Machine Learning Interface for Medical Image Analysis.
TensorFlow is a second-generation open-source machine learning software library with a built-in framework for implementing neural networks in wide variety of perceptual tasks. Although TensorFlow usage is well established with computer vision datasets, the TensorFlow interface with DICOM formats for...
| Published in: | Journal of Digital Imaging Vol. 30; no. 5; pp. 615 - 622 |
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| Main Authors: | , |
| Format: | research tables/charts Journal Article |
| Published: |
Springer Nature
Oct2017
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| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=125205816&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 125205816 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 08971889 DOQ jtl: Journal of Digital Imaging issn: 08971889 maglogo: N pubinfo: dt: Oct2017 vid: 30 iid: 5 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 125205816 125205816 144161913 125205816 10.1007/s10278-016-9910-0 125205816 ppf: 615 ppct: 7 formats: fmt: @attributes: type: P tig: atl: Machine Learning Interface for Medical Image Analysis. aug: au: Zhang, Yi Kagen, Alexander affil: Translational and Molecular Imaging Institute , Icahn School of Medicine at Mount Sinai , 1470 Madison Avenue, 1st Floor New York 10029 USA sug: subj: Neural Networks (Computer) DICOM Image Processing, Computer Assisted Parkinson Disease Radiography Parkinson Disease Diagnosis Software Algorithms Validation Studies Confidence Intervals Sensitivity and Specificity Human ab: TensorFlow is a second-generation open-source machine learning software library with a built-in framework for implementing neural networks in wide variety of perceptual tasks. Although TensorFlow usage is well established with computer vision datasets, the TensorFlow interface with DICOM formats for medical imaging remains to be established. Our goal is to extend the TensorFlow API to accept raw DICOM images as input; 1513 DaTscan DICOM images were obtained from the Parkinson's Progression Markers Initiative (PPMI) database. DICOM pixel intensities were extracted and shaped into tensors, or n-dimensional arrays, to populate the training, validation, and test input datasets for machine learning. A simple neural network was constructed in TensorFlow to classify images into normal or Parkinson's disease groups. Training was executed over 1000 iterations for each cross-validation set. The gradient descent optimization and Adagrad optimization algorithms were used to minimize cross-entropy between the predicted and ground-truth labels. Cross-validation was performed ten times to produce a mean accuracy of 0.938 ± 0.047 (95 % CI 0.908-0.967). The mean sensitivity was 0.974 ± 0.043 (95 % CI 0.947-1.00) and mean specificity was 0.822 ± 0.207 (95 % CI 0.694-0.950). We extended the TensorFlow API to enable DICOM compatibility in the context of DaTscan image analysis. We implemented a neural network classifier that produces diagnostic accuracies on par with excellent results from previous machine learning models. These results indicate the potential role of TensorFlow as a useful adjunct diagnostic tool in the clinical setting. pubtype: Academic Journal doctype: research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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