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A Practical Implementation of Deep Learning Method for Supporting the Classification of Breast Lesions in Ultrasound Images

  • Han, Seokmin (Department of Computer Science and Information Engineering, Korea National University of Transportation) ;
  • Lee, Suchul (Department of Computer Science and Information Engineering, Korea National University of Transportation) ;
  • Lee, Jun-Rak (Division of Liberal Studies, Kangwon National University)
  • Received : 2019.01.07
  • Accepted : 2019.01.20
  • Published : 2019.03.31

Abstract

In this research, a practical deep learning framework to differentiate the lesions and nodules in breast acquired with ultrasound imaging has been proposed. 7408 ultrasound breast images of 5151 patient cases were collected. All cases were biopsy proven and lesions were semi-automatically segmented. To compensate for the shift caused in the segmentation, the boundaries of each lesion were drawn using Fully Convolutional Networks(FCN) segmentation method based on the radiologist's specified point. The data set consists of 4254 benign and 3154 malignant lesions. In 7408 ultrasound breast images, the number of training images is 6579, and the number of test images is 829. The margin between the boundary of each lesion and the boundary of the image itself varied for training image augmentation. The training images were augmented by varying the margin between the boundary of each lesion and the boundary of the image itself. The images were processed through histogram equalization, image cropping, and margin augmentation. The networks trained on the data with augmentation and the data without augmentation all had AUC over 0.95. The network exhibited about 90% accuracy, 0.86 sensitivity and 0.95 specificity. Although the proposed framework still requires to point to the location of the target ROI with the help of radiologists, the result of the suggested framework showed promising results. It supports human radiologist to give successful performance and helps to create a fluent diagnostic workflow that meets the fundamental purpose of CADx.

Keywords

Breast Cancer;Deep Learning;FCN;segmentation

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Figure 1. The conceptual architecture of the proposed deep learning CAD framework

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Figure 2. The conceptual architecture of the proposed deep learning CAD framework

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Figure 4. ROC curves of GoogLeNets trained and tested on the images without a margin(black) and with a margin(red).

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Figure 5. Saliency map examples that shows where the important information exists in the image.

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Figure 6. ROC curves of GoogLeNet without the data augmentation and with the dataaugmentation.

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Figure 7. The perturbation in the center location by radiologist does not affect the performance much

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Figure 8. An implementation examples of (a)a possibly benign lesion, (b)a possibly malignant lesion.

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Figure 3. (a) and (b) are examples of segmented boundaries of breast lesion.

Table 1. Overview of the lesion size attributes of training data and test data.

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Table 2. Comparison of the required time for learning and the number of training images.

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Table 3. Performance comparison of a CNN trained on the images with a margin to a network trained on images without a margin. GLN refers to GoogLeNet.

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Table 4. Diagnostic performances of CNN networks.

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Table 5. Diagnostic performances on centered images, and perturbed images.

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Acknowledgement

Supported by : National Research Foundation of Korea(NRF), Korea National University of Transportation, Kangwon National University

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