UNETR++: Delving Into Efficient and Accurate 3D Medical Image Segmentation

Published:2024-09 Issue:9 Volume:43 Page:3377-3390
ISSN:0278-0062
Container-title:IEEE Transactions on Medical Imaging
language:
Short-container-title:IEEE Trans. Med. Imaging

Author:

Shaker Abdelrahman¹^ORCID,Maaz Muhammad¹,Rasheed Hanoona¹,Khan Salman¹^ORCID,Yang Ming-Hsuan²^ORCID,Shahbaz Khan Fahad³^ORCID

Affiliation:

1. Computer Vision Department, Mohamed bin Zayed University of Artificial Intelligence, Abu Dhabi, United Arab Emirates

2. Electrical Engineering and Computer Science Department, University of California at Merced, Merced, CA, USA

3. Mohamed bin Zayed University, Abu Dhabi, United Arab Emirates

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/42/10663877/10526382.pdf?arnumber=10526382

Reference47 articles.

1. UNETR: Transformers for 3D Medical Image Segmentation

2. nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation

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

4. MedNeXt: Transformer-Driven Scaling of ConvNets for Medical Image Segmentation

5. nnFormer: Volumetric Medical Image Segmentation via a 3D Transformer

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