Learning what to look in chest X-rays with a recurrent visual attention model
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Summary
A stochastic attention-based model that is capable of learning what regions within a chest X-ray scan should be visually explored in order to conclude that the scan contains a specific radiological abnormality is presented.
- Type
- preprint
- Published
- 2017-01-23
- Cited by
- 48
- References
- 10
- Access
- Open access
- OpenAlex
- https://openalex.org/W2580967590
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:17240237
Keywords
Abnormality, Focus (optics), Reinforcement learning, Recurrent neural network, Radiological weapon
References
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- ImageNet classification with deep convolutional neural networks
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- Modelling Radiological Language with Bidirectional Long Short-Term Memory Networks
- Recurrent Models of Visual Attention
- Multiple Object Recognition with Visual Attention
- Attention for Fine-Grained Categorization
- Stacked Convolutional Auto-Encoders for Hierarchical Feature Extraction
- VISUAL ATTENTION
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- Multi-label chest X-ray image classification via category-wise residual attention learning
- Finding a Needle in the Haystack: Attention-Based Classification of High Resolution Microscopy Images
- Mobile Phone based ensemble classification of Deep Learned Feature for Medical Image Analysis
- DA-U-Net: Densely Connected Convolutional Networks and Decoder with Attention Gate for Retinal Vessel Segmentation
- SCAN: Structure Correcting Adversarial Network for Organ Segmentation in Chest X-Rays
- U-GAN: Generative Adversarial Networks with U-Net for Retinal Vessel Segmentation
- Attention-Based Deep Neural Networks for Detection of Cancerous and Precancerous Esophagus Tissue on Histopathological Slides
- Lesion Location Attention Guided Network for Multi-Label Thoracic Disease Classification in Chest X-Rays
- Align, Attend and Locate: Chest X-Ray Diagnosis via Contrast Induced Attention Network With Limited Supervision
- Improving Whole-Heart CT Image Segmentation by Attention Mechanism
- Nuclei Detection Using Residual Attention Feature Pyramid Networks
- Label Co-Occurrence Learning With Graph Convolutional Networks for Multi-Label Chest X-Ray Image Classification
- Attention-Aware Discrimination for MR-to-CT Image Translation Using Cycle-Consistent Generative Adversarial Networks.
- Spatio‐temporal context based recurrent visual attention model for lymph node detection
- Automated Radiological Report Generation For Chest X-Rays With Weakly-Supervised End-to-End Deep Learning
- A Study on Tuberculosis Classification in Chest X-ray Using Deep Residual Attention Networks*