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Found 6 papers, 2 papers with code
Higher Chest X-ray Resolution Improves Classification Performance
Deep learning models for image classification are often trained at a resolution of 224 x 224 pixels for historical and efficiency reasons.
WindowNet: Learnable Windows for Chest X-ray Classification
Finally, we propose and evaluate WindowNet, a model that learns optimal window settings, and show that it significantly improves performance compared to the baseline model without windowing.
Automated Labeling of German Chest X-Ray Radiology Reports using Deep Learning
In this work, we explore the potential of weak supervision of a deep learning-based label prediction model, using a rule-based labeler.
German CheXpert Chest X-ray Radiology Report Labeler
This study aimed to develop an algorithm to automatically extract annotations for chest X-ray classification models from German thoracic radiology reports.
Attention-based Saliency Maps Improve Interpretability of Pneumothorax Classification
Conclusion: ViTs performed similarly to CNNs in CXR classification, and their attention-based saliency maps were more useful to radiologists and outperformed GradCAM.
A knee cannot have lung disease: out-of-distribution detection with in-distribution voting using the medical example of chest X-ray classification
The proposed IDV approach trained on ID (chest X-ray 14) and OOD data (IRMA and ImageNet) achieved, on average, 0. 999 OOD AUC across the three data sets, surpassing all other OOD detection methods.
