C
Changlu Guo
Researcher at Budapest University of Technology and Economics
Publications - 11
Citations - 311
Changlu Guo is an academic researcher from Budapest University of Technology and Economics. The author has contributed to research in topics: Feature (computer vision) & Segmentation. The author has an hindex of 4, co-authored 10 publications receiving 60 citations. Previous affiliations of Changlu Guo include Eötvös Loránd University & Jiangxi Normal University.
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SA-UNet: Spatial Attention U-Net for Retinal Vessel Segmentation
TL;DR: A lightweight network named Spatial Attention U-Net (SA-UNet) that does not require thousands of annotated training samples and can be utilized in a data augmentation manner to use the available annotated samples more efficiently.
Proceedings ArticleDOI
SA-UNet: Spatial Attention U-Net for Retinal Vessel Segmentation
TL;DR: SA-UNet as mentioned in this paper introduces a spatial attention module which infers the attention map along the spatial dimension, and multiplies the attention maps by the input feature map for adaptive feature refinement.
Proceedings ArticleDOI
SD-Unet: A Structured Dropout U-Net for Retinal Vessel Segmentation
TL;DR: This paper utilizes the U-shaped structure to exploit the local features of the retinal vessels and perform retinal vessel segmentation in an end-to-end manner and proposes a new method called Structured Dropout U-Net (SD-Unet), which abandons the traditional dropout for convolutional layers, and applies the structured dropout to regularize U- net.
Proceedings ArticleDOI
Channel Attention Residual U-Net for Retinal Vessel Segmentation
TL;DR: Wang et al. as mentioned in this paper introduced a modified efficient channel attention (MECA) to enhance the discriminative ability of the network by considering the interdependence between feature maps, which achieved state-of-the-art performance on three publicly available retinal vessel datasets: DRIVE, CHASE DB1 and STARE.
Book ChapterDOI
Residual Spatial Attention Network for Retinal Vessel Segmentation
TL;DR: The Residual Spatial Attention Network (RSAN) for retinal vessel segmentation employs a modified residual block structure that integrates DropBlock, which can not only be utilized to construct deep networks to extract more complex vascular features, but can also effectively alleviate the overfitting.