scholarly journals DNN Feature Map Compression Using Learned Representation over GF(2)

2019 ◽  
pp. 502-516
Author(s):  
Denis Gudovskiy ◽  
Alec Hodgkinson ◽  
Luca Rigazio
Keyword(s):  
Feature Map

scholarly journals A Self-Spatial Adaptive Weighting Based U-Net for Image Segmentation

Electronics ◽  
2021 ◽  
Vol 10 (3) ◽  
pp. 348
Author(s):  
Choongsang Cho ◽  
Young Han Lee ◽  
Jongyoul Park ◽  
Sangkeun Lee
Keyword(s):  
Image Segmentation ◽  
Medical Image ◽  
Medical Image Segmentation ◽  
Feature Maps ◽  
Data Set ◽  
Feature Map ◽  
Adaptive Weighting ◽  
Spatially Adaptive ◽  
Wide Range ◽  
Decoder Architecture

Semantic image segmentation has a wide range of applications. When it comes to medical image segmentation, its accuracy is even more important than those of other areas because the performance gives useful information directly applicable to disease diagnosis, surgical planning, and history monitoring. The state-of-the-art models in medical image segmentation are variants of encoder-decoder architecture, which is called U-Net. To effectively reflect the spatial features in feature maps in encoder-decoder architecture, we propose a spatially adaptive weighting scheme for medical image segmentation. Specifically, the spatial feature is estimated from the feature maps, and the learned weighting parameters are obtained from the computed map, since segmentation results are predicted from the feature map through a convolutional layer. Especially in the proposed networks, the convolutional block for extracting the feature map is replaced with the widely used convolutional frameworks: VGG, ResNet, and Bottleneck Resent structures. In addition, a bilinear up-sampling method replaces the up-convolutional layer to increase the resolution of the feature map. For the performance evaluation of the proposed architecture, we used three data sets covering different medical imaging modalities. Experimental results show that the network with the proposed self-spatial adaptive weighting block based on the ResNet framework gave the highest IoU and DICE scores in the three tasks compared to other methods. In particular, the segmentation network combining the proposed self-spatially adaptive block and ResNet framework recorded the highest 3.01% and 2.89% improvements in IoU and DICE scores, respectively, in the Nerve data set. Therefore, we believe that the proposed scheme can be a useful tool for image segmentation tasks based on the encoder-decoder architecture.

scholarly journals Diagnosis Support Model of Cardiomegaly based on CNN using ResNet and Explainable Feature Map

IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Hyun Yoo ◽  
Soyoung Han ◽  
Kyungyong Chung
Keyword(s):  
Feature Map ◽  
Support Model

scholarly journals Method for constructing multi-dimensional feature map of malicious code

2021 ◽  
Vol 1748 ◽  
pp. 042055
Author(s):  
Haocong Ma ◽  
Ji Zhang ◽  
Junhua Zhou ◽  
Xiang Zhai ◽  
Junjie Xue ◽  
...  
Keyword(s):  
Malicious Code ◽  
Feature Map

scholarly journals Transform-Based Feature Map Compression for CNN Inference

2021 ◽  
Author(s):  
Yubo Shi ◽  
Meiqi Wang ◽  
Siyi Chen ◽  
Jinghe Wei ◽  
Zhongfeng Wang
Keyword(s):  
Feature Map
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