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Genetic U-Net: Automatically Designing Lightweight U-shaped CNN Architectures Using the Genetic Algorithm for Retinal Vessel Segmentation
Many previous works based on deep learning for retinal vessel segmentati...
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Biomedical Image Segmentation by Retina-like Sequential Attention Mechanism Using Only A Few Training Images
In this paper we propose a novel deep learning-based algorithm for biome...
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Beyond CNNs: Exploiting Further Inherent Symmetries in Medical Images for Segmentation
Automatic tumor segmentation is a crucial step in medical image analysis...
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Down-Scaling with Learned Kernels in Multi-Scale Deep Neural Networks for Non-Uniform Single Image Deblurring
Multi-scale approach has been used for blind image / video deblurring pr...
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Joint segmentation and classification of retinal arteries/veins from fundus images
Objective Automatic artery/vein (A/V) segmentation from fundus images is...
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Automatic segmenting teeth in X-ray images: Trends, a novel data set, benchmarking and future perspectives
This review presents an in-depth study of the literature on segmentation...
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CNN-Based Deep Architecture for Reinforced Concrete Delamination Segmentation Through Thermography
Delamination assessment of the bridge deck plays a vital role for bridge...
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Deep Vessel Segmentation By Learning Graphical Connectivity
We propose a novel deep-learning-based system for vessel segmentation. Existing methods using CNNs have mostly relied on local appearances learned on the regular image grid, without considering the graphical structure of vessel shape. To address this, we incorporate a graph convolutional network into a unified CNN architecture, where the final segmentation is inferred by combining the different types of features. The proposed method can be applied to expand any type of CNN-based vessel segmentation method to enhance the performance. Experiments show that the proposed method outperforms the current state-of-the-art methods on two retinal image datasets as well as a coronary artery X-ray angiography dataset.
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