Med-DANet V2: A Flexible Dynamic Architecture for Efficient Medical Volumetric Segmentation-Reference-Cited by-同舟云学术

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Med-DANet V2: A Flexible Dynamic Architecture for Efficient Medical Volumetric Segmentation

Published:2024-01-03 Issue: Volume:29 Page:7856-7866
ISSN:
Container-title:2024 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)
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Author:

Shen Haoran¹,Zhang Yifu¹,Wang Wenxuan¹,Chen Chen²,Liu Jing³,Song Shanshan¹,Li Jiangyun¹

Affiliation:

1. University of Science and Technology,School of Automation and Electrical Engineering,Beijing

2. University of Central Florida,Center for Research in Computer Vision

3. Institute of Automation, Chinese Academy of Sciences,National Lab of Pattern Recognition

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10483279/10483282/10483875.pdf?arnumber=10483875

Reference51 articles.

1. U-net fixed-point quantization for medical image segmentation;AskariHemmat

2. Advancing The Cancer Genome Atlas glioma MRI collections with expert segmentation labels and radiomic features

3. Identifying the best machine learning algorithms for brain tumor segmentation, progression assessment, and overall survival prediction in the brats challenge;Bakas,2018

4. Estimating or propagating gradients through stochastic neurons for conditional computation;Bengio,2013

5. Swin-Unet: Unet-Like Pure Transformer for Medical Image Segmentation

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