85 citations · 565 across the 57 of their papers we have counts for
15 papers · 1 filter
ReXGroundingCT: A 3D Chest CT Dataset for Segmentation of Findings from Free-Text Reports
Mohammed Baharoon, Luyang Luo, Michael Moritz +22
We introduce ReXGroundingCT, the first publicly available dataset linking free-text findings to pixel-level 3D segmentations in chest CT scans. The dataset includes 3,142 non-contr…
ReXGradient-160K: A Large-Scale Publicly Available Dataset of Chest Radiographs with Free-text Reports
Xiaoman Zhang, Julián N. Acosta, Josh Miller +2
We present ReXGradient-160K, representing the largest publicly available chest X-ray dataset to date in terms of the number of patients. This dataset contains 160,000 chest X-ray s…
a2z-1 for Multi-Disease Detection in Abdomen-Pelvis CT: External Validation and Performance Analysis Across 21 Conditions
Pranav Rajpurkar, Julian N. Acosta, Siddhant Dogra +4
We present a comprehensive evaluation of a2z-1, an artificial intelligence (AI) model designed to analyze abdomen-pelvis CT scans for 21 time-sensitive and actionable findings. Our…
Video Pretraining Advances 3D Deep Learning on Chest CT Tasks
Alexander Ke, Shih-Cheng Huang, Chloe P O'Connell +3
Pretraining on large natural image classification datasets such as ImageNet has aided model development on data-scarce 2D medical tasks. 3D medical tasks often have much less data…
Improving dermatology classifiers across populations using images generated by large diffusion models
Luke W. Sagers, James A. Diao, Matthew Groh +3
Dermatological classification algorithms developed without sufficiently diverse training data may generalize poorly across populations. While intentional data collection and annota…
Deep Learning-Based Sparse Whole-Slide Image Analysis for the Diagnosis of Gastric Intestinal Metaplasia
Jon Braatz, Pranav Rajpurkar, Stephanie Zhang +2
In recent years, deep learning has successfully been applied to automate a wide variety of tasks in diagnostic histopathology. However, fast and reliable localization of small-scal…
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…
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…
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,…
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…
CheXphotogenic: Generalization of Deep Learning Models for Chest X-ray Interpretation to Photos of Chest X-rays
Pranav Rajpurkar, Anirudh Joshi, Anuj Pareek +3
The use of smartphones to take photographs of chest x-rays represents an appealing solution for scaled deployment of deep learning models for chest x-ray interpretation. However, t…
GloFlow: Global Image Alignment for Creation of Whole Slide Images for Pathology from Video
Viswesh Krishna, Anirudh Joshi, Philip L. Bulterys +3
The application of deep learning to pathology assumes the existence of digital whole slide images of pathology slides. However, slide digitization is bottlenecked by the high cost…
CheXphoto: 10,000+ Photos and Transformations of Chest X-rays for Benchmarking Deep Learning Robustness
Nick A. Phillips, Pranav Rajpurkar, Mark Sabini +10
Clinical deployment of deep learning algorithms for chest x-ray interpretation requires a solution that can integrate into the vast spectrum of clinical workflows across the world.…
CheXpedition: Investigating Generalization Challenges for Translation of Chest X-Ray Algorithms to the Clinical Setting
Pranav Rajpurkar, Anirudh Joshi, Anuj Pareek +5
Although there have been several recent advances in the application of deep learning algorithms to chest x-ray interpretation, we identify three major challenges for the translatio…