Automatic Chest Radiograph X-ray (CXR) interpretation by machines is an important research topic of Artificial Intelligence. As part of my journey through the California Science Fair, I have developed an algorithm that can detect pneumonia from a CXR image to compensate for human fallibility. My algorithm, PneumoXttention, is an ensemble of two 13 layer convolutional neural network trained on the RSNA dataset, a dataset provided by the Radiological Society of North America, containing 26,684 frontal X-ray images split into the categories of pneumonia and no pneumonia. The dataset was annotated by many professional radiologists in North America. It achieved an impressive F1 score, 0.82, on the test set (20% random split of RSNA dataset) and completely compensated Human Radiologists on a random set of 25 test images drawn from RSNA and NIH. I don't have a direct comparison but Stanford's Chexnet has a F1 score of 0.435 on the NIH dataset for category Pneumonia.

Research Using CheXNet at Stanford: CheXNet is a deep learning Convolutional Neural Network (CNN) model developed at Stanford University to identify thoracic pathologies from the NIH ChestXray14 dataset. CheXNet is a 121-layer CNN that uses chest X-Ray images to predict the output probabilities of a pathology. It correctly detects pneumonia by localizing the areas in the image that are most indicative of the pathology. Stanford researchers have been able to train the ChestX-Ray14 dataset using a pre-trained model of CheXNet-121 with the ImageNet2012-1K dataset. The NIH dataset consists of over one hundred thousand frontal chest X-ray images from over 30,000 unique patients that have been annotated with up to 14 thoracic diseases including pneumonia and emphysema.

Artificial intelligence is better than us at analyzing data. We're all familiar with IBM Watson, the AI which won a round of Jeopardy and is now free for anyone to use to crunch data. But you may not know that the 160 man hours it would take a human to analyze genomic data from both tumor cells and healthy cells takes Watson 10 minutes to process. Just as robotics and automation has found a natural home in manufacturing, doctors and researchers are finding artificial intelligence has hundreds of applications in their industry. From telling the difference between cancerous and non-cancerous cells to diagnosing rare genetic conditions using facial recognition algorithms and refining everything from hearing aids to artificial hands, artificial intelligence healthcare is finding its footing. There are a lot of barriers for full integration of the technology.

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. For example, this article denotes that "Medical AI May Be Better at Spotting Eye Disease Than Real Doctors" To help critique the ChexNet paper, we constituted a panel composed of the author team (most of the authors listed on the paper were kind enough to be in attendance -- thank you!), Dr. Luke(blog) and Dr. Paras (blog) who had critiqued the data used and Jeremy Howard (past president and chief scientist of Kaggle, a data analytics competition site, Ex-CEO of Enlitic, a healthcare imaging company, and the Current CEO of Fast.ai, a deep learning educational site) to provide insight to deep learning methodology.

Artificial Intelligence (AI) is starting to make a dent in the multi-trillion dollar healthcare industry, from being able to identify cancers and healthcare issues with just a glance, to being able to decipher the mysteries of the human genome and figure out how much longer you have left to live, but now it has a new trick. In a new arXiv paper published by the researchers from Stanford University, who also trained their smart watches to identify when you're getting ill, the team behind the newest AI addition to healthcare explain how CheXNet, their Convolutional Neural Network achieved the feat. CheXNet was trained on a publicly available data set of more than 100,000 chest X-Rays that were annotated with information on at least fourteen different diseases. The team then had four Radiologists go through a test set of X-Rays and make diagnoses, and these were compared to the diagnoses performed by CheXNet. Not only did CheXNet beat the Radiologists at spotting Pneumonia, but once the algorithm was expanded, it proved better at identifying the other thirteen diseases as well.

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 researchers have developed a deep-learning algorithm that evaluates chest X-rays for signs of disease. Stanford researchers have developed an algorithm that offers diagnoses based off chest X-ray images. A paper about the algorithm, called CheXNet, was published Nov. 14 on the open-access, scientific preprint website arXiv. "Interpreting X-ray images to diagnose pathologies like pneumonia is very challenging, and we know that there's a lot of variability in the diagnoses radiologists arrive at," said Pranav Rajpurkar, a graduate student in the Machine Learning Group at Stanford and co-lead author of the paper. "We became interested in developing machine learning algorithms that could learn from hundreds of thousands of chest X-ray diagnoses and make accurate diagnoses."

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. The Machine Learning Group, led by Stanford adjunct professor Andrew Ng, was inspired by a data set released by the National Institutes of Health on 26 September. The data set contains 112,120 chest X-ray images labeled with 14 different possible diagnoses, along with some preliminary algorithms. The researchers asked four Stanford radiologists to annotate 420 of the images for possible indications of pneumonia.

The dataset, released by the NIH, contains 112,120 frontal-view X-ray images of 30,805 unique patients, annotated with up to 14 different thoracic pathology labels using NLP methods on radiology reports. We label images that have pneumonia as one of the annotated pathologies as positive examples and label all other images as negative examples for the pneumonia detection task. We collected a test set of 420 frontal chest X-rays. Annotations were obtained independently from four practicing radiologists at Stanford University, who were asked to label all 14 pathologies, even though . We then evaluate the performance of an individual radiologist by using the majority vote of the other 3 radiologists as ground truth.