Goto

Collaborating Authors



CheXNet: Radiologist-Level Pneumonia Detection on Chest X-Rays with Deep Learning

arXiv.org Machine Learning

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 network trained on ChestX-ray14, currently the largest publicly available chest X-ray dataset, containing over 100,000 frontal-view X-ray images with 14 diseases. Four practicing academic radiologists annotate a test set, on which we compare the performance of CheXNet to that of radiologists. We find that CheXNet exceeds average radiologist performance on the F1 metric. We extend CheXNet to detect all 14 diseases in ChestX-ray14 and achieve state of the art results on all 14 diseases.


Stanford Algorithm Can Diagnose Pneumonia Better Than Radiologists

IEEE Spectrum Robotics

Stanford researchers have developed a machine-learning algorithm that can diagnose pneumonia from a chest x-ray better than a human radiologist can. And it learned how to do so in just about a month.


Stanford AI application reads chest X-rays multiple times faster than radiologists

#artificialintelligence

A new artificial intelligence algorithm can reliably screen chest X-rays for more than a dozen types of disease, and it does so in less time than it takes to read this sentence, according to a new study led by Stanford University researchers. The algorithm, dubbed CheXNeXt, is the first to simultaneously evaluate X-rays for a multitude of possible maladies and return results that are consistent with the readings of radiologists, the study says. Scientists trained the algorithm to detect 14 different pathologies: For 10 diseases, the algorithm performed just as well as radiologists; for three, it underperformed compared with radiologists; and for one, the algorithm outdid the experts. "Usually, we see AI algorithms that can detect a brain hemorrhage or a wrist fracture -- a very narrow scope for single-use cases," says associate professor of radiology Matthew Lungren. "But here we're talking about 14 different pathologies analyzed simultaneously, and it's all through one algorithm."