85 citations · 565 across the 57 of their papers we have counts for
13 papers · 1 filter
Q-Pain: A Question Answering Dataset to Measure Social Bias in Pain Management
Cécile Logé, Emily Ross, David Yaw Amoah Dadey +4
Recent advances in Natural Language Processing (NLP), and specifically automated Question Answering (QA) systems, have demonstrated both impressive linguistic fluency and a pernici…
RadGraph: Extracting Clinical Entities and Relations from Radiology Reports
Saahil Jain, Ashwin Agrawal, Adriel Saporta +9
Extracting structured clinical information from free-text radiology reports can enable the use of radiology report information for a variety of critical healthcare applications. In…
3KG: Contrastive Learning of 12-Lead Electrocardiograms using Physiologically-Inspired Augmentations
Bryan Gopal, Ryan W. Han, Gautham Raghupathi +3
We propose 3KG, a physiologically-inspired contrastive learning approach that generates views using 3D augmentations of the 12-lead electrocardiogram. We evaluate representation qu…
Structured dataset documentation: a datasheet for CheXpert
Christian Garbin, Pranav Rajpurkar, Jeremy Irvin +2
Billions of X-ray images are taken worldwide each year. Machine learning, and deep learning in particular, has shown potential to help radiologists triage and diagnose images. Howe…
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,…
CheXtransfer: Performance and Parameter Efficiency of ImageNet Models for Chest X-Ray Interpretation
Alexander Ke, William Ellsworth, Oishi Banerjee +2
Deep learning methods for chest X-ray interpretation typically rely on pretrained models developed for ImageNet. This paradigm assumes that better ImageNet architectures perform be…
MedAug: Contrastive learning leveraging patient metadata improves representations for chest X-ray interpretation
Yen Nhi Truong Vu, Richard Wang, Niranjan Balachandar +3
Self-supervised contrastive learning between pairs of multiple views of the same image has been shown to successfully leverage unlabeled data to produce meaningful visual represent…
CheXseg: Combining Expert Annotations with DNN-generated Saliency Maps for X-ray Segmentation
Soham Gadgil, Mark Endo, Emily Wen +2
Medical image segmentation models are typically supervised by expert annotations at the pixel-level, which can be expensive to acquire. In this work, we propose a method that combi…