Deep Residual Convolutional Sparse Coding Networks for Low Dose CT Imaging
With the explosion of deep learning algorithms, big data, and high-performance computing, deep learning has flourished in the fields of medical analysis and image processing. In this paper, we present a simple yet effective model for low dose computed tomography (CT) image processing procedure, by c...
Uložené v:
| Vydané v: | 2021 14th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI) s. 1 - 6 |
|---|---|
| Hlavní autori: | , , , |
| Médium: | Konferenčný príspevok.. |
| Jazyk: | English |
| Vydavateľské údaje: |
IEEE
23.10.2021
|
| Predmet: | |
| On-line prístup: | Získať plný text |
| Tagy: |
Pridať tag
Žiadne tagy, Buďte prvý, kto otaguje tento záznam!
|
| Abstract | With the explosion of deep learning algorithms, big data, and high-performance computing, deep learning has flourished in the fields of medical analysis and image processing. In this paper, we present a simple yet effective model for low dose computed tomography (CT) image processing procedure, by combining with the advantages of residual convolution network and convolutional sparse coding (DRCSC). Through the learned iterative shrinkage threshold algorithm (LISTA), we extend convolutional sparse coding to its convolutional learning from and entirely following the residual convolution network structure, which improves the network's interpretability. The network workflow consists of three components: input feature maps prepare, recursive manner for feature maps learning by convolutional sparse coding, and high-frequency information recover. Within the residual learning strategy, the deep network training become easier and preservation more detail feature. Experiments on AAPM datasets has shown the efficacy of our method. Network testing results identify that the proposed method can restrain of artifacts and noise oscillations for low dose CT imaging. |
|---|---|
| AbstractList | With the explosion of deep learning algorithms, big data, and high-performance computing, deep learning has flourished in the fields of medical analysis and image processing. In this paper, we present a simple yet effective model for low dose computed tomography (CT) image processing procedure, by combining with the advantages of residual convolution network and convolutional sparse coding (DRCSC). Through the learned iterative shrinkage threshold algorithm (LISTA), we extend convolutional sparse coding to its convolutional learning from and entirely following the residual convolution network structure, which improves the network's interpretability. The network workflow consists of three components: input feature maps prepare, recursive manner for feature maps learning by convolutional sparse coding, and high-frequency information recover. Within the residual learning strategy, the deep network training become easier and preservation more detail feature. Experiments on AAPM datasets has shown the efficacy of our method. Network testing results identify that the proposed method can restrain of artifacts and noise oscillations for low dose CT imaging. |
| Author | Qiang, Jun Liu, Jin Xia, Zhenyu Kang, Yanqin |
| Author_xml | – sequence: 1 givenname: Jin surname: Liu fullname: Liu, Jin organization: Anhui Polytechnic University,College of Computer and Information,Wuhu,China – sequence: 2 givenname: Zhenyu surname: Xia fullname: Xia, Zhenyu organization: Anhui Polytechnic University,College of Computer and Information,Wuhu,China – sequence: 3 givenname: Yanqin surname: Kang fullname: Kang, Yanqin organization: Anhui Polytechnic University,College of Computer and Information,Wuhu,China – sequence: 4 givenname: Jun surname: Qiang fullname: Qiang, Jun organization: Anhui Polytechnic University,College of Computer and Information,Wuhu,China |
| BookMark | eNotj01Pg0AYhNdED1r7C7xs4hl89xP2qLRWDH7E1nOzwLvNRsoSoDb-ezH2NJlnJpPMFTlvQ4uE3DKIGQNzl-Xr9-jhZZkrobmJOXAWG82l0MkZmZskZVorCQBSX5LnBWJHP3Dw9cE2NAvtd2gOow_t5Nad7QecYO3bHX3F8Rj6r4G60NMiHOki_IUbmu_tbipckwtnmwHnJ52Rz8flJnuKirdVnt0XkWcsHSNZGXCVrm2SIi-V5I6hA6tKVk9gIoZBWRlhZWq0YqVKE1EmBjQgSKydmJGb_12PiNuu93vb_2xPD8UvS-hL2A |
| ContentType | Conference Proceeding |
| DBID | 6IE 6IL CBEJK RIE RIL |
| DOI | 10.1109/CISP-BMEI53629.2021.9624367 |
| DatabaseName | IEEE Electronic Library (IEL) Conference Proceedings IEEE Xplore POP ALL IEEE Xplore All Conference Proceedings IEEE/IET Electronic Library IEEE Proceedings Order Plans (POP All) 1998-Present |
| DatabaseTitleList | |
| Database_xml | – sequence: 1 dbid: RIE name: IEEE/IET Electronic Library url: https://ieeexplore.ieee.org/ sourceTypes: Publisher |
| DeliveryMethod | fulltext_linktorsrc |
| EISBN | 9781665400046 1665400048 |
| EndPage | 6 |
| ExternalDocumentID | 9624367 |
| Genre | orig-research |
| GrantInformation_xml | – fundername: National Natural Science Foundation grantid: 61801003 funderid: 10.13039/501100001809 |
| GroupedDBID | 6IE 6IL CBEJK RIE RIL |
| ID | FETCH-LOGICAL-i118t-4c90fc6da78e2b542f1ef0a5b1d8e22b5910bc93a489651b5873b79060e04edf3 |
| IEDL.DBID | RIE |
| IngestDate | Thu Jun 29 18:37:45 EDT 2023 |
| IsPeerReviewed | false |
| IsScholarly | false |
| Language | English |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-i118t-4c90fc6da78e2b542f1ef0a5b1d8e22b5910bc93a489651b5873b79060e04edf3 |
| PageCount | 6 |
| ParticipantIDs | ieee_primary_9624367 |
| PublicationCentury | 2000 |
| PublicationDate | 2021-Oct.-23 |
| PublicationDateYYYYMMDD | 2021-10-23 |
| PublicationDate_xml | – month: 10 year: 2021 text: 2021-Oct.-23 day: 23 |
| PublicationDecade | 2020 |
| PublicationTitle | 2021 14th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI) |
| PublicationTitleAbbrev | CISP-BMEI |
| PublicationYear | 2021 |
| Publisher | IEEE |
| Publisher_xml | – name: IEEE |
| Score | 1.7739226 |
| Snippet | With the explosion of deep learning algorithms, big data, and high-performance computing, deep learning has flourished in the fields of medical analysis and... |
| SourceID | ieee |
| SourceType | Publisher |
| StartPage | 1 |
| SubjectTerms | Computed tomography Convolution Convolutional codes convolutional sparse coding Deep learning Image coding low dose CT noise-artifacts Protocols residual network Training |
| Title | Deep Residual Convolutional Sparse Coding Networks for Low Dose CT Imaging |
| URI | https://ieeexplore.ieee.org/document/9624367 |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV1LSwMxEB5qEfGk0opvAno07e4mm2yu9oEVLcVW6K1kkwn0YFv68u-bbJeK4MVb8iVDYBIyM0m-DMBDuCJ0LmU00YpRLpmhGeqYWm-crUFlLGZFsgnZ72fjsRpU4HHPhUHE4vEZNkKxuMu3c7MJR2VNJRLOhDyAAynFjqt1BPflt5nNVm84oE9vnV7qN-XAQUniRinxK3VKYTm6J_8b8xTqPxQ8MtgblzOo4KwGL23EBXnHVcGgIr7rtlw5vjZc-BgVPRgESH_3vHtFvFNKXudfpD0PjSPS-yzyEtXho9sZtZ5pmQyBTn0MsKbcqMgZYbXMMMlTnrgYXaTTPLYe8Ii3-7lRTPNMiTTO00yyXKpIRBhxtI6dQ3U2n-EFEIy55kZq58NT7lyUS-uM9o5YljsphLqEWtDDZLH772JSquDqb_gajoOqw36esBuorpcbvIVDs11PV8u7YpK-AUAhlI4 |
| linkProvider | IEEE |
| linkToHtml | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV1LTwMhECa1GvWkpjW-JdGj1N2FXZarfaSr7aaxNemtYWFIerDb9OXfF7abGhMv3mCAkAxkhgG--RB6dE-ExoSUBFJQwjhVJAbpE22ds1YglIa4IJvgaRqPx2JQQU87LAwAFJ_PoOGKxVu-ztXaXZU9iyhgNOJ7aN8xZ5VorUP0UCbOfG4mwwF56beT0Jplh0IJ_EY55hd5SuE7Oif_m_UU1X9AeHiwcy9nqAKzGnptAczxOywLDBW2XTfl3rG14dxGqWCFbgBOtx-8l9geS3Ev_8Kt3DWOcPJZMBPV0UenPWp2SUmHQKY2ClgRpoRnVKQljyHIQhYYH4wnw8zXVmAl1vNnSlDJYhGFfhbGnGZceJEHHgNt6DmqzvIZXCAMPpNMcWlsgMqM8TKujZL2KBZnhkeRuEQ1p4fJfJvxYlKq4Opv8T066o76vUkvSd-u0bFTu7PuAb1B1dViDbfoQG1W0-Xirliwby59l9c |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Abook&rft.genre=proceeding&rft.title=2021+14th+International+Congress+on+Image+and+Signal+Processing%2C+BioMedical+Engineering+and+Informatics+%28CISP-BMEI%29&rft.atitle=Deep+Residual+Convolutional+Sparse+Coding+Networks+for+Low+Dose+CT+Imaging&rft.au=Liu%2C+Jin&rft.au=Xia%2C+Zhenyu&rft.au=Kang%2C+Yanqin&rft.au=Qiang%2C+Jun&rft.date=2021-10-23&rft.pub=IEEE&rft.spage=1&rft.epage=6&rft_id=info:doi/10.1109%2FCISP-BMEI53629.2021.9624367&rft.externalDocID=9624367 |