scholarly journals A Novel Framework for Improving Pulse-Coupled Neural Networks With Fuzzy Connectedness for Medical Image Segmentation

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 138129-138140
Author(s):  
Peirui Bai ◽  
Kai Yang ◽  
Xiaolin Min ◽  
Ziyang Guo ◽  
Chang Li ◽  
...  
Keyword(s):  
Neural Networks ◽  
Image Segmentation ◽  
Medical Image ◽  
Medical Image Segmentation ◽  
Fuzzy Connectedness ◽  
Coupled Neural Networks ◽  
Pulse Coupled Neural Networks

Three-dimensional Medical Image Segmentation with SE-VNet Neural Networks

2021 ◽  
Author(s):  
Sheng Lu ◽  
Jungang Han ◽  
Jiantao Li ◽  
Liyang Zhu ◽  
Jiewei Jiang ◽  
...  
Keyword(s):  
Neural Networks ◽  
Image Segmentation ◽  
Medical Image ◽  
Three Dimensional ◽  
Medical Image Segmentation

scholarly journals Test-time adaptable neural networks for robust medical image segmentation

2021 ◽  
Vol 68 ◽  
pp. 101907
Author(s):  
Neerav Karani ◽  
Ertunc Erdil ◽  
Krishna Chaitanya ◽  
Ender Konukoglu
Keyword(s):  
Neural Networks ◽  
Image Segmentation ◽  
Medical Image ◽  
Medical Image Segmentation ◽  
Test Time

scholarly journals STAMP: Simultaneous Training and Model Pruning for Low Data Regimes in Medical Image Segmentation

2021 ◽  
Author(s):  
Nicola K Dinsdale ◽  
Mark Jenkinson ◽  
Ana IL Namburete
Keyword(s):  
Neural Networks ◽  
Image Segmentation ◽  
Medical Image ◽  
Medical Image Segmentation ◽  
High Quality ◽  
Vast Number ◽  
Large Numbers ◽  
Segmentation Algorithms ◽  
Simultaneous Training ◽  
Training And Pruning

Acquisition of high quality manual annotations is vital for the development of segmentation algorithms. However, to create them we require a substantial amount of expert time and knowledge. Large numbers of labels are required to train convolutional neural networks due to the vast number of parameters that must be learned in the optimisation process. Here, we develop the STAMP algorithm to allow the simultaneous training and pruning of a UNet architecture for medical image segmentation with targeted channelwise dropout to make the network robust to the pruning. We demonstrate the technique across segmentation tasks and imaging modalities. It is then shown that, through online pruning, we are able to train networks to have much higher performance than the equivalent standard UNet models while reducing their size by more than 85% in terms of parameters. This has the potential to allow networks to be directly trained on datasets where very low numbers of labels are available.

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