Multi-label chest X-ray image classification via category-wise residual attention learning
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Summary
A category-wise residual attention learning (CRAL) framework that predicts the presence of multiple pathologies in a class-specific attentive view and yields the average AUC score of 0.816 which is a new state of the art.
- Type
- article
- Published
- 2020-02-01
- Cited by
- 220
- References
- 48
- OpenAlex
- https://openalex.org/W2897806204
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:125515806
Keywords
Embedding, Feature (linguistics), Artificial intelligence, Convolutional neural network, Computer science
References
- Segmenting Retinal Blood Vessels With Deep Neural Networks
- Show, Attend and Tell: Neural Image Caption Generation with Visual Attention
- Very Deep Convolutional Networks for Large-Scale Image Recognition
- Object-based visual attention for computer vision
- Multi-Class Active Learning by Uncertainty Sampling with Diversity Maximization
- A Multimedia Retrieval Framework Based on Semi-Supervised Ranking and Relevance Feedback
- Feature Selection for Multimedia Analysis by Sharing Information Among Multiple Tasks
- Long Short-Term Memory
- Application of principal axes for registration of NMR imagey sequences
- Object extraction from T2 weighted brain MR image using histogram based gradient calculation
- Going deeper with convolutions
- ImageNet classification with deep convolutional neural networks
- Deep Residual Learning for Image Recognition
- Semisupervised Feature Analysis by Mining Correlations Among Multiple Tasks
- Learning Deep Features for Discriminative Localization
- Lung Pattern Classification for Interstitial Lung Diseases Using a Deep Convolutional Neural Network
- Bi-Level Semantic Representation Analysis for Multimedia Event Detection
- Image Classification by Cross-Media Active Learning With Privileged Information
- Semantic Pooling for Complex Event Analysis in Untrimmed Videos
- Learning what to look in chest X-rays with a recurrent visual attention model
Cited by
- A Weighted Voting Ensemble Self-Labeled Algorithm for the Detection of Lung Abnormalities from X-Rays
- Deep adversarial one-class learning for normal and abnormal chest radiograph classification
- ImageGCN: Multi-Relational Image Graph Convolutional Networks for Disease Identification with Chest X-rays
- DualCheXNet: dual asymmetric feature learning for thoracic disease classification in chest X-rays
- Spatial non-local attention for thoracic disease diagnosis and visualisation in weakly supervised learning
- Deep Multi Label Classification in Affine Subspaces
- Attract or Distract: Exploit the Margin of Open Set
- Compound Fault Diagnosis of Gearboxes via Multi-label Convolutional Neural Network and Wavelet Transform
- Semi-Supervised learning with Collaborative Bagged Multi-label K-Nearest-Neighbors
- Lesion Location Attention Guided Network for Multi-Label Thoracic Disease Classification in Chest X-Rays
- Thorax disease classification with attention guided convolutional neural network
- Label Co-Occurrence Learning With Graph Convolutional Networks for Multi-Label Chest X-Ray Image Classification
- KGZNet:Knowledge-Guided Deep Zoom Neural Networks for Thoracic Disease Classification
- LU-Net: a multi-task network to improve the robustness of segmentation of left ventriclular structures by deep learning in 2D echocardiography
- Models Genesis
- Transfer-Learning-Aware Neuro-Evolution for Diseases Detection in Chest X-Ray Images
- Two-stream collaborative network for multi-label chest X-ray Image classification with lung segmentation
- Oil Pipeline Weld Defect Identification System Based on Convolutional Neural Network
- Active, continual fine tuning of convolutional neural networks for reducing annotation efforts
- Region Proposals for Saliency Map Refinement for Weakly-supervised Disease Localisation and Classification
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