Abstract
Deep convolutional generative adversarial networks (DCGANs) are newly developed tools for generating synthesized images. To determine the clinical utility of synthesized images, we generated breast ultrasound images and assessed their quality and clinical value. After retrospectively collecting 528 images of 144 benign masses and 529 images of 216 malignant masses in the breasts, synthesized images were generated using a DCGAN with 50, 100, 200, 500, and 1000 epochs. The synthesized (n = 20) and original (n = 40) images were evaluated by two radiologists, who scored them for overall quality, definition of anatomic structures, and visualization of the masses on a five-point scale. They also scored the possibility of images being original. Although there was no significant difference between the images synthesized with 1000 and 500 epochs, the latter were evaluated as being of higher quality than all other images. Moreover, 2.5%, 0%, 12.5%, 37.5%, and 22.5% of the images synthesized with 50, 100, 200, 500, and 1000 epochs, respectively, and 14% of the original images were indistinguishable from one another. Interobserver agreement was very good (|r| = 0.708–0.825, p < 0.001). Therefore, DCGAN can generate high-quality and realistic synthesized breast ultrasound images that are indistinguishable from the original images.
Highlights
With the recent development of deep learning technology, the use of deep learning methods for medical image synthesis has increased dramatically [1,2,3]
We used a Deep convolutional generative adversarial networks (DCGANs) to synthesize breast ultrasound images and asked two experienced breast radiologists to evaluate those images from a clinical perspective
Our results indicate that high-quality breast ultrasound images that are indistinguishable from the original images can be generated using the DCGAN
Summary
With the recent development of deep learning technology, the use of deep learning methods for medical image synthesis has increased dramatically [1,2,3]. One of the most interesting breakthroughs in the field of deep learning is the advent of generative adversarial networks (GANs), which consist of effective machine learning frameworks used to train unsupervised generative models. A GAN is a special type of neural network model in which two networks are trained simultaneously; one focuses on image generation and the other on discrimination [4]. A deep convolutional GAN (DCGAN) is a direct extension of GAN that uses convolutional and transpose–convolutional layers in the discriminator and generator, respectively. DCGANs can reportedly generate high-quality medical images [6]. When using diagnostic images in Diagnostics 2019, 9, 176; doi:10.3390/diagnostics9040176 www.mdpi.com/journal/diagnostics
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