Attention U-Net: Learning Where to Look for the Pancreas
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Summary
A novel attention gate (AG) model for medical imaging that automatically learns to focus on target structures of varying shapes and sizes is proposed to eliminate the necessity of using explicit external tissue/organ localisation modules of cascaded convolutional neural networks (CNNs).
- Type
- preprint
- Published
- 2018-04-11
- Cited by
- 7,915
- References
- 41
- Access
- Open access
- OpenAlex
- https://openalex.org/W2798122215
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:4861068
Keywords
Computer science, Convolutional neural network, Overhead (engineering), Artificial intelligence, Code (set theory)
References
- Effective Approaches to Attention-based Neural Machine Translation
- Fully convolutional networks for semantic segmentation
- Automated Abdominal Multi-Organ Segmentation With Subject-Specific Atlas Generation
- Joint optimization of segmentation and shape prior from level-set-based statistical shape model, and its application to the automated segmentation of abdominal organs
- Efficient multi‐scale 3D CNN with fully connected CRF for accurate brain lesion segmentation
- V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation
- Learning what to look in chest X-rays with a recurrent visual attention model
- Spatial aggregation of holistically‐nested convolutional neural networks for automated pancreas localization and segmentation☆
- Holistically-Nested Edge Detection
- Hierarchical 3D fully convolutional networks for multi-organ segmentation
- Residual Attention Network for Image Classification
- Bottom-Up and Top-Down Attention for Image Captioning and VQA
- Improving Deep Pancreas Segmentation in CT and MRI Images via Recurrent Neural Contextual Learning and Direct Loss Function
- Bottom-Up and Top-Down Attention for Image Captioning and Visual Question Answering
- Squeeze-and-Excitation Networks
- Graph Attention Networks
- Human-level CMR image analysis with deep fully convolutional networks
- Evaluate the Malignancy of Pulmonary Nodules Using the 3-D Deep Leaky Noisy-OR Network
- Recurrent Saliency Transformation Network: Incorporating Multi-stage Visual Cues for Small Organ Segmentation
- Fully convolutional multi‐scale residual DenseNets for cardiac segmentation and automated cardiac diagnosis using ensemble of classifiers
Cited by
- Knowledge-Based Systems
- Abdominal multi-organ segmentation with organ-attention networks and statistical fusion
- A Novel Focal Tversky Loss Function With Improved Attention U-Net for Lesion Segmentation
- Salient Object Detection in Video using Deep Non-Local Neural Networks
- RA-UNet: A Hybrid Deep Attention-Aware Network to Extract Liver and Tumor in CT Scans
- Accurate, Data-Efficient, Unconstrained Text Recognition with Convolutional Neural Networks
- Estimação automática de espessura de gordura subcutânea bovina em imagens ultrassonográficas utilizando Deep Learning
- Deep Learning-Based Lesion Detection
- Going Deep in Medical Image Analysis: Concepts, Methods, Challenges, and Future Directions
- OBELISK‐Net: Fewer layers to solve 3D multi‐organ segmentation with sparse deformable convolutions
- Machine Learning on Biomedical Images: Interactive Learning, Transfer Learning, Class Imbalance, and Beyond
- AHCNet: An Application of Attention Mechanism and Hybrid Connection for Liver Tumor Segmentation in CT Volumes
- Probability Map Guided Bi-directional Recurrent UNet for Pancreas Segmentation
- Connection Sensitive Attention U-NET for Accurate Retinal Vessel Segmentation
- An ensemble of U-Net architecture variants for left atrial segmentation
- Integrating spatial configuration into heatmap regression based CNNs for landmark localization
- Coronary Arteries Segmentation Based on 3D FCN With Attention Gate and Level Set Function
- nnU-Net: Breaking the Spell on Successful Medical Image Segmentation
- Segmentation of Thoracic Organs Using Pixel Shuffle
- Self-Attention Capsule Networks for Image Classification
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