From the 1 of 51 linked papers with an AI index.
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Effect of Radiology Report Labeler Quality on Deep Learning Models for Chest X-Ray Interpretation
Saahil Jain, Akshay Smit, Andrew Y. Ng +1
Although deep learning models for chest X-ray interpretation are commonly trained on labels generated by automatic radiology report labelers, the impact of improvements in report l…
MedSelect: Selective Labeling for Medical Image Classification Combining Meta-Learning with Deep Reinforcement Learning
Akshay Smit, Damir Vrabac, Yujie He +3
We propose a selective learning method using meta-learning and deep reinforcement learning for medical image interpretation in the setting of limited labeling resources. Our method…
VisualCheXbert: Addressing the Discrepancy Between Radiology Report Labels and Image Labels
Saahil Jain, Akshay Smit, Steven QH Truong +7
Automatic extraction of medical conditions from free-text radiology reports is critical for supervising computer vision models to interpret medical images. In this work, we show th…
CheXbreak: Misclassification Identification for Deep Learning Models Interpreting Chest X-rays
Emma Chen, Andy Kim, Rayan Krishnan +3
A major obstacle to the integration of deep learning models for chest x-ray interpretation into clinical settings is the lack of understanding of their failure modes. In this work,…
CheXseen: Unseen Disease Detection for Deep Learning Interpretation of Chest X-rays
Siyu Shi, Ishaan Malhi, Kevin Tran +2
We systematically evaluate the performance of deep learning models in the presence of diseases not labeled for or present during training. First, we evaluate whether deep learning…
CheXternal: Generalization of Deep Learning Models for Chest X-ray Interpretation to Photos of Chest X-rays and External Clinical Settings
Pranav Rajpurkar, Anirudh Joshi, Anuj Pareek +2
Recent advances in training deep learning models have demonstrated the potential to provide accurate chest X-ray interpretation and increase access to radiology expertise. However,…