CT Slice Segmentation for Patients with COVID-19 based on Transformer and Convolution Neural Network
Author:
Affiliation:
1. School of Electronic and Information Engineering, Soochow University,Suzhou,China
2. School of Mathematical Sciences, Soochow University,Suzhou,China
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/9836326/9836330/09836820.pdf?arnumber=9836820
Reference6 articles.
1. Unet++: A nested u-net architecture for medical image segmentation;zhou;Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support,2018
2. U-net: Convolutional networks for biomedical image segmentation;ronneberger;The 18th International Conference on Medical Image Computing and Computer Assisted Interventions (MICCAI),2015
3. Rethinking semantic segmentation from a sequence-to-sequenceperspective with transformers;zheng;ArXiv Preprint,2020
4. 3D U-Net: learning dense volumetric segmentation from “sparse annotation;çiçek;Conference on Medical Image Computing and Computer-Assisted Intervention,2016
5. Managing high clinical suspicion COVID-19 inpatients with negative RT-PCR: a pragmatic and limited role for thoracic CT
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