Annual Conference of KIPS (한국정보처리학회:학술대회논문집)

  • 2024.10a
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  • Pages.577-579
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  • 2024
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  • 2005-0011(pISSN)
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  • 2671-7298(eISSN)
Korea Information Processing Society (한국정보처리학회)
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Cross-modality Medical Image Generation using Deep Generative Adversarial Network

깊은 생성적 적대 신경망을 이용한 교차 모달리티 의료 이미지 생성

  • 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)
  • 두이풍다오 (전남대학교 인공지능융합학과) ;
  • 양형정 (전남대학교 인공지능융합학과) ;
  • 정혜원 ;
  • 로저 데이비
  • Published : 2024.10.31
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Abstract

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.

Keywords

Acknowledgement

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).

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