Full metadata record
DC Field | Value | Language |
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dc.contributor.author | Won, Dongkyu | - |
dc.contributor.author | An, Sion | - |
dc.contributor.author | Park, Sang Hyun | - |
dc.contributor.author | Ye, Dong Hye | - |
dc.date.accessioned | 2021-01-29T07:25:03Z | - |
dc.date.available | 2021-01-29T07:25:03Z | - |
dc.date.created | 2020-10-29 | - |
dc.date.issued | 2020-10-05 | - |
dc.identifier.isbn | 9783030593537 | - |
dc.identifier.issn | 0302-9743 | - |
dc.identifier.uri | http://hdl.handle.net/20.500.11750/12885 | - |
dc.description.abstract | Low-dose CT denoising has been studied to reduce radiation exposure to patients. Recently, deep learning-based techniques have improved the CT denoising performance, but it is difficult to reflect the characteristics of signals concerning different frequencies properly. Even though high-frequency components play an essential role in denoising, the deep network with a large number of parameters doesn’t concern it and tends to generate the image still having noise and losing the structure. To address this problem, we propose a novel CT denoising method that decomposes high- and low-frequency features and learns more parameters on important features during training. We introduce a network consisting of Octave convolution layers that take feature maps with two frequencies and extract information directly from both maps with inter- and intra-convolutions. The proposed method effectively reduces the noise while maintaining edge sharpness by reducing the spatial redundancy in the network. For evaluation, the 2016 AAPM Low-Dose CT challenge data set was used. The proposed method achieved better performance than the existing CT denoising methods in quantitative and qualitative evaluations. © 2020, Springer Nature Switzerland AG. | - |
dc.language | English | - |
dc.publisher | SPRINGER INTERNATIONAL PUBLISHING AG | - |
dc.relation.ispartof | PREDICTIVE INTELLIGENCE IN MEDICINE, PRIME 2020 | - |
dc.title | Low-Dose CT Denoising Using Octave Convolution with High and Low Frequency Bands | - |
dc.type | Conference Paper | - |
dc.identifier.doi | 10.1007/978-3-030-59354-4_7 | - |
dc.identifier.wosid | 001116105300007 | - |
dc.identifier.scopusid | 2-s2.0-85092928213 | - |
dc.identifier.bibliographicCitation | 3rd International Workshop on Predictive Intelligence in Medicine (PRIME), pp.68 - 78 | - |
dc.citation.conferenceDate | 2020-10-03 | - |
dc.citation.conferencePlace | PE | - |
dc.citation.conferencePlace | Lima | - |
dc.citation.endPage | 78 | - |
dc.citation.startPage | 68 | - |
dc.citation.title | 3rd International Workshop on Predictive Intelligence in Medicine (PRIME) | - |
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