AutoEncoder-Driven Multimodal Collaborative Learning for Medical Image Synthesis
Multimodal medical images have been widely applied in various clinical diagnoses and treatments. Due to the practical restrictions, certain modalities may be hard to acquire, resulting in incomplete data. Existing methods attempt to generate the missing data with multiple available modalities. Howev...
Gespeichert in:
| Veröffentlicht in: | International journal of computer vision Jg. 131; H. 8; S. 1995 - 2014 |
|---|---|
| Hauptverfasser: | , , , , , , |
| Format: | Journal Article |
| Sprache: | Englisch |
| Veröffentlicht: |
New York
Springer US
01.08.2023
Springer Springer Nature B.V |
| Schlagworte: | |
| ISSN: | 0920-5691, 1573-1405 |
| Online-Zugang: | Volltext |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| Abstract | Multimodal medical images have been widely applied in various clinical diagnoses and treatments. Due to the practical restrictions, certain modalities may be hard to acquire, resulting in incomplete data. Existing methods attempt to generate the missing data with multiple available modalities. However, the modality differences in tissue contrast and lesion appearance become an obstacle to making a precise estimation. To address this issue, we propose an autoencoder-driven multimodal collaborative learning framework for medical image synthesis. The proposed approach takes an autoencoder to comprehensively supervise the synthesis network using the self-representation of target modality, which provides target-modality-specific prior to guide multimodal image fusion. Furthermore, we endow the autoencoder with adversarial learning capabilities by converting its encoder into a pixel-sensitive discriminator capable of both reconstruction and discrimination. To this end, the generative model is completely supervised by the autoencoder. Considering the efficiency of multimodal generation, we also introduce a modality mask vector as the target modality label to guide the synthesis direction, empowering our method to estimate any missing modality with a single model. Extensive experiments on multiple medical image datasets demonstrate the significant generalization capability as well as the superior synthetic quality of the proposed method, compared with other competing methods. The source code will be available:
https://github.com/bcaosudo/AE-GAN
. |
|---|---|
| AbstractList | Multimodal medical images have been widely applied in various clinical diagnoses and treatments. Due to the practical restrictions, certain modalities may be hard to acquire, resulting in incomplete data. Existing methods attempt to generate the missing data with multiple available modalities. However, the modality differences in tissue contrast and lesion appearance become an obstacle to making a precise estimation. To address this issue, we propose an autoencoder-driven multimodal collaborative learning framework for medical image synthesis. The proposed approach takes an autoencoder to comprehensively supervise the synthesis network using the self-representation of target modality, which provides target-modality-specific prior to guide multimodal image fusion. Furthermore, we endow the autoencoder with adversarial learning capabilities by converting its encoder into a pixel-sensitive discriminator capable of both reconstruction and discrimination. To this end, the generative model is completely supervised by the autoencoder. Considering the efficiency of multimodal generation, we also introduce a modality mask vector as the target modality label to guide the synthesis direction, empowering our method to estimate any missing modality with a single model. Extensive experiments on multiple medical image datasets demonstrate the significant generalization capability as well as the superior synthetic quality of the proposed method, compared with other competing methods. The source code will be available: Multimodal medical images have been widely applied in various clinical diagnoses and treatments. Due to the practical restrictions, certain modalities may be hard to acquire, resulting in incomplete data. Existing methods attempt to generate the missing data with multiple available modalities. However, the modality differences in tissue contrast and lesion appearance become an obstacle to making a precise estimation. To address this issue, we propose an autoencoder-driven multimodal collaborative learning framework for medical image synthesis. The proposed approach takes an autoencoder to comprehensively supervise the synthesis network using the self-representation of target modality, which provides target-modality-specific prior to guide multimodal image fusion. Furthermore, we endow the autoencoder with adversarial learning capabilities by converting its encoder into a pixel-sensitive discriminator capable of both reconstruction and discrimination. To this end, the generative model is completely supervised by the autoencoder. Considering the efficiency of multimodal generation, we also introduce a modality mask vector as the target modality label to guide the synthesis direction, empowering our method to estimate any missing modality with a single model. Extensive experiments on multiple medical image datasets demonstrate the significant generalization capability as well as the superior synthetic quality of the proposed method, compared with other competing methods. The source code will be available: https://github.com/bcaosudo/AE-GAN . Multimodal medical images have been widely applied in various clinical diagnoses and treatments. Due to the practical restrictions, certain modalities may be hard to acquire, resulting in incomplete data. Existing methods attempt to generate the missing data with multiple available modalities. However, the modality differences in tissue contrast and lesion appearance become an obstacle to making a precise estimation. To address this issue, we propose an autoencoder-driven multimodal collaborative learning framework for medical image synthesis. The proposed approach takes an autoencoder to comprehensively supervise the synthesis network using the self-representation of target modality, which provides target-modality-specific prior to guide multimodal image fusion. Furthermore, we endow the autoencoder with adversarial learning capabilities by converting its encoder into a pixel-sensitive discriminator capable of both reconstruction and discrimination. To this end, the generative model is completely supervised by the autoencoder. Considering the efficiency of multimodal generation, we also introduce a modality mask vector as the target modality label to guide the synthesis direction, empowering our method to estimate any missing modality with a single model. Extensive experiments on multiple medical image datasets demonstrate the significant generalization capability as well as the superior synthetic quality of the proposed method, compared with other competing methods. The source code will be available: https://github.com/bcaosudo/AE-GAN. |
| Audience | Academic |
| Author | Bi, Zhiwei Cao, Bing Gao, Xinbo Zhang, Han Shen, Dinggang Wang, Nannan Hu, Qinghua |
| Author_xml | – sequence: 1 givenname: Bing orcidid: 0000-0002-0316-5404 surname: Cao fullname: Cao, Bing organization: College of Intelligence and Computing, Tianjin University, State Key Laboratory of Integrated Services Networks, Xidian University – sequence: 2 givenname: Zhiwei surname: Bi fullname: Bi, Zhiwei organization: College of Intelligence and Computing, Tianjin University – sequence: 3 givenname: Qinghua surname: Hu fullname: Hu, Qinghua email: huqinghua@tju.edu.cn organization: College of Intelligence and Computing, Tianjin University – sequence: 4 givenname: Han surname: Zhang fullname: Zhang, Han organization: School of Biomedical Engineering, ShanghaiTech University – sequence: 5 givenname: Nannan surname: Wang fullname: Wang, Nannan organization: State Key Laboratory of Integrated Services Networks, Xidian University – sequence: 6 givenname: Xinbo surname: Gao fullname: Gao, Xinbo organization: Chongqing University of Posts and Telecommunications – sequence: 7 givenname: Dinggang surname: Shen fullname: Shen, Dinggang organization: School of Biomedical Engineering, ShanghaiTech University, Shanghai United Imaging Intelligence Co., Ltd., Shanghai Clinical Research and Trial Center |
| BookMark | eNp9kV1rHCEUhiUkkM3HH8jVQK9ne_yYGb1ctptkYUMLyb24znFjmNFUZwP597HdQmgpi4qgz3PE816Q0xADEnJDYU4Buq-ZUtbyGlhZtFO0hhMyo03HayqgOSUzUAzqplX0nFzk_AIATDI-Iz8W-ymugo09pvpb8m8Yqof9MPkx9maolnEYzDYmM5WbaoMmBR92lYupesDe24KsR7PD6vE9TM-Yfb4iZ84MGa__7Jfk6Xb1tLyvN9_v1svFprZctlPd01YI2gkHoudgQaltmYz2jiNT214JkEKiVMIJMBal3FLk4KhrBSjkl-TLoexrij_3mCf9EvcplBc1k1w2oDpOP6mdGVD74OKUjB19tnrRNV0juehYoeb_ocrocfS2NNr5cv6XIA-CTTHnhE5bP5UWxVBEP2gK-lcq-pCKLqno36loKCr7R31NfjTp_bjED1IucNhh-vzsEesDFJafCg |
| CitedBy_id | crossref_primary_10_1007_s00371_023_03171_8 crossref_primary_10_1007_s00371_024_03377_4 crossref_primary_10_1007_s11263_024_02004_y crossref_primary_10_1080_00102202_2025_2501695 crossref_primary_10_1109_TCSVT_2025_3528981 crossref_primary_10_1080_01431161_2025_2471595 crossref_primary_10_1007_s00521_023_09283_5 crossref_primary_10_1007_s11263_024_02128_1 crossref_primary_10_1007_s11263_025_02389_4 crossref_primary_10_1117_1_JEI_33_5_053011 crossref_primary_10_1016_j_knosys_2025_114313 crossref_primary_10_1109_TPAMI_2024_3465649 crossref_primary_10_3390_bioengineering11010017 crossref_primary_10_1016_j_eswa_2025_127367 |
| Cites_doi | 10.1038/nature08538 10.1090/S0002-9904-1920-03378-1 10.1007/BF00332918 10.2967/jnumed.115.156299 10.1007/s11263-019-01285-y 10.1007/s11263-021-01448-w 10.1109/TMI.2014.2377694 10.1007/s11263-006-6855-7 10.1016/j.media.2020.101944 10.1016/j.media.2016.07.009 10.1007/s11263-019-01284-z 10.1109/TMI.2018.2884053 10.1111/1467-985X.00122 10.2307/1932409 10.1007/s11263-021-01510-7 10.1109/TIP.2003.819861 10.1109/TMI.2022.3167808 10.1002/(SICI)1097-0258(19990330)18:6<681::AID-SIM71>3.0.CO;2-R 10.1109/TMI.2014.2340135 10.1007/s11263-021-01501-8 10.1109/TMI.2017.2781192 10.1016/j.media.2016.08.009 10.1007/s11263-020-01321-2 10.1109/TMI.2020.2975344 10.1126/science.1127647 10.1109/ACCESS.2018.2872025 10.1109/TIP.2011.2109730 10.1007/s11263-017-1015-9 10.1109/TMI.2017.2759102 10.1073/pnas.90.24.11944 10.1109/TMM.2019.2898777 10.1609/aaai.v34i07.6619 10.1007/978-3-030-58545-7_19 10.1146/annurev-bioeng-071516-044442 10.1109/CVPR.2016.265 10.1109/CVPR.2018.00917 10.1007/978-3-030-11726-9_4 10.1109/IWAIT.2018.8369657 10.1109/CVPR.2017.632 10.1109/CVPR.2019.00453 10.1109/CVPR.2019.00726 10.1007/978-3-319-10443-0_39 10.1109/CVPR.2017.19 10.1007/978-3-031-18523-6_8 10.1007/978-3-030-58580-8_13 10.1109/CVPR.2019.00259 10.1109/ISBI.2013.6556484 10.1109/ICCV.2017.244 10.1109/TMI.2023.3290149 10.1109/CVPR.2019.00244 10.1109/WACV51458.2022.00103 10.1007/978-3-319-24574-4_28 10.1109/CVPR.2016.90 10.18653/v1/D16-1139 10.1007/978-3-319-66179-7_48 10.1109/CVPR.2018.00916 |
| ContentType | Journal Article |
| Copyright | The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. COPYRIGHT 2023 Springer |
| Copyright_xml | – notice: The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. – notice: COPYRIGHT 2023 Springer |
| DBID | AAYXX CITATION 3V. 7SC 7WY 7WZ 7XB 87Z 8AL 8FD 8FE 8FG 8FK 8FL ABUWG AFKRA ARAPS AZQEC BENPR BEZIV BGLVJ CCPQU DWQXO FRNLG F~G GNUQQ HCIFZ JQ2 K60 K6~ K7- L.- L7M L~C L~D M0C M0N P5Z P62 PHGZM PHGZT PKEHL PQBIZ PQBZA PQEST PQGLB PQQKQ PQUKI PYYUZ Q9U |
| DOI | 10.1007/s11263-023-01791-0 |
| DatabaseName | CrossRef ProQuest Central (Corporate) Computer and Information Systems Abstracts ABI/INFORM Collection ABI/INFORM Global (PDF only) ProQuest Central (purchase pre-March 2016) ABI/INFORM Global (Alumni Edition) Computing Database (Alumni Edition) Technology Research Database ProQuest SciTech Collection ProQuest Technology Collection ProQuest Central (Alumni) (purchase pre-March 2016) ABI/INFORM Collection (Alumni Edition) ProQuest Central (Alumni) ProQuest Central UK/Ireland Advanced Technologies & Computer Science Collection ProQuest Central Essentials ProQuest Central Business Premium Collection Technology collection ProQuest One ProQuest Central Business Premium Collection (Alumni) ABI/INFORM Global (Corporate) ProQuest Central Student SciTech Premium Collection ProQuest Computer Science Collection ProQuest Business Collection (Alumni Edition) ProQuest Business Collection Computer Science Database ABI/INFORM Professional Advanced Advanced Technologies Database with Aerospace Computer and Information Systems Abstracts Academic Computer and Information Systems Abstracts Professional ABI/INFORM Global Computing Database Advanced Technologies & Aerospace Database ProQuest Advanced Technologies & Aerospace Collection ProQuest Central Premium ProQuest One Academic ProQuest One Academic Middle East (New) ProQuest One Business (UW System Shared) ProQuest One Business (Alumni) ProQuest One Academic Eastern Edition (DO NOT USE) One Applied & Life Sciences ProQuest One Academic (retired) ProQuest One Academic UKI Edition ABI/INFORM Collection China ProQuest Central Basic |
| DatabaseTitle | CrossRef ABI/INFORM Global (Corporate) ProQuest Business Collection (Alumni Edition) ProQuest One Business Computer Science Database ProQuest Central Student Technology Collection Technology Research Database Computer and Information Systems Abstracts – Academic ProQuest One Academic Middle East (New) ProQuest Advanced Technologies & Aerospace Collection ProQuest Central Essentials ProQuest Computer Science Collection Computer and Information Systems Abstracts ProQuest Central (Alumni Edition) SciTech Premium Collection ProQuest One Community College ABI/INFORM Complete ProQuest Central ABI/INFORM Professional Advanced ProQuest One Applied & Life Sciences ProQuest Central Korea ProQuest Central (New) Advanced Technologies Database with Aerospace ABI/INFORM Complete (Alumni Edition) Advanced Technologies & Aerospace Collection Business Premium Collection ABI/INFORM Global ProQuest Computing ABI/INFORM Global (Alumni Edition) ProQuest Central Basic ProQuest Computing (Alumni Edition) ProQuest One Academic Eastern Edition ABI/INFORM China ProQuest Technology Collection ProQuest SciTech Collection ProQuest Business Collection Computer and Information Systems Abstracts Professional Advanced Technologies & Aerospace Database ProQuest One Academic UKI Edition ProQuest One Business (Alumni) ProQuest One Academic ProQuest Central (Alumni) ProQuest One Academic (New) Business Premium Collection (Alumni) |
| DatabaseTitleList | ABI/INFORM Global (Corporate) |
| Database_xml | – sequence: 1 dbid: BENPR name: ProQuest Central url: https://www.proquest.com/central sourceTypes: Aggregation Database |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Applied Sciences Computer Science |
| EISSN | 1573-1405 |
| EndPage | 2014 |
| ExternalDocumentID | A757583472 10_1007_s11263_023_01791_0 |
| GroupedDBID | -4Z -59 -5G -BR -EM -Y2 -~C .4S .86 .DC .VR 06D 0R~ 0VY 199 1N0 1SB 2.D 203 28- 29J 2J2 2JN 2JY 2KG 2KM 2LR 2P1 2VQ 2~H 30V 3V. 4.4 406 408 409 40D 40E 5GY 5QI 5VS 67Z 6NX 6TJ 78A 7WY 8FE 8FG 8FL 8TC 8UJ 95- 95. 95~ 96X AAAVM AABHQ AACDK AAHNG AAIAL AAJBT AAJKR AANZL AAOBN AARHV AARTL AASML AATNV AATVU AAUYE AAWCG AAYIU AAYQN AAYTO AAYZH ABAKF ABBBX ABBXA ABDBF ABDZT ABECU ABFTD ABFTV ABHLI ABHQN ABJNI ABJOX ABKCH ABKTR ABMNI ABMQK ABNWP ABQBU ABQSL ABSXP ABTEG ABTHY ABTKH ABTMW ABULA ABUWG ABWNU ABXPI ACAOD ACBXY ACDTI ACGFO ACGFS ACHSB ACHXU ACIHN ACKNC ACMDZ ACMLO ACOKC ACOMO ACPIV ACREN ACUHS ACZOJ ADHHG ADHIR ADIMF ADINQ ADKNI ADKPE ADMLS ADRFC ADTPH ADURQ ADYFF ADYOE ADZKW AEAQA AEBTG AEFIE AEFQL AEGAL AEGNC AEJHL AEJRE AEKMD AEMSY AENEX AEOHA AEPYU AESKC AETLH AEVLU AEXYK AFBBN AFEXP AFGCZ AFKRA AFLOW AFQWF AFWTZ AFYQB AFZKB AGAYW AGDGC AGGDS AGJBK AGMZJ AGQEE AGQMX AGRTI AGWIL AGWZB AGYKE AHAVH AHBYD AHKAY AHSBF AHYZX AIAKS AIGIU AIIXL AILAN AITGF AJBLW AJRNO AJZVZ ALMA_UNASSIGNED_HOLDINGS ALWAN AMKLP AMTXH AMXSW AMYLF AMYQR AOCGG ARAPS ARCSS ARMRJ ASPBG AVWKF AXYYD AYJHY AZFZN AZQEC B-. B0M BA0 BBWZM BDATZ BENPR BEZIV BGLVJ BGNMA BPHCQ BSONS CAG CCPQU COF CS3 CSCUP DDRTE DL5 DNIVK DPUIP DU5 DWQXO EAD EAP EAS EBLON EBS EDO EIOEI EJD EMK EPL ESBYG ESX F5P FEDTE FERAY FFXSO FIGPU FINBP FNLPD FRNLG FRRFC FSGXE FWDCC GGCAI GGRSB GJIRD GNUQQ GNWQR GQ6 GQ7 GQ8 GROUPED_ABI_INFORM_COMPLETE GXS H13 HCIFZ HF~ HG5 HG6 HMJXF HQYDN HRMNR HVGLF HZ~ I-F I09 IAO IHE IJ- IKXTQ ISR ITC ITM IWAJR IXC IZIGR IZQ I~X I~Y I~Z J-C J0Z JBSCW JCJTX JZLTJ K60 K6V K6~ K7- KDC KOV KOW LAK LLZTM M0C M0N M4Y MA- N2Q N9A NB0 NDZJH NPVJJ NQJWS NU0 O9- O93 O9G O9I O9J OAM OVD P19 P2P P62 P9O PF0 PQBIZ PQBZA PQQKQ PROAC PT4 PT5 QF4 QM1 QN7 QO4 QOK QOS R4E R89 R9I RHV RNI RNS ROL RPX RSV RZC RZE RZK S16 S1Z S26 S27 S28 S3B SAP SCJ SCLPG SCO SDH SDM SHX SISQX SJYHP SNE SNPRN SNX SOHCF SOJ SPISZ SRMVM SSLCW STPWE SZN T13 T16 TAE TEORI TSG TSK TSV TUC TUS U2A UG4 UOJIU UTJUX UZXMN VC2 VFIZW W23 W48 WK8 YLTOR Z45 Z7R Z7S Z7V Z7W Z7X Z7Y Z7Z Z83 Z86 Z88 Z8M Z8N Z8P Z8Q Z8R Z8S Z8T Z8W Z92 ZMTXR ~8M ~EX AAPKM AAYXX ABBRH ABDBE ABFSG ABRTQ ACSTC ADHKG ADKFA AEZWR AFDZB AFFHD AFHIU AFOHR AGQPQ AHPBZ AHWEU AIXLP ATHPR AYFIA CITATION ICD PHGZM PHGZT PQGLB 7SC 7XB 8AL 8FD 8FK JQ2 L.- L7M L~C L~D PKEHL PQEST PQUKI Q9U |
| ID | FETCH-LOGICAL-c386t-d1644174f04d30c099b99b21df3e29bd940848e894f40ace88b1e30f1f6409e3 |
| IEDL.DBID | M0C |
| ISICitedReferencesCount | 21 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000981840400001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| ISSN | 0920-5691 |
| IngestDate | Wed Nov 05 00:43:06 EST 2025 Sat Nov 29 13:58:52 EST 2025 Sat Nov 29 10:28:18 EST 2025 Sat Nov 29 06:42:29 EST 2025 Tue Nov 18 21:53:30 EST 2025 Fri Feb 21 02:41:59 EST 2025 |
| IsPeerReviewed | true |
| IsScholarly | true |
| Issue | 8 |
| Keywords | Medical image synthesis Self-representation Autoencoder Multimodal collaborative learning |
| Language | English |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c386t-d1644174f04d30c099b99b21df3e29bd940848e894f40ace88b1e30f1f6409e3 |
| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 |
| ORCID | 0000-0002-0316-5404 |
| PQID | 2838509731 |
| PQPubID | 1456341 |
| PageCount | 20 |
| ParticipantIDs | proquest_journals_2838509731 gale_infotracmisc_A757583472 gale_infotracacademiconefile_A757583472 crossref_citationtrail_10_1007_s11263_023_01791_0 crossref_primary_10_1007_s11263_023_01791_0 springer_journals_10_1007_s11263_023_01791_0 |
| PublicationCentury | 2000 |
| PublicationDate | 20230800 2023-08-00 20230801 |
| PublicationDateYYYYMMDD | 2023-08-01 |
| PublicationDate_xml | – month: 8 year: 2023 text: 20230800 |
| PublicationDecade | 2020 |
| PublicationPlace | New York |
| PublicationPlace_xml | – name: New York |
| PublicationTitle | International journal of computer vision |
| PublicationTitleAbbrev | Int J Comput Vis |
| PublicationYear | 2023 |
| Publisher | Springer US Springer Springer Nature B.V |
| Publisher_xml | – name: Springer US – name: Springer – name: Springer Nature B.V |
| References | Huang, Shao, Frangi (CR18) 2017; 37 Kim, Myung (CR27) 2018; 6 Blumberg (CR1) 1920; 27 CR39 CR38 Dice (CR10) 1945; 26 CR35 Zhou, Fu, Chen, Shen, Shao (CR65) 2020; 39 CR33 Park, Zhu, Wang, Lu, Shechtman, Efros, Zhang (CR45) 2020; 33 CR32 Singh, Raza (CR52) 2021 Burgos, Cardoso, Thielemans, Modat, Pedemonte, Dickson, Barnes, Ahmed, Mahoney, Schott, Duncan, Atkinson, Arridge, Hutton, Ourselin (CR3) 2014; 33 CR30 Sauerbrei, Royston (CR50) 1999; 162 Bourlard, Kamp (CR2) 1988; 59 Zhang, Ma (CR62) 2021; 129 Xu, Keshmiri, Wang (CR60) 2019; 21 Ramirez-Manzanares, Rivera (CR48) 2006; 69 Miller, Christensen, Amit, Grenander (CR37) 1993; 90 CR4 CR5 CR8 CR7 CR49 CR47 CR44 CR43 CR42 CR41 Sun, Dong, Li, Wu, Li, Shi (CR53) 2021; 129 CR19 CR16 CR15 Dalmaz, Yurt, Çukur (CR9) 2022; 41 CR14 CR13 Nie, Shen (CR40) 2020; 128 CR57 CR56 CR11 Jiao, Yang, He, Gu, Zhang, Lau (CR22) 2017; 124 CR51 Wang, Zhou, Yu, Wang, Zu, Lalush, Lin, Wu, Zhou, Shen (CR58) 2018; 38 Maier, Menze, Gablentz, Häni, Heinrich, Liebrand, Winzeck, Basit, Bentley, Chen, Christiaens, Dutil, Egger, Feng, Glocker, Götz, Haeck, Halme, Havaei, Reyes (CR34) 2017; 35 Hinton, Salakhutdinov (CR17) 2006; 313 Georgopoulos, Oldfield, Nicolaou, Panagakis, Pantic (CR12) 2021; 129 Yurt, Dar, Erdem, Erdem, Oguz, Çukur (CR61) 2021; 70 Jog, Carass, Roy, Pham, Prince (CR23) 2017; 35 Costa, Galdran, Meyer, Niemeijer, Abràmoff, Mendonça, Campilho (CR6) 2017; 37 Torrado-Carvajal, Herraiz, Alcain, Montemayor, Garcia-Canamaque, Hernandez-Tamames, Rozenholc, Malpica (CR54) 2016; 57 Zhang, Dong, Hu, Lai, Wang, Yang (CR64) 2020; 128 CR29 CR28 Wang, Bovik, Sheikh, Simoncelli (CR59) 2004; 13 CR26 CR25 Zhang, Zhang, Mou, Zhang (CR63) 2011; 20 CR24 CR66 CR21 CR20 Lee, Tseng, Mao, Huang, Lu, Singh, Yang (CR31) 2020; 128 Van Buuren, Boshuizen, Knook (CR55) 1999; 18 Menze, Jakab, Bauer, Kalpathy-Cramer, Farahani (CR36) 2015; 34 Perrin, Fagan, Holtzman (CR46) 2009; 461 1791_CR15 RJ Perrin (1791_CR46) 2009; 461 S Van Buuren (1791_CR55) 1999; 18 1791_CR16 1791_CR11 1791_CR56 1791_CR13 1791_CR57 1791_CR14 1791_CR19 P Costa (1791_CR6) 2017; 37 K Kim (1791_CR27) 2018; 6 Y Wang (1791_CR58) 2018; 38 1791_CR7 1791_CR5 1791_CR4 H Bourlard (1791_CR2) 1988; 59 1791_CR51 LR Dice (1791_CR10) 1945; 26 1791_CR8 1791_CR49 1791_CR44 L Zhang (1791_CR63) 2011; 20 A Torrado-Carvajal (1791_CR54) 2016; 57 1791_CR47 A Ramirez-Manzanares (1791_CR48) 2006; 69 BH Menze (1791_CR36) 2015; 34 O Maier (1791_CR34) 2017; 35 L Sun (1791_CR53) 2021; 129 W Sauerbrei (1791_CR50) 1999; 162 N Burgos (1791_CR3) 2014; 33 M Georgopoulos (1791_CR12) 2021; 129 1791_CR41 M Yurt (1791_CR61) 2021; 70 1791_CR42 1791_CR43 D Nie (1791_CR40) 2020; 128 O Dalmaz (1791_CR9) 2022; 41 1791_CR38 1791_CR39 W Xu (1791_CR60) 2019; 21 1791_CR33 1791_CR35 H Blumberg (1791_CR1) 1920; 27 NK Singh (1791_CR52) 2021 H-Y Lee (1791_CR31) 2020; 128 H Zhang (1791_CR62) 2021; 129 1791_CR30 1791_CR32 T Park (1791_CR45) 2020; 33 GE Hinton (1791_CR17) 2006; 313 1791_CR26 1791_CR28 1791_CR29 T Zhou (1791_CR65) 2020; 39 1791_CR66 1791_CR24 MI Miller (1791_CR37) 1993; 90 1791_CR25 Y Huang (1791_CR18) 2017; 37 J Jiao (1791_CR22) 2017; 124 A Jog (1791_CR23) 2017; 35 X Zhang (1791_CR64) 2020; 128 1791_CR20 1791_CR21 Z Wang (1791_CR59) 2004; 13 |
| References_xml | – volume: 461 start-page: 916 issue: 7266 year: 2009 end-page: 922 ident: CR46 article-title: Multimodal techniques for diagnosis and prognosis of Alzheimer’s disease publication-title: Nature doi: 10.1038/nature08538 – ident: CR49 – volume: 27 start-page: 116 issue: 3 year: 1920 end-page: 129 ident: CR1 article-title: Hausdorff’s grundzüge der mengenlehre publication-title: Bulletin of the American Mathematical Society doi: 10.1090/S0002-9904-1920-03378-1 – volume: 59 start-page: 291 issue: 4 year: 1988 end-page: 294 ident: CR2 article-title: Auto-association by multilayer perceptrons and singular value decomposition publication-title: Biological cybernetics doi: 10.1007/BF00332918 – ident: CR4 – ident: CR39 – ident: CR16 – ident: CR51 – volume: 57 start-page: 136 issue: 1 year: 2016 end-page: 143 ident: CR54 article-title: Fast patch-based pseudo-ct synthesis from t1-weighted mr images for pet/mr attenuation correction in brain studies publication-title: Journal of Nuclear Medicine doi: 10.2967/jnumed.115.156299 – volume: 128 start-page: 1699 issue: 6 year: 2020 end-page: 1721 ident: CR64 article-title: Gated fusion network for degraded image super resolution publication-title: International Journal of Computer Vision doi: 10.1007/s11263-019-01285-y – volume: 129 start-page: 2288 issue: 7 year: 2021 end-page: 2307 ident: CR12 article-title: Mitigating demographic bias in facial datasets with style-based multi-attribute transfer publication-title: International Journal of Computer Vision doi: 10.1007/s11263-021-01448-w – volume: 34 start-page: 1993 issue: 10 year: 2015 end-page: 2024 ident: CR36 article-title: The multimodal brain tumor image segmentation benchmark (brats) publication-title: IEEE Transactions on Medical Imaging doi: 10.1109/TMI.2014.2377694 – ident: CR35 – ident: CR29 – volume: 69 start-page: 77 issue: 1 year: 2006 end-page: 92 ident: CR48 article-title: Basis tensor decomposition for restoring intra-voxel structure and stochastic walks for inferring brain connectivity in dt-mri publication-title: International Journal of Computer Vision doi: 10.1007/s11263-006-6855-7 – ident: CR8 – volume: 70 year: 2021 ident: CR61 article-title: Mustgan: Multi-stream generative adversarial networks for mr image synthesis publication-title: Medical Image Analysis doi: 10.1016/j.media.2020.101944 – ident: CR25 – ident: CR42 – volume: 35 start-page: 250 year: 2017 end-page: 269 ident: CR34 article-title: Isles 2015-a public evaluation benchmark for ischemic stroke lesion segmentation from multispectral mri publication-title: Medical Image Analysis doi: 10.1016/j.media.2016.07.009 – ident: CR21 – volume: 128 start-page: 2402 issue: 10 year: 2020 end-page: 2417 ident: CR31 article-title: Drit++: Diverse image-to-image translation via disentangled representations publication-title: International Journal of Computer Vision doi: 10.1007/s11263-019-01284-z – ident: CR19 – ident: CR15 – ident: CR11 – ident: CR57 – volume: 38 start-page: 1328 issue: 6 year: 2018 end-page: 1339 ident: CR58 article-title: 3d auto-context-based locality adaptive multi-modality gans for pet synthesis publication-title: IEEE Transactions on Medical Imaging doi: 10.1109/TMI.2018.2884053 – volume: 162 start-page: 71 issue: 1 year: 1999 end-page: 94 ident: CR50 article-title: Building multivariable prognostic and diagnostic models: transformation of the predictors by using fractional polynomials publication-title: Journal of the Royal Statistical Society: Series A (Statistics in Society) doi: 10.1111/1467-985X.00122 – ident: CR32 – ident: CR5 – volume: 26 start-page: 297 issue: 3 year: 1945 end-page: 302 ident: CR10 article-title: Measures of the amount of ecologic association between species publication-title: Ecology doi: 10.2307/1932409 – volume: 33 start-page: 7198 year: 2020 end-page: 7211 ident: CR45 article-title: Swapping autoencoder for deep image manipulation publication-title: Advances in Neural Information Processing Systems – ident: CR26 – ident: CR43 – ident: CR66 – ident: CR47 – volume: 129 start-page: 2827 issue: 10 year: 2021 end-page: 2845 ident: CR53 article-title: Deep maximum a posterior estimator for video denoising publication-title: International Journal of Computer Vision doi: 10.1007/s11263-021-01510-7 – volume: 13 start-page: 600 issue: 4 year: 2004 end-page: 612 ident: CR59 article-title: Image quality assessment: from error visibility to structural similarity publication-title: IEEE Transactions on Image Processing doi: 10.1109/TIP.2003.819861 – volume: 41 start-page: 2598 issue: 10 year: 2022 end-page: 2614 ident: CR9 article-title: Resvit: residual vision transformers for multimodal medical image synthesis publication-title: IEEE Transactions on Medical Imaging doi: 10.1109/TMI.2022.3167808 – ident: CR14 – volume: 18 start-page: 681 issue: 6 year: 1999 end-page: 694 ident: CR55 article-title: Multiple imputation of missing blood pressure covariates in survival analysis publication-title: Statistics in Medicine doi: 10.1002/(SICI)1097-0258(19990330)18:6<681::AID-SIM71>3.0.CO;2-R – start-page: 77 year: 2021 end-page: 96 ident: CR52 publication-title: Medical image generation using generative adversarial networks: A review – volume: 33 start-page: 2332 issue: 12 year: 2014 end-page: 2341 ident: CR3 article-title: Attenuation correction synthesis for hybrid pet-mr scanners: Application to brain studies publication-title: IEEE Transactions on Medical Imaging doi: 10.1109/TMI.2014.2340135 – ident: CR30 – ident: CR33 – ident: CR56 – volume: 129 start-page: 2761 issue: 10 year: 2021 end-page: 2785 ident: CR62 article-title: Sdnet: A versatile squeeze-and-decomposition network for real-time image fusion publication-title: International Journal of Computer Vision doi: 10.1007/s11263-021-01501-8 – volume: 37 start-page: 815 issue: 3 year: 2017 end-page: 827 ident: CR18 article-title: Cross-modality image synthesis via weakly coupled and geometry co-regularized joint dictionary learning publication-title: IEEE Transactions on Medical Imaging doi: 10.1109/TMI.2017.2781192 – volume: 35 start-page: 475 year: 2017 end-page: 488 ident: CR23 article-title: Random forest regression for magnetic resonance image synthesis publication-title: Medical Image Analysis doi: 10.1016/j.media.2016.08.009 – volume: 128 start-page: 2494 issue: 10 year: 2020 end-page: 2513 ident: CR40 article-title: Adversarial confidence learning for medical image segmentation and synthesis publication-title: International Journal of Computer Vision doi: 10.1007/s11263-020-01321-2 – ident: CR44 – volume: 39 start-page: 2772 issue: 9 year: 2020 end-page: 2781 ident: CR65 article-title: Hi-net: Hybrid-fusion network for multi-modal mr image synthesis publication-title: IEEE Transactions on Medical Imaging doi: 10.1109/TMI.2020.2975344 – ident: CR38 – ident: CR13 – volume: 313 start-page: 504 issue: 5786 year: 2006 end-page: 507 ident: CR17 article-title: Reducing the dimensionality of data with neural networks publication-title: Science doi: 10.1126/science.1127647 – volume: 6 start-page: 54207 year: 2018 end-page: 54214 ident: CR27 article-title: Autoencoder-combined generative adversarial networks for synthetic image data generation and detection of jellyfish swarm publication-title: IEEE Access doi: 10.1109/ACCESS.2018.2872025 – volume: 20 start-page: 2378 issue: 8 year: 2011 end-page: 2386 ident: CR63 article-title: Fsim: A feature similarity index for image quality assessment publication-title: IEEE Transactions on Image Processing doi: 10.1109/TIP.2011.2109730 – volume: 124 start-page: 204 issue: 2 year: 2017 end-page: 222 ident: CR22 article-title: Joint image denoising and disparity estimation via stereo structure pca and noise-tolerant cost publication-title: International Journal of Computer Vision doi: 10.1007/s11263-017-1015-9 – volume: 37 start-page: 781 issue: 3 year: 2017 end-page: 791 ident: CR6 article-title: End-to-end adversarial retinal image synthesis publication-title: IEEE Transactions on Medical Imaging doi: 10.1109/TMI.2017.2759102 – ident: CR7 – ident: CR28 – ident: CR41 – ident: CR24 – ident: CR20 – volume: 90 start-page: 11944 issue: 24 year: 1993 end-page: 11948 ident: CR37 article-title: Mathematical textbook of deformable neuroanatomies publication-title: Proceedings of the National Academy of Sciences doi: 10.1073/pnas.90.24.11944 – volume: 21 start-page: 2387 issue: 9 year: 2019 end-page: 2396 ident: CR60 article-title: Adversarially approximated autoencoder for image generation and manipulation publication-title: IEEE Transactions on Multimedia doi: 10.1109/TMM.2019.2898777 – ident: 1791_CR4 doi: 10.1609/aaai.v34i07.6619 – ident: 1791_CR35 – ident: 1791_CR43 doi: 10.1007/978-3-030-58545-7_19 – volume: 69 start-page: 77 issue: 1 year: 2006 ident: 1791_CR48 publication-title: International Journal of Computer Vision doi: 10.1007/s11263-006-6855-7 – ident: 1791_CR16 – ident: 1791_CR51 doi: 10.1146/annurev-bioeng-071516-044442 – volume: 38 start-page: 1328 issue: 6 year: 2018 ident: 1791_CR58 publication-title: IEEE Transactions on Medical Imaging doi: 10.1109/TMI.2018.2884053 – volume: 313 start-page: 504 issue: 5786 year: 2006 ident: 1791_CR17 publication-title: Science doi: 10.1126/science.1127647 – volume: 35 start-page: 250 year: 2017 ident: 1791_CR34 publication-title: Medical Image Analysis doi: 10.1016/j.media.2016.07.009 – ident: 1791_CR11 doi: 10.1109/CVPR.2016.265 – ident: 1791_CR57 doi: 10.1109/CVPR.2018.00917 – ident: 1791_CR26 doi: 10.1007/978-3-030-11726-9_4 – volume: 59 start-page: 291 issue: 4 year: 1988 ident: 1791_CR2 publication-title: Biological cybernetics doi: 10.1007/BF00332918 – volume: 129 start-page: 2761 issue: 10 year: 2021 ident: 1791_CR62 publication-title: International Journal of Computer Vision doi: 10.1007/s11263-021-01501-8 – ident: 1791_CR47 doi: 10.1109/IWAIT.2018.8369657 – ident: 1791_CR19 – ident: 1791_CR20 doi: 10.1109/CVPR.2017.632 – volume: 18 start-page: 681 issue: 6 year: 1999 ident: 1791_CR55 publication-title: Statistics in Medicine doi: 10.1002/(SICI)1097-0258(19990330)18:6<681::AID-SIM71>3.0.CO;2-R – volume: 20 start-page: 2378 issue: 8 year: 2011 ident: 1791_CR63 publication-title: IEEE Transactions on Image Processing doi: 10.1109/TIP.2011.2109730 – ident: 1791_CR38 – volume: 124 start-page: 204 issue: 2 year: 2017 ident: 1791_CR22 publication-title: International Journal of Computer Vision doi: 10.1007/s11263-017-1015-9 – volume: 21 start-page: 2387 issue: 9 year: 2019 ident: 1791_CR60 publication-title: IEEE Transactions on Multimedia doi: 10.1109/TMM.2019.2898777 – ident: 1791_CR13 – volume: 33 start-page: 7198 year: 2020 ident: 1791_CR45 publication-title: Advances in Neural Information Processing Systems – ident: 1791_CR25 doi: 10.1109/CVPR.2019.00453 – ident: 1791_CR33 doi: 10.1109/CVPR.2019.00726 – ident: 1791_CR32 doi: 10.1007/978-3-319-10443-0_39 – ident: 1791_CR29 doi: 10.1109/CVPR.2017.19 – volume: 41 start-page: 2598 issue: 10 year: 2022 ident: 1791_CR9 publication-title: IEEE Transactions on Medical Imaging doi: 10.1109/TMI.2022.3167808 – volume: 27 start-page: 116 issue: 3 year: 1920 ident: 1791_CR1 publication-title: Bulletin of the American Mathematical Society doi: 10.1090/S0002-9904-1920-03378-1 – ident: 1791_CR7 – ident: 1791_CR8 doi: 10.1007/978-3-031-18523-6_8 – volume: 128 start-page: 2402 issue: 10 year: 2020 ident: 1791_CR31 publication-title: International Journal of Computer Vision doi: 10.1007/s11263-019-01284-z – start-page: 77 volume-title: Medical image generation using generative adversarial networks: A review year: 2021 ident: 1791_CR52 – volume: 129 start-page: 2827 issue: 10 year: 2021 ident: 1791_CR53 publication-title: International Journal of Computer Vision doi: 10.1007/s11263-021-01510-7 – volume: 90 start-page: 11944 issue: 24 year: 1993 ident: 1791_CR37 publication-title: Proceedings of the National Academy of Sciences doi: 10.1073/pnas.90.24.11944 – volume: 129 start-page: 2288 issue: 7 year: 2021 ident: 1791_CR12 publication-title: International Journal of Computer Vision doi: 10.1007/s11263-021-01448-w – ident: 1791_CR21 doi: 10.1007/978-3-030-58580-8_13 – ident: 1791_CR30 doi: 10.1109/CVPR.2019.00259 – volume: 35 start-page: 475 year: 2017 ident: 1791_CR23 publication-title: Medical Image Analysis doi: 10.1016/j.media.2016.08.009 – volume: 26 start-page: 297 issue: 3 year: 1945 ident: 1791_CR10 publication-title: Ecology doi: 10.2307/1932409 – volume: 461 start-page: 916 issue: 7266 year: 2009 ident: 1791_CR46 publication-title: Nature doi: 10.1038/nature08538 – ident: 1791_CR24 doi: 10.1109/ISBI.2013.6556484 – volume: 162 start-page: 71 issue: 1 year: 1999 ident: 1791_CR50 publication-title: Journal of the Royal Statistical Society: Series A (Statistics in Society) doi: 10.1111/1467-985X.00122 – volume: 128 start-page: 2494 issue: 10 year: 2020 ident: 1791_CR40 publication-title: International Journal of Computer Vision doi: 10.1007/s11263-020-01321-2 – ident: 1791_CR66 doi: 10.1109/ICCV.2017.244 – ident: 1791_CR42 doi: 10.1109/TMI.2023.3290149 – volume: 6 start-page: 54207 year: 2018 ident: 1791_CR27 publication-title: IEEE Access doi: 10.1109/ACCESS.2018.2872025 – ident: 1791_CR44 doi: 10.1109/CVPR.2019.00244 – volume: 33 start-page: 2332 issue: 12 year: 2014 ident: 1791_CR3 publication-title: IEEE Transactions on Medical Imaging doi: 10.1109/TMI.2014.2340135 – volume: 57 start-page: 136 issue: 1 year: 2016 ident: 1791_CR54 publication-title: Journal of Nuclear Medicine doi: 10.2967/jnumed.115.156299 – volume: 13 start-page: 600 issue: 4 year: 2004 ident: 1791_CR59 publication-title: IEEE Transactions on Image Processing doi: 10.1109/TIP.2003.819861 – ident: 1791_CR14 doi: 10.1109/WACV51458.2022.00103 – ident: 1791_CR49 doi: 10.1007/978-3-319-24574-4_28 – volume: 37 start-page: 815 issue: 3 year: 2017 ident: 1791_CR18 publication-title: IEEE Transactions on Medical Imaging doi: 10.1109/TMI.2017.2781192 – ident: 1791_CR15 doi: 10.1109/CVPR.2016.90 – ident: 1791_CR28 doi: 10.18653/v1/D16-1139 – ident: 1791_CR41 doi: 10.1007/978-3-319-66179-7_48 – volume: 70 year: 2021 ident: 1791_CR61 publication-title: Medical Image Analysis doi: 10.1016/j.media.2020.101944 – ident: 1791_CR5 doi: 10.1109/CVPR.2018.00916 – volume: 39 start-page: 2772 issue: 9 year: 2020 ident: 1791_CR65 publication-title: IEEE Transactions on Medical Imaging doi: 10.1109/TMI.2020.2975344 – volume: 37 start-page: 781 issue: 3 year: 2017 ident: 1791_CR6 publication-title: IEEE Transactions on Medical Imaging doi: 10.1109/TMI.2017.2759102 – volume: 34 start-page: 1993 issue: 10 year: 2015 ident: 1791_CR36 publication-title: IEEE Transactions on Medical Imaging doi: 10.1109/TMI.2014.2377694 – volume: 128 start-page: 1699 issue: 6 year: 2020 ident: 1791_CR64 publication-title: International Journal of Computer Vision doi: 10.1007/s11263-019-01285-y – ident: 1791_CR56 – ident: 1791_CR39 |
| SSID | ssj0002823 |
| Score | 2.5366561 |
| Snippet | Multimodal medical images have been widely applied in various clinical diagnoses and treatments. Due to the practical restrictions, certain modalities may be... |
| SourceID | proquest gale crossref springer |
| SourceType | Aggregation Database Enrichment Source Index Database Publisher |
| StartPage | 1995 |
| SubjectTerms | Artificial Intelligence Coders Collaborative learning Computer Imaging Computer Science Computer vision Group work in education Image Processing and Computer Vision Image reconstruction Learning Medical imaging Medical imaging equipment Missing data Pattern Recognition Pattern Recognition and Graphics Source code Synthesis Target masking Team learning approach in education Vision |
| SummonAdditionalLinks | – databaseName: SpringerLINK Contemporary 1997-Present dbid: RSV link: http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwnV3LS8MwGA86PXhxPrE6pQfBgxaaNmuT45gbehnDDdkttE0qA9dJ2w387_2SpSvzBQo9JWka0u_x-_heCF2DBsaS6OQYFjiEiciJGIsdX8ZxW4pAJNLVzSbCwYBOJmxoksKKKtq9cklqSV0nu2FP-xxV_E-o4nW20Q6oO6oaNjyNntfyF4yIVQN5MIzaAcMmVeb7PTbU0Weh_MU7qpVOv_m_4x6gfQMy7c6KKg7RlsyOUNMATtuwcwFDVU-HauwYDTuLct7LVKZ77tznShbaOkl3NhewZbcmm6W0TXHWFxuQr21cPvbjDESUPXrPAFoW0-IEjfu9cffBMV0XnMSnQekIrCBSSFKXCN9NAEHG8HhYpL70WCwYUSX4JWUkJW6USEpjLH03xWkAtqL0T1Ejm2fyTEVNhTINYwEYICawIApTKiiWroAPgJ1lIVzdPU9MRXLVGOOV17WU1SVyuESuL5G7Frpdv_O2qsfx6-ob9Uu5YlbYOYlMzgGcT5W94p0Q0Cr1SehZqLWxEpgs2ZyuiIIbJi84IDPaVuWOsIXuKiKop38-1vnfll-gPU_TkQo7bKFGmS_kJdpNluW0yK808X8A0CL6vA priority: 102 providerName: Springer Nature |
| Title | AutoEncoder-Driven Multimodal Collaborative Learning for Medical Image Synthesis |
| URI | https://link.springer.com/article/10.1007/s11263-023-01791-0 https://www.proquest.com/docview/2838509731 |
| Volume | 131 |
| WOSCitedRecordID | wos000981840400001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| journalDatabaseRights | – providerCode: PRVAVX databaseName: SpringerLINK Contemporary 1997-Present customDbUrl: eissn: 1573-1405 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0002823 issn: 0920-5691 databaseCode: RSV dateStart: 19970101 isFulltext: true titleUrlDefault: https://link.springer.com/search?facet-content-type=%22Journal%22 providerName: Springer Nature |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV3da9swED_Wdg97absvmq0NfhjsYROzbMWSnkaWpmyMhdCWrduLsCV5FFanjdPC_vvdKXJDVtaXgRFYkmXB6U6_k-4D4BXuwNyL4ByjCya0K1mpdcVyX1UD7wpnfRqSTcjJRJ2d6Wk8cGujWWUnE4OgdjNLZ-TvcBtUA4otw99fXjHKGkW3qzGFxgZsEbIhk74v6ehWEqM6sUwljyrSoNA8Os0sXed4Fm4wyZpIkvXP2sb0t3i-c08atp-jnf-d-C5sR-CZDJcr5TE88M0T2IkgNIks3mJVl-ehq3sK0-H1YjZuyPt9zg7nJB-T4Lh7MXM45Gi1lG58EgO2_kwQDSfxGij5dIFiKzn53SDcbM_bZ3B6ND4dfWQxEwOzuSoWzHGCTVLUqXB5ahFVVvhk3NW5z3TltKCw_F5pUYu0tF6pivs8rXldoP7o8-ew2cwav0eWVNLXsnKICyqBHUpZK6e4Tx3-AHWvHvCOCsbGKOWULOOXWcVXJsoZpJwJlDNpD97cfnO5jNFxb-_XRFxDDIwj2zL6IeD8KBSWGUpEsCoXMuvB_lpPZDy73twR3ETGb82K2j142y2ZVfO_p_Xi_tFewqMsLFYyPdyHzcX82h_AQ3uzOG_nfdiQ3773YevDeDI9xrfPkvUDK2A5HfzA8vjk6x_PUApb |
| linkProvider | ProQuest |
| linkToHtml | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMw1V1Lb9QwEB6VggQXylMsFMgBxAEs4sQb2weEVn2oqy2rSuyhNyuxHVSJZstmW9QfxX9kxnG6WhC99YCUU-I4r2--mYnnAfAGNTD3IiTH6IIJ7UpWal2x3FfV0LvCWZ-GZhNyOlXHx_poA371uTAUVtlzYiBqN7f0j_wjqkE1pNoy_PPZD0Zdo2h1tW-h0cFi4i9_osvWfhrv4vd9m2X7e7OdAxa7CjCbq2LJHCcTQIo6FS5PLVpIFW4Zd3XuM105LajEvFda1CItrVeq4j5Pa14X6Av5HKe9BbdFriSJ1USyK-JH76XrXI8e2bDQPObodJl6PAsLphS8JCnYaE0P_qkN_lqWDdpuf-s_e08P4H40q5NRJwcPYcM3j2ArmthJJLAWd_VdLPp9j-FodL6c7zWU279guwti_ySkJZ_OHU65sxKUC5_EcrTfErT1k7jIlYxPkZSTr5cNGtPtSfsEZjfxpE9hs5k3_hnFiUlfy8qh1VMJHFDKWjnFferwAuhZDoD3H93YWIOdWoF8N6vq0QQUg0AxASgmHcD7q3POugok145-R1gyRE84sy1jlgXeHxX6MiOJ9rnKhcwGsL02EmnFrh_u8WUirbVmBa4BfOgRujr879t6fv1sr-HuwezLoTkcTycv4F4W5ISCLLdhc7k49y_hjr1YnrSLV0HiEjA3jNzfLlFfOw |
| linkToPdf | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMw1V3Nb9MwFH8aAyEujE_RsYEPIA4QLU7cxD6gqVpXUQ1Vldhh4mIltjNNYunWdEP70_bf7T3HWVUQu-2AlJPjOE7ye195XwAfUAJzJ3xyjMoioWwRFUqVUerKsu9sZo2LfbOJfDKRR0dqugbXXS4MhVV2PNEzajsz9I98B8Wg7FNtGb5ThbCI6XC0e3YeUQcp8rR27TRaiBy4q99ovjVfx0P81h-TZLR_uPctCh0GIpPKbBFZTupALqpY2DQ2qC2VeCTcVqlLVGmVoHLzTipRibgwTsqSuzSueJWhXeRSXPYBPMzRxKRowmn_560QQEum7WKP1lk_Uzzk67RZezzxzlMKZMop8GhFJv4pGf5y0XrJN9r4j9_ZM3ga1G02aOnjOay5-gVsBNWbBcbW4FDX3aIbewnTwcVitl9Tzv88Gs5JKjCfrnw6s7jk3pKALh0LZWqPGdoALDi_2PgUmTX7cVWjkt2cNK_g8D6e9DWs17PavaH4sdxVeWlRGyoFTijySlrJXWzxBmhx9oB3ANAm1GanFiG_9LKqNIFGI2i0B42Oe_D59pqztjLJnbM_Ea40sS1c2RQh-wL3RwXA9CBHvV2mIk96sLUyE9mNWT3dYU0HdtfoJdB68KVD6_L0v7e1efdq7-ExAlZ_H08O3sKTxJMMxV5uwfpifuG24ZG5XJw083ee-BjoewbuDRk3aF8 |
| 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%3Ajournal&rft.genre=article&rft.atitle=AutoEncoder-Driven+Multimodal+Collaborative+Learning+for+Medical+Image+Synthesis&rft.jtitle=International+journal+of+computer+vision&rft.au=Cao%2C+Bing&rft.au=Bi%2C+Zhiwei&rft.au=Hu%2C+Qinghua&rft.au=Zhang%2C+Han&rft.date=2023-08-01&rft.pub=Springer&rft.issn=0920-5691&rft.volume=131&rft.issue=8&rft.spage=1995&rft_id=info:doi/10.1007%2Fs11263-023-01791-0&rft.externalDocID=A757583472 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=0920-5691&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=0920-5691&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=0920-5691&client=summon |