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John L. Dart Library
9 a.m. – 7 p.m.
Phone: (843) 722-7550
West Ashley Library
9 a.m. – 7 p.m.
Phone: (843) 766-6635
Folly Beach Library
Closed
Phone: (843) 588-2001
Edgar Allan Poe/Sullivan's Island Library
Closed for renovations
Phone: (843) 883-3914
Wando Mount Pleasant Library
9 a.m. – 8 p.m.
Phone: (843) 805-6888
Village Library
9 a.m. – 6 p.m.
Phone: (843) 884-9741
St. Paul's/Hollywood Library
9 a.m. – 8 p.m.
Phone: (843) 889-3300
Otranto Road Library
9 a.m. – 8 p.m.
Phone: (843) 572-4094
Mt. Pleasant Library
9 a.m. – 8 p.m.
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McClellanville Library
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John's Island Library
9 a.m. – 8 p.m.
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Hurd/St. Andrews Library
9 a.m. – 8 p.m.
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Miss Jane's Building (Edisto Library Temporary Location)
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Dorchester Road Library
9 a.m. – 8 p.m.
Phone: (843) 552-6466
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Phone: (843) 795-6679
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9 a.m. – 8 p.m.
Phone: (843) 805-6930
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High-Throughput Classification of Radiographs Using Deep Convolutional Neural Networks.
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- Author(s): Rajkomar, Alvin; Lingam, Sneha; Taylor, Andrew; Blum, Michael; Mongan, John
- Source:
Journal of Digital Imaging; Feb2017, Vol. 30 Issue 1, p95-101, 7p, 1 Diagram, 2 Charts, 3 Graphs- Subject Terms:
- Source:
- Additional Information
- Subject Terms:
- Abstract: 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. [ABSTRACT FROM AUTHOR]
- Abstract: Copyright of Journal of Digital Imaging is the property of Springer Nature and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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