AHCNet: An Application of Attention Mechanism and Hybrid Connection for Liver Tumor Segmentation in CT Volumes
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Summary
An Attention Hybrid Connection Network architecture which combines soft and hard attention mechanism and long and short skip connections is designed which can achieve faster network convergence and accurate semantic segmentation and further demonstrate that the proposed method has a good clinical value.
- Type
- article
- Published
- 2019-02-25
- Cited by
- 134
- References
- 56
- Access
- Open access
- OpenAlex
- https://openalex.org/W2916686134
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:71148869
Keywords
Connection (principal bundle), Computer science, Mechanism (biology), Segmentation, Artificial intelligence
References
- A Likelihood and Local Constraint Level Set Model for Liver Tumor Segmentation from CT Volumes
- Deep Epitomic Convolutional Neural Networks
- Show, Attend and Tell: Neural Image Caption Generation with Visual Attention
- Automated method for detection and segmentation of liver metastatic lesions in follow-up CT examinations
- The neural mechanisms of top-down attentional control
- Metrics for evaluating 3D medical image segmentation: analysis, selection, and tool
- A New Level-Set-Based Protocol for Accurate Bone Segmentation From CT Imaging
- Estimates of worldwide burden of cancer in 2008: GLOBOCAN 2008
- 3D DCT supervised segmentation applied on liver volumes
- Liver segmentation from computed tomography scans: A survey and a new algorithm
- Semi-automatic liver tumor segmentation with hidden Markov measure field model and non-parametric distribution estimation
- Microscopic observation of elastic distortions caused by orowan loops
- Automatic Liver Segmentation Based on Shape Constraints and Deformable Graph Cut in CT Images
- A Survey on Transfer Learning
- Deep Residual Learning for Image Recognition
- Automatic 3D liver location and segmentation via convolutional neural network and graph cut
- Diversified Visual Attention Networks for Fine-Grained Object Classification
- Multi-loss Regularized Deep Neural Network
- Pyramid Scene Parsing Network
- MR‐based synthetic CT generation using a deep convolutional neural network method
Cited by
- Liver Tumor Segmentation Based on Multi-Scale Candidate Generation and Fractal Residual Network
- Automatic segmentation of kidney and liver tumors in CT images
- A New Three-stage Curriculum Learning Approach for Deep Network Based Liver Tumor Segmentation
- Channel-Unet: A Spatial Channel-Wise Convolutional Neural Network for Liver and Tumors Segmentation
- Automatic segmentation of 3D prostate MR images with iterative localization refinement
- Hepatocellular Carcinoma Segmentation within Ultrasound Images using Convolutional Neural Networks
- Deep Learning Initialized and Gradient Enhanced Level-Set Based Segmentation for Liver Tumor From CT Images
- Hybrid Cascaded Neural Network for Liver Lesion Segmentation
- Performance Analysis of Different 2D and 3D CNN Model for Liver Semantic Segmentation: A Review
- Tumor detection for whole slide image of liver based on patch-based convolutional neural network
- A Stacked Generalization U-shape network based on zoom strategy and its application in biomedical image segmentation
- Deep Learning for Automated Liver Segmentation to Aid in the Study of infectious Diseases in Nonhuman Primates
- Dial/Hybrid Cascade 3DResUNet for Liver and Tumor Segmentation
- Pyramid graph cut: Integrating intensity and gradient information for grayscale medical image segmentation
- Deep learning for liver tumour classification: enhanced loss function
- Liver Tumor Segmentation and Classification: A Systematic Review
- 3D Bounding Box Detection in Volumetric Medical Image Data: A Systematic Literature Review
- A Multiple Layer U-Net, Un-Net, for Liver and Liver Tumor Segmentation in CT
- TMD-Unet: Triple-Unet with Multi-Scale Input Features and Dense Skip Connection for Medical Image Segmentation
- Multi-Slice Low-Rank Tensor Decomposition Based Multi-Atlas Segmentation: Application to Automatic Pathological Liver CT Segmentation
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