scholarly journals CC-NET: Image Complexity Guided Network Compression for Biomedical Image Segmentation

Author(s):  
Suraj Mishra ◽  
Peixian Liang ◽  
Adam Czajka ◽  
Danny Z. Chen ◽  
X. Sharon Hu
2022 ◽  
Vol 18 (2) ◽  
pp. 1-23
Author(s):  
Suraj Mishra ◽  
Danny Z. Chen ◽  
X. Sharon Hu

Compression is a standard procedure for making convolutional neural networks (CNNs) adhere to some specific computing resource constraints. However, searching for a compressed architecture typically involves a series of time-consuming training/validation experiments to determine a good compromise between network size and performance accuracy. To address this, we propose an image complexity-guided network compression technique for biomedical image segmentation. Given any resource constraints, our framework utilizes data complexity and network architecture to quickly estimate a compressed model which does not require network training. Specifically, we map the dataset complexity to the target network accuracy degradation caused by compression. Such mapping enables us to predict the final accuracy for different network sizes, based on the computed dataset complexity. Thus, one may choose a solution that meets both the network size and segmentation accuracy requirements. Finally, the mapping is used to determine the convolutional layer-wise multiplicative factor for generating a compressed network. We conduct experiments using 5 datasets, employing 3 commonly-used CNN architectures for biomedical image segmentation as representative networks. Our proposed framework is shown to be effective for generating compressed segmentation networks, retaining up to ≈95% of the full-sized network segmentation accuracy, and at the same time, utilizing ≈32x fewer network trainable weights (average reduction) of the full-sized networks.


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.


Author(s):  
Bekhzod Olimov ◽  
Karshiev Sanjar ◽  
Sadia Din ◽  
Awaise Ahmad ◽  
Anand Paul ◽  
...  

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

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

Author(s):  
Lei Qu ◽  
Meng Wang ◽  
Kaixuan Guo ◽  
Wan Wan ◽  
Yu Liu ◽  
...  

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