1.3k citations · 1.6k across the 4 of their papers we have counts for
4 papers
CheXpert: A Large Chest Radiograph Dataset with Uncertainty Labels and Expert Comparison
Jeremy Irvin, Pranav Rajpurkar, Michael Ko +17
Large, labeled datasets have driven deep learning methods to achieve expert-level performance on a variety of medical imaging tasks. We present CheXpert, a large dataset that conta…
Deep Learning with Attention to Predict Gestational Age of the Fetal Brain
Liyue Shen, Katie Shpanskaya, Edward Lee +6
Fetal brain imaging is a cornerstone of prenatal screening and early diagnosis of congenital anomalies. Knowledge of fetal gestational age is the key to the accurate assessment of…
CheXNet: Radiologist-Level Pneumonia Detection on Chest X-Rays with Deep Learning
Pranav Rajpurkar, Jeremy Irvin, Kaylie Zhu +9
We develop an algorithm that can detect pneumonia from chest X-rays at a level exceeding practicing radiologists. Our algorithm, CheXNet, is a 121-layer convolutional neural networ…
MURA: Large Dataset for Abnormality Detection in Musculoskeletal Radiographs
Pranav Rajpurkar, Jeremy Irvin, Aarti Bagul +11
We introduce MURA, a large dataset of musculoskeletal radiographs containing 40,561 images from 14,863 studies, where each study is manually labeled by radiologists as either norma…