ULFAC-Net: Ultra-Lightweight Fully Asymmetric Convolutional Network for Skin Lesion Segmentation
Author:
Affiliation:
1. School of Automation, Hangzhou Dianzi University, Hangzhou, China
2. School of Computer Science, Hangzhou Dianzi University, Hangzhou, China
Funder
National Natural Science Foundation of China
Key Research and Development Project of Zhejiang Province
Key Laboratory of Brain Machine Collaborative Intelligence of Zhejiang Province
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Subject
Health Information Management,Electrical and Electronic Engineering,Computer Science Applications,Health Informatics
Link
http://xplorestaging.ieee.org/ielx7/6221020/10144459/10077446.pdf?arnumber=10077446
Reference46 articles.
1. ACNet: Strengthening the Kernel Skeletons for Powerful CNN via Asymmetric Convolution Blocks
2. Cascaded context enhancement network for automatic skin lesion segmentation
3. LRNNET: A Light-Weighted Network with Efficient Reduced Non-Local Operation for Real-Time Semantic Segmentation
4. An Improved and Robust Encoder–Decoder for Skin Lesion Segmentation
5. The Lovasz-Softmax Loss: A Tractable Surrogate for the Optimization of the Intersection-Over-Union Measure in Neural Networks
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