An algorithm developed by researchers at Stanford University proved more effective than human radiologists in diagnosing cases of pneumonia. Much research has been shared on the potential of Artificial Intelligence applied to medicine, and in some cases, can reach a level of accuracy that exceeds the performance of professionals.
For the month of January, we addressed the performance of deep learning algorithms for disease diagnosis, specifically focusing on the paper by the stanford group -- CheXNet: Radiologist-Level Pneumonia Detection on Chest X-Rays with Deep Learning. We continue to generate a large interest in the journal club, with 347 people registered, 150 of whom signed on January 24th 2018 to participate in the discussion. The paper has had 3 revisions and is available here https://arxiv.org/abs/1711.05225 . Like many deep learning papers that claim super human performance, the paper was widely circulated in the news media, several blog posts, on reddit and twitter. Please note that the findings of superhuman performance are increasingly being reported in medical AI papers.
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.
In the case of CheXnet, the research team led by Stanford adjunct professor Andrew Ng, started by training the neural network with 112,120 chest X-ray images that were previously manually labeled with up to 14 different diseases. One of them was pneumonia. After training it for a month, the software beat previous computer-based methods to detect this type of infection. The Stanford Machine Learning Group team pitted its software against four Stanford radiologists, giving each of them 420 X-ray images. This graphic shows how the radiologists–represented by the orange Xs–did compared to the program–represented by the blue curve.