From the 1 of 51 linked papers with an AI index.
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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…
Improving Palliative Care with Deep Learning
Anand Avati, Kenneth Jung, Stephanie Harman +3
Improving the quality of end-of-life care for hospitalized patients is a priority for healthcare organizations. Studies have shown that physicians tend to over-estimate prognoses,…
Cardiologist-Level Arrhythmia Detection with Convolutional Neural Networks
Pranav Rajpurkar, Awni Y. Hannun, Masoumeh Haghpanahi +2
We develop an algorithm which exceeds the performance of board certified cardiologists in detecting a wide range of heart arrhythmias from electrocardiograms recorded with a single…
Data Noising as Smoothing in Neural Network Language Models
Ziang Xie, Sida I. Wang, Jiwei Li +4
Data noising is an effective technique for regularizing neural network models. While noising is widely adopted in application domains such as vision and speech, commonly used noisi…