Thoracic Disease Classification
2 papers with code • 1 benchmarks • 1 datasets
Benchmarks
These leaderboards are used to track progress in Thoracic Disease Classification
| Trend | Dataset | Best Model | Paper | Code | Compare |
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ChestX-ray14
Latest papers with no code
ThoraX-PriorNet: A Novel Attention-Based Architecture Using Anatomical Prior Probability Maps for Thoracic Disease Classification
Next, we develop a novel attention-based classification model that combines information from the estimated anatomical prior and automatically extracted chest region of interest (ROI) masks to provide attention to the feature maps generated from a deep convolution network.
Learning Underrepresented Classes from Decentralized Partially Labeled Medical Images
In the second stage, the decentralized partially labeled data are exploited to learn an energy-based multi-label classifier for the common classes.
Breaking with Fixed Set Pathology Recognition through Report-Guided Contrastive Training
We show that despite using unstructured medical report supervision, we perform on par with direct label supervision through a sophisticated inference setting.
Anatomy-XNet: An Anatomy Aware Convolutional Neural Network for Thoracic Disease Classification in Chest X-rays
We adopt a semi-supervised learning method by utilizing available small-scale organ-level annotations to locate the anatomy regions in large-scale datasets where the organ-level annotations are absent.
Weighing Features of Lung and Heart Regions for Thoracic Disease Classification
By zeroing features of non-lung and heart regions in attention maps, we can effectively exploit their disease-specific cues in lung and heart regions.
Rethinking Annotation Granularity for Overcoming Shortcuts in Deep Learning-based Radiograph Diagnosis: A Multicenter Study
The models were also compared to radiologists on a subset of the internal testing set (n=496).
Multi-label Thoracic Disease Image Classification with Cross-Attention Networks
Automated disease classification of radiology images has been emerging as a promising technique to support clinical diagnosis and treatment planning.
Deep Mining External Imperfect Data for Chest X-ray Disease Screening
Recent researches have demonstrated that performance bottleneck exists in joint training on different CXR datasets, and few made efforts to address the obstacle.
SDFN: Segmentation-based Deep Fusion Network for Thoracic Disease Classification in Chest X-ray Images
Two CNN-based classification models were then used as feature extractors to obtain the discriminative features of the entire CXR images and the cropped lung region images.
Dynamic Routing on Deep Neural Network for Thoracic Disease Classification and Sensitive Area Localization
We present and evaluate a new deep neural network architecture for automatic thoracic disease detection on chest X-rays.
