Rajpurkar et al. (2017)
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CheXNet demonstrates AI medical imaging capabilities exceeding radiologist performance, illustrating both the potential for AI in healthcare and the importance of rigorous evaluation, robustness testing, and alignment between AI system capabilities and real-world deployment safety requirements.
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Abstract
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.
Summary
Rajpurkar et al. (2017) present CheXNet, a 121-layer convolutional neural network trained on ChestX-ray14, the largest publicly available chest X-ray dataset with over 100,000 images labeled for 14 diseases. The model achieves pneumonia detection performance exceeding that of practicing radiologists on the F1 metric. The authors extend CheXNet to detect all 14 diseases in the dataset and demonstrate state-of-the-art results across all disease categories, representing a significant advance in automated medical image analysis.
Cited by 1 page
| Page | Type | Quality |
|---|---|---|
| AI-Human Hybrid Systems | Approach | 91.0 |
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[1711.05225] Chest
Chest
Pranav Rajpurkar
Jeremy Irvin
Kaylie Zhu
Brandon Yang
Hershel Mehta
Tony Duan
Daisy Ding
Aarti Bagul
Robyn L. Ball
Curtis Langlotz
Katie Shpanskaya
Matthew P. Lungren
Andrew Y. Ng
CheXNet: Radiologist-Level Pneumonia Detection on Chest X-Rays with Deep Learning
Pranav Rajpurkar
Jeremy Irvin
Kaylie Zhu
Brandon Yang
Hershel Mehta
Tony Duan
Daisy Ding
Aarti Bagul
Robyn L. Ball
Curtis Langlotz
Katie Shpanskaya
Matthew P. Lungren
Andrew Y. Ng
Abstract
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.
healthcare, medical imaging, convolutional neural networks, xrays, pneumonia
Figure 1 :
CheXNet is a 121-layer convolutional neural network that takes a chest X-ray image as input, and outputs the probability of a pathology. On this example, CheXnet correctly detects pneumonia and also localizes areas in the image most indicative of the pathology.
† † Project website at https://stanfordmlgroup.github.io/projects/chexnet
1 Introduction
More than 1 million adults are hospitalized with pneumonia and around 50,000 die from the disease every year in the US alone (CDC, 2017 ) . Chest X-rays are currently the best available method for diagnosing pneumonia (WHO, 2001 ) , playing a crucial role in clinical care (Franquet, 2001 ) and epidemiological studies (Cherian et al., 2005 ) . However, detecting pneumonia in chest X-rays is a challenging task that relies on the availability of expert radiologists. In this work, we present a model that can automatically detect pneumonia from chest X-rays at a level exceeding practicing radiologists.
Our model, ChexNet (shown in Figure 1 ), is a 121-layer convolutional neural network that inputs a chest X-ray image and outputs the probability of pneumonia along with a heatmap localizing the areas of the image most indicative of pneumonia. We train CheXNet on the recently released ChestX-ray14 dataset (Wang et al., 2017 ) , which contains 112,120 frontal-view chest X-ray images individually labeled with up to 14 different thoracic diseases, including pneumonia. We use dense connections (Huang et al., 2016 ) and bat
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