FU-Net: fast biomedical image segmentation model based on bottleneck convolution layers

Author(s):  
Bekhzod Olimov ◽  
Karshiev Sanjar ◽  
Sadia Din ◽  
Awaise Ahmad ◽  
Anand Paul ◽  
...  
2021 ◽  
Vol 554 ◽  
pp. 33-46
Author(s):  
Mingwen Shao ◽  
Gaozhi Zhang ◽  
Wangmeng Zuo ◽  
Deyu Meng

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Changyong Li ◽  
Yongxian Fan ◽  
Xiaodong Cai

Abstract Background With the development of deep learning (DL), more and more methods based on deep learning are proposed and achieve state-of-the-art performance in biomedical image segmentation. However, these methods are usually complex and require the support of powerful computing resources. According to the actual situation, it is impractical that we use huge computing resources in clinical situations. Thus, it is significant to develop accurate DL based biomedical image segmentation methods which depend on resources-constraint computing. Results A lightweight and multiscale network called PyConvU-Net is proposed to potentially work with low-resources computing. Through strictly controlled experiments, PyConvU-Net predictions have a good performance on three biomedical image segmentation tasks with the fewest parameters. Conclusions Our experimental results preliminarily demonstrate the potential of proposed PyConvU-Net in biomedical image segmentation with resources-constraint computing.


2021 ◽  
Vol 2 (4) ◽  
Author(s):  
Yahya Alzahrani ◽  
Boubakeur Boufama

2021 ◽  
Vol 68 ◽  
pp. 101889
Author(s):  
Rodney LaLonde ◽  
Ziyue Xu ◽  
Ismail Irmakci ◽  
Sanjay Jain ◽  
Ulas Bagci

2013 ◽  
Vol 40 (12) ◽  
pp. 4934-4943 ◽  
Author(s):  
Tatiana von Landesberger ◽  
Sebastian Bremm ◽  
Matthias Kirschner ◽  
Stefan Wesarg ◽  
Arjan Kuijper

2012 ◽  
Vol 2 (2) ◽  
pp. 200-205
Author(s):  
Raghotham Reddy Ganta ◽  
Syed Zaheeruddin ◽  
Narsimha Baddiri ◽  
R. Rameshwar Rao

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