Boosted Cascaded Convnets for Multilabel Classification of Thoracic Diseases in Chest Radiographs
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Summary
This work experiments a set of deep learning models and presents a cascaded deep neural network that can diagnose all 14 pathologies better than the baseline and is competitive with other published methods.
- Type
- preprint
- Published
- 2017-11-23
- Cited by
- 124
- References
- 14
- Access
- Open access
- OpenAlex
- https://openalex.org/W2770384498
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:25142689
Keywords
Convolutional neural network, Deep learning, Computer science, Artificial intelligence, Machine learning
References
- Bayes Optimal Multilabel Classification via Probabilistic Classifier Chains
- Deep Convolutional Ranking for Multilabel Image Annotation
- A Brief Introduction to Boosting
- Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification
- Classifier chains for multi-label classification
- Multi-stage Contextual Deep Learning for Pedestrian Detection
- Improving Pairwise Ranking for Multi-label Image Classification
- ChestX-Ray8: Hospital-Scale Chest X-Ray Database and Benchmarks on Weakly-Supervised Classification and Localization of Common Thorax Diseases
- Learning Deep Latent Spaces for Multi-Label Classification
- Learning to diagnose from scratch by exploiting dependencies among labels
- CheXNet: Radiologist-Level Pneumonia Detection on Chest X-Rays with Deep Learning
- Densely Connected Convolutional Networks
- Large-Scale Multi-label Text Classification - Revisiting Neural Networks
Cited by
- Diagnose like a Radiologist: Attention Guided Convolutional Neural Network for Thorax Disease Classification
- Weakly Supervised Medical Diagnosis and Localization from Multiple Resolutions
- Large Scale Automated Reading of Frontal and Lateral Chest X-Rays using Dual Convolutional Neural Networks
- Automated chest screening based on a hybrid model of transfer learning and convolutional sparse denoising autoencoder
- ChestNet: A Deep Neural Network for Classification of Thoracic Diseases on Chest Radiography
- Computer-aided detection in chest radiography based on artificial intelligence: a survey
- Deep Generative Classifiers for Thoracic Disease Diagnosis with Chest X-ray Images
- Multi-label chest X-ray image classification via category-wise residual attention learning
- CheXpert: A Large Chest Radiograph Dataset with Uncertainty Labels and Expert Comparison
- ImageGCN: Multi-Relational Image Graph Convolutional Networks for Disease Identification with Chest X-rays
- Enhanced Diagnosis of Pneumothorax with an Improved Real-Time Augmentation for Imbalanced Chest X-rays Data Based on DCNN
- Deep Integration: A Multi-Label Architecture for Road Scene Recognition
- A Novel Approach for Multi-Label Chest X-Ray Classification of Common Thorax Diseases
- Combining LSTM and DenseNet for Automatic Annotation and Classification of Chest X-Ray Images
- Thorax-Net: An Attention Regularized Deep Neural Network for Classification of Thoracic Diseases on Chest Radiography
- Classification and Segmentation of Hyperspectral Data of Hepatocellular Carcinoma Samples Using 1‐D Convolutional Neural Network
- Multiple Feature Integration for Classification of Thoracic Disease in Chest Radiography
- Pneumonia Detection Using CNN based Feature Extraction
- Interpreting chest X-rays via CNNs that exploit disease dependencies and uncertainty labels
- Chest disease radiography in twofold: using convolutional neural networks and transfer learning
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