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Found 11 papers, 8 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.
Adapted Large Language Models Can Outperform Medical Experts in Clinical Text Summarization
Analyzing vast textual data and summarizing key information from electronic health records imposes a substantial burden on how clinicians allocate their time.
RadAdapt: Radiology Report Summarization via Lightweight Domain Adaptation of Large Language Models
We systematically investigate lightweight strategies to adapt large language models (LLMs) for the task of radiology report summarization (RRS).
The Effect of Counterfactuals on Reading Chest X-rays
This study evaluates the effect of counterfactual explanations on the interpretation of chest X-rays.
Benchmarking saliency methods for chest X-ray interpretation
Saliency methods, which produce heat maps that highlight the areas of the medical image that influence model prediction, are often presented to clinicians as an aid in diagnostic decision-making.
Gifsplanation via Latent Shift: A Simple Autoencoder Approach to Counterfactual Generation for Chest X-rays
We also found that the Latent Shift explanation allows a user to have more confidence in true positive predictions compared to traditional approaches (0. 15$\pm$0. 95 in a 5 point scale with p=0. 01) with only a small increase in false positive predictions (0. 04$\pm$1. 06 with p=0. 57).
CheXternal: Generalization of Deep Learning Models for Chest X-ray Interpretation to Photos of Chest X-rays and External Clinical Settings
Recent advances in training deep learning models have demonstrated the potential to provide accurate chest X-ray interpretation and increase access to radiology expertise.
CheXphotogenic: Generalization of Deep Learning Models for Chest X-ray Interpretation to Photos of Chest X-rays
In this study, we measured the diagnostic performance for 8 different chest x-ray models when applied to photos of chest x-rays.
CheXphoto: 10,000+ Photos and Transformations of Chest X-rays for Benchmarking Deep Learning Robustness
We introduce CheXphoto, a dataset of smartphone photos and synthetic photographic transformations of chest x-rays sampled from the CheXpert dataset.
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.
CheXpedition: Investigating Generalization Challenges for Translation of Chest X-Ray Algorithms to the Clinical Setting
First, we find that the top 10 chest x-ray models on the CheXpert competition achieve an average AUC of 0. 851 on the task of detecting TB on two public TB datasets without fine-tuning or including the TB labels in training data.
