HRED-Net: High-Resolution Encoder-Decoder Network for Fine-Grained Image Segmentation
Accurate segmentation of fine-grained information is an important step in medical image analysis applications. With the development of the encoder-decoder-based networks, various network structures and algorithms have made significant progress in semantic segmentation tasks. This work aims to presen...
Main Authors: | Chengzhi Lyu, Guoqing Hu, Dan Wang |
---|---|
Format: | Article |
Language: | English |
Published: |
IEEE
2020-01-01
|
Series: | IEEE Access |
Subjects: | |
Online Access: | https://ieeexplore.ieee.org/document/9007452/ |
Similar Items
-
Residual Connection-Based Encoder Decoder Network (RCED-Net) for Retinal Vessel Segmentation
by: Tariq Mahmood Khan, et al.
Published: (2020-01-01) -
Unsupervised multiscale retinal blood vessel segmentation using fundus images
by: Kamini Upadhyay, et al.
Published: (2020-09-01) -
ICENETv2: A Fine-Grained River Ice Semantic Segmentation Network Based on UAV Images
by: Xiuwei Zhang, et al.
Published: (2021-02-01) -
Retinal Blood Vessels Segmentation using Local Ternary Pattern
by: Bibi Misbah Kazmi, et al.
Published: (2021-03-01) -
Attention Guided U-Net With Atrous Convolution for Accurate Retinal Vessels Segmentation
by: Yan Lv, et al.
Published: (2020-01-01)