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Found 6 papers, 4 papers with code
Combining Automatic Labelers and Expert Annotations for Accurate Radiology Report Labeling Using BERT
The extraction of labels from radiology text reports enables large-scale training of medical imaging models.
Effect of Radiology Report Labeler Quality on Deep Learning Models for Chest X-Ray Interpretation
Next, after training image classification models using labels generated from the different radiology report labelers on one of the largest datasets of chest X-rays, we show that an image classification model trained on labels from the VisualCheXbert labeler outperforms image classification models trained on labels from the CheXpert and CheXbert labelers.
MedSelect: Selective Labeling for Medical Image Classification Combining Meta-Learning with Deep Reinforcement Learning
We propose a selective learning method using meta-learning and deep reinforcement learning for medical image interpretation in the setting of limited labeling resources.
VisualCheXbert: Addressing the Discrepancy Between Radiology Report Labels and Image Labels
We also find that VisualCheXbert better agrees with radiologists labeling chest X-ray images than do radiologists labeling the corresponding radiology reports by an average F1 score across several medical conditions of between 0. 12 (95% CI 0. 09, 0. 15) and 0. 21 (95% CI 0. 18, 0. 24).
DLBCL-Morph: Morphological features computed using deep learning for an annotated digital DLBCL image set
We used a deep learning model to segment all tumor nuclei in the ROIs, and computed several geometric features for each segmented nucleus.
CheXbert: Combining Automatic Labelers and Expert Annotations for Accurate Radiology Report Labeling Using BERT
The extraction of labels from radiology text reports enables large-scale training of medical imaging models.
