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Improved Lung Cancer Detection in Ultra Low dose CT with Combined AI-based Nodule Detection and Denoising Techniques
| DC Field | Value | Language |
|---|---|---|
| dc.contributor.author | Lee, Jemyoung | - |
| dc.contributor.author | Park, Jae-Hyun | - |
| dc.contributor.author | Kim, Minsu | - |
| dc.contributor.author | Heo, Changyoung | - |
| dc.contributor.author | Lee, Kyong Joon | - |
| dc.contributor.author | Kim, Hanyoung | - |
| dc.contributor.author | Kim, Jihang | - |
| dc.contributor.author | Kim, Jong Hyo | - |
| dc.date.accessioned | 2022-10-12T00:54:11Z | - |
| dc.date.available | 2022-10-12T00:54:11Z | - |
| dc.date.created | 2022-09-30 | - |
| dc.date.issued | 2022-01 | - |
| dc.identifier.citation | Proceedings of SPIE - The International Society for Optical Engineering, Vol.12177, p. 1217721 | - |
| dc.identifier.issn | 0277-786X | - |
| dc.identifier.uri | https://hdl.handle.net/10371/185884 | - |
| dc.description.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. | - |
| dc.language | 영어 | - |
| dc.publisher | SPIE | - |
| dc.title | Improved Lung Cancer Detection in Ultra Low dose CT with Combined AI-based Nodule Detection and Denoising Techniques | - |
| dc.type | Article | - |
| dc.identifier.doi | 10.1117/12.2625966 | - |
| dc.citation.journaltitle | Proceedings of SPIE - The International Society for Optical Engineering | - |
| dc.identifier.wosid | 000836377300072 | - |
| dc.identifier.scopusid | 2-s2.0-85131798806 | - |
| dc.citation.startpage | 1217721 | - |
| dc.citation.volume | 12177 | - |
| dc.description.isOpenAccess | N | - |
| dc.contributor.affiliatedAuthor | Kim, Jong Hyo | - |
| dc.type.docType | Proceedings Paper | - |
| dc.description.journalClass | 1 | - |
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