Unsupervised learning for dental CT metal artifact reduction using CycleGAN with CBAM = CycleGAN과 컨볼루션 블록 주의 모듈을 사용한 치아 CT 금속 인공음영 제거 비지도 학습

Cited 0 time in webofscience Cited 0 time in scopus
  • Hit : 18
  • Download : 0
Metal artifact reduction(MAR) is one of the most important issues in Dental computed tomography (CT). Various methods have been suggested for metal artifact removal, among which supervised learning methods are most popular. However matched non-metal - and metal - real CT image pairs are dicult to obtain. In this paper, we propose an unsupervised MAR method for CT using attention cycle-consistent adversarial network. The proposed method is based on unsupervised learning scheme using adversarial loss and cycle-consistency loss to overcome the none of paired data. Moreover adding the convolutional block attention module (CBAM) layers, we can get more improved MAR image and preserve the detailed texture of the original image compare to standard cycle-consistent adversarial network.
Advisors
Ye, Jongchulresearcher예종철researcher
Description
한국과학기술원 :바이오및뇌공학과,
Publisher
한국과학기술원
Issue Date
2020
Identifier
325007
Language
eng
Description

학위논문(석사) - 한국과학기술원 : 바이오및뇌공학과, 2020.8,[iii, 30 p. :]

Keywords

metal artifact removal▼adental CT▼aunsupervised Learning▼acycle-consistent adversarial network▼aconvolutional block attention module; 금속 인공음영 제거▼a치아 CT▼a비지도학습▼aCycleGAN▼a컨볼루션 블록 주의 모듈

URI
http://hdl.handle.net/10203/284931
Link
http://library.kaist.ac.kr/search/detail/view.do?bibCtrlNo=925090&flag=dissertation
Appears in Collection
BiS-Theses_Master(석사논문)
Files in This Item
There are no files associated with this item.

qr_code

  • mendeley

    citeulike


rss_1.0 rss_2.0 atom_1.0