Improved Lung Cancer Detection in Ultra Low dose CT with Combined AI-based Nodule Detection and Denoising Techniques

Jemyoung Lee, Changyoung Heo, Jae Hyun Park, Kyong Joon Lee, Minsu Kim, Hanyoung Kim, Jihang Kim, Jong Hyo Kim

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

In this study, we evaluated the synergy between the two artificial intelligence solutions by applying the deep learning based denoising technique to determine if the performance of the AI-based lung nodule detection solution is enhanced.

Original languageEnglish
Title of host publicationInternational Workshop on Advanced Imaging Technology, IWAIT 2022
EditorsMasayuki Nakajima, Shogo Muramatsu, Jae-Gon Kim, Jing-Ming Guo, Qian Kemao
PublisherSPIE
ISBN (Electronic)9781510653313
DOIs
StatePublished - 2022
Event2022 International Workshop on Advanced Imaging Technology, IWAIT 2022 - Hong Kong, China
Duration: 4 Jan 20226 Jan 2022

Publication series

NameProceedings of SPIE - The International Society for Optical Engineering
Volume12177
ISSN (Print)0277-786X
ISSN (Electronic)1996-756X

Conference

Conference2022 International Workshop on Advanced Imaging Technology, IWAIT 2022
Country/TerritoryChina
CityHong Kong
Period4/01/226/01/22

Keywords

  • deep learning
  • denoising
  • lung nodule
  • nodule-wise sensitivity
  • ultra low dose

Fingerprint

Dive into the research topics of 'Improved Lung Cancer Detection in Ultra Low dose CT with Combined AI-based Nodule Detection and Denoising Techniques'. Together they form a unique fingerprint.

Cite this