Feasibility study of deep convolutional generative adversarial networks to generate mammography images

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)


We conducted a feasibility study to generate mammography images using a deep convolutional generative adversarial network (DCGAN), which directly produces realistic images without 3-D model passing through any complex rendering algorithm, such as ray tracing. We trained DCGAN with breast 2D mammography images, which were generated from anatomical noise. The generated X-ray mammography images were successful in that the image preserves reasonable quality and retains the visual patterns similar to training images. Especially, generated images share the distinctive structure of training images. For the quantitative evaluation, we used the mean and variance of beta values of generated images and observed that they are very similar to those of training images. Although the general distribution of generated images matches well with those of training images, there are several limitations of the DCGAN. First, checkboard pattern like artifacts are found in generated images, which is a well-known issue of deconvolution algorithm. Moreover, training GAN is often unstable so to require manual fine-tunes. To overcome such limitations, we plan to extend our idea to conditional GAN approach for improving training stability, and employ an auto-encoder for handling artifacts. To validate our idea on real data, we will apply clinical images to train the network. We believe that our framework can be easily extended to generate other medical images.

Original languageEnglish
Title of host publicationMedical Imaging 2018
Subtitle of host publicationImage Perception, Observer Performance, and Technology Assessment
EditorsFrank W. Samuelson, Robert M. Nishikawa
ISBN (Electronic)9781510616431
Publication statusPublished - 2018
EventMedical Imaging 2018: Image Perception, Observer Performance, and Technology Assessment - Houston, United States
Duration: 2018 Feb 112018 Feb 12

Publication series

NameProgress in Biomedical Optics and Imaging - Proceedings of SPIE
ISSN (Print)1605-7422


ConferenceMedical Imaging 2018: Image Perception, Observer Performance, and Technology Assessment
Country/TerritoryUnited States

Bibliographical note

Funding Information:
Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (2017M2A2A4A01070302, 2017M2A2A2A6A01019663, 2015R1C1A1A101052268); MSIT(Ministry of Science and ICT), Korea, under the “ICT Consilience Creative program” (IITP-2017-2017-0-01015) supervised by the IITP(Institute for Information & Communication Technology Promotion)

Publisher Copyright:
© 2018 SPIE.

All Science Journal Classification (ASJC) codes

  • Electronic, Optical and Magnetic Materials
  • Biomaterials
  • Atomic and Molecular Physics, and Optics
  • Radiology Nuclear Medicine and imaging


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