한국정보처리학회:학술대회논문집 (Annual Conference of KIPS)

한국정보처리학회 (Korea Information Processing Society)
권호 표지

깊은 생성적 적대 신경망을 이용한 교차 모달리티 의료 이미지 생성

Cross-modality Medical Image Generation using Deep Generative Adversarial Network

  • 두이풍다오 (전남대학교 인공지능융합학과) ;
  • 양형정 (전남대학교 인공지능융합학과) ;
  • 정혜원 ;
  • 로저 데이비
  • Duy-Phuong Dao (Dept. of Artificial Intelligence Convergence, Chonnam National University) ;
  • Hyung-Jeong Yang (Dept. of Artificial Intelligence Convergence, Chonnam National University) ;
  • Hye-Won Jung (AdelaideMRI) ;
  • Roger Davies (AdelaideMRI)
  • 발행 : 2024.10.31
PDF 다운로드
⟨ 이전 논문 다음 논문 ⟩

초록

Due to the advancement of deep learning techniques, the medical field is undergoing significant upheaval. One of the prominent applications is generating an imaging modality from another imaging modality. This application helps reduce the cost of taking multiple types of medical images for diagnostic imaging. Although many methods have been proposed for generating medical images, only a few studies focus on three-dimensional (3D) images. Therefore, in this paper, we propose a deep generative adversarial network (GAN) for generating a 3D target image from a 3D source image. The results have shown that our proposed approach can generate high-quality images and holds promise for practical use.

키워드

과제정보

This work was supported by Institute of Information & communications Technology Planning & Evaluation (IITP) under the Artificial Intelligence Convergence Innovation Human Resources Development (IITP-2023-RS-2023-00256629) grant funded by the Korea government(MSIT). This work was supported by the National Research Foundation of Korea(NRF) grant funded by the Korea government(MSIT). (RS-2023-00208397). This research was supported by the MSIT(Ministry of Science and ICT), Korea, under the ITRC(Information Technology Research Center) support program(IITP-2024-RS-2024-00437718) supervised by the IITP(Institute for Information & Communications Technology Planning & Evaluation).

참고문헌

  1. Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., ... & Bengio, Y. (2014). Generative adversarial nets. Advances in neural information processing systems, 27.
  2. Bousmalis, K., Silberman, N., Dohan, D., Erhan, D., & Krishnan, D. (2017). Unsupervised pixel-level domain adaptation with generative adversarial networks. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 3722-3731).
  3. Aljohani, A., & Alharbe, N. (2022). Generating synthetic images for healthcare with novel deep pix2pix gan. Electronics, 11(21), 3470.
  4. Yang, Q., Li, N., Zhao, Z., Fan, X., Chang, E. I. C., & Xu, Y. (2020). MRI cross-modality image-to-image translation. Scientific reports, 10(1), 3753.
  5. Liu, S., Zhu, C., Xu, F., Jia, X., Shi, Z., & Jin, M. (2022). Bci: Breast cancer immunohistochemical image generation through pyramid pix2pix. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition (pp. 1815-1824).
  6. Yan, S., Wang, C., Chen, W., & Lyu, J. (2022). Swin transformer-based GAN for multi-modal medical image translation. Frontiers in Oncology, 12, 942511.
  7. Ho, J., Jain, A., & Abbeel, P. (2020). Denoising diffusion probabilistic models. Advances in neural information processing systems, 33, 6840-6851.
  8. Ronneberger, O., Fischer, P., & Brox, T. (2015). U-net: Convolutional networks for biomedical image segmentation. In Medical image computing and computer-assisted intervention-MICCAI 2015: 18th international conference, Munich, Germany, October 5-9, 2015, proceedings, part III 18 (pp. 234-241). Springer International Publishing.
  9. Isola, P., Zhu, J. Y., Zhou, T., & Efros, A. A. (2017). Image-to-image translation with conditional adversarial networks. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 1125-1134).
  10. Kingma, D. P. (2014). Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980.