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J Digit Imaging. 2017 Oct;30(5):615-621. doi: 10.1007/s10278-016-9910-0.

Machine Learning Interface for Medical Image Analysis.

Author information

1
Translational and Molecular Imaging Institute, Icahn School of Medicine at Mount Sinai, 1470 Madison Avenue, 1st Floor, New York, NY, 10029, USA. yi.zhang1@mssm.edu.
2
Department of Radiology, Mount Sinai West, 1000 10th Ave Rm 4C-12, New York, NY, 10019, USA.

Abstract

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.

KEYWORDS:

Artificial intelligence; Classification; Computer vision; Image analysis

PMID:
27730415
PMCID:
PMC5603426
DOI:
10.1007/s10278-016-9910-0
[Indexed for MEDLINE]
Free PMC Article

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