High-Throughput Classification of Radiographs Using Deep Convolutional Neural Networks

By Alvin Rajkomar, Sneha Lingam, Andrew G. Taylor, Michael Blum, John T Mongan. 2016

137 citations recorded by the source corpus.

The study aimed to determine if computer vision techniques rooted in deep learning can use a small set of radiographs to perform clinically relevant image classification with high fidelity. One thousand eight hundred eighty-five chest radiographs on 909 patients obtained between January 2013 and July 2015 at our institution were retrieved and anonymized. The source images were manually annotated as frontal or lateral and randomly divided into training, validation, and test sets. Training and validation sets were augmented to over 150,000 images using standard image manipulations. We then pre-trained a series of deep convolutional networks based on the open-source GoogLeNet with various transformations of the open-source ImageNet (non-radiology) images. These trained networks were then fine-tuned using the original and augmented radiology images. The model with highest validation accuracy was applied to our institutional test set and a publicly available set. Accuracy was assessed by using the Youden Index to set a binary cutoff for frontal or lateral classification. This retrospective study was IRB approved prior to initiation. A network pre-trained on 1.2 million greyscale ImageNet images and fine-tuned on augmented radiographs was chosen. The binary classification method correctly classified 100 % (95 % CI 99.73–100 %) of both our test set and the publicly available images. Classification was rapid, at 38 images per second. A deep convolutional neural network created using non-radiological images, and an augmented set of radiographs is effective in highly accurate classification of chest radiograph view type and is a feasible, rapid method for high-throughput annotation.

Subfields: Artificial Intelligence, Radiology, Nuclear Medicine and Imaging

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Citations in the corpus

A survey on deep learning in medical image analysis (2017, neutral): “Rajkomar et al. (2017) Frontal/lateral classification Pre-trained CNN performs frontal/lateral classification task”
Searching for prostate cancer by fully automated magnetic resonance imaging classification: deep learning versus non-deep learning (2017, methodology): “We used the method published by Samala 33 , Rajkomar 32 to resize each image with cubic interpolation.”
Convolutional Neural Networks-Based MRI Image Analysis for the Alzheimer’s Disease Prediction From Mild Cognitive Impairment (2018, unknown): “As the most widely used architecture of deep learning, convolutional neural networks (CNN) has attracted a lot of attention due to its great success in image classiﬁcation and analysis (Gulshan et al., 2016; Nie et al., 2016; Shin et al., 2016; Rajkomar et al., 2017; Du et al., 2018).”
A scoping review of transfer learning research on medical image analysis using ImageNet (2020, unknown): “M o d el C a te g o ry Inception [27,39–46] [47–51] [52,53] [54–59] [60,61] [62–65] [66] [67] VGGNet [68–73] [74–76] [77–81] [82–84] [85–87] [88] [89– 91] [92– 94] ResNet [95–97] [98– 101] [102– 105] [106] [107–109] [110,111] [112] [113]”
Assessment of Convolutional Neural Networks for Automated Classification of Chest Radiographs. (2019, background): “The remarkable success of machine learning techniques such as convolutional neural networks (CNNs) for image classification tasks makes these algorithms a natural choice for automated radiograph analysis (5,6), and they have already performed well for tasks such as skeletal bone age assessment (7–9)”