SELECTOR: Heterogeneous graph network with convolutional masked autoencoder for multimodal robust prediction of cancer survival
Accurately predicting the survival rate of cancer patients is crucial for aiding clinicians in planning appropriate treatment, reducing cancer-related medical expenses, and significantly enhancing patients’ quality of life. Multimodal prediction of cancer patient survival offers a more comprehensive...
Saved in:
| Published in: | Computers in biology and medicine Vol. 172; p. 108301 |
|---|---|
| Main Authors: | , , , , , , , |
| Format: | Journal Article |
| Language: | English |
| Published: |
United States
Elsevier Ltd
01.04.2024
Elsevier Limited |
| Subjects: | |
| ISSN: | 0010-4825, 1879-0534, 1879-0534 |
| Online Access: | Get full text |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Abstract | Accurately predicting the survival rate of cancer patients is crucial for aiding clinicians in planning appropriate treatment, reducing cancer-related medical expenses, and significantly enhancing patients’ quality of life. Multimodal prediction of cancer patient survival offers a more comprehensive and precise approach. However, existing methods still grapple with challenges related to missing multimodal data and information interaction within modalities. This paper introduces SELECTOR, a heterogeneous graph-aware network based on convolutional mask encoders for robust multimodal prediction of cancer patient survival. SELECTOR comprises feature edge reconstruction, convolutional mask encoder, feature cross-fusion, and multimodal survival prediction modules. Initially, we construct a multimodal heterogeneous graph and employ the meta-path method for feature edge reconstruction, ensuring comprehensive incorporation of feature information from graph edges and effective embedding of nodes. To mitigate the impact of missing features within the modality on prediction accuracy, we devised a convolutional masked autoencoder (CMAE) to process the heterogeneous graph post-feature reconstruction. Subsequently, the feature cross-fusion module facilitates communication between modalities, ensuring that output features encompass all features of the modality and relevant information from other modalities. Extensive experiments and analysis on six cancer datasets from TCGA demonstrate that our method significantly outperforms state-of-the-art methods in both modality-missing and intra-modality information-confirmed cases. Our codes are made available at https://github.com/panliangrui/Selector.
[Display omitted]
•Construct a multimodal heterogeneous graph and propose the idea of a meta-path to perform edge reconstruction of features to fully consider the feature information of the edge of the graph and the effective embedding of nodes.•We propose a convolutional masked autoencoder (CMAE) to process the heterogeneous image after feature reconstruction. It mainly relies on the idea of sparse convolution in feature extraction. Only unmasked features are extracted.•A feature cross-communication module is designed to establish communication between multiple modalities, so that the output features include all features of the modality as well as relevant information of other modalities.•SELECTOR can effectively predict cancer patient survival in the presence of both within-modality and missing-modality data. Through extensive experimental verification, we found that this framework achieved the best prediction accuracy on six cancer multimodal datasets of TCGA. |
|---|---|
| AbstractList | Accurately predicting the survival rate of cancer patients is crucial for aiding clinicians in planning appropriate treatment, reducing cancer-related medical expenses, and significantly enhancing patients' quality of life. Multimodal prediction of cancer patient survival offers a more comprehensive and precise approach. However, existing methods still grapple with challenges related to missing multimodal data and information interaction within modalities. This paper introduces SELECTOR, a heterogeneous graph-aware network based on convolutional mask encoders for robust multimodal prediction of cancer patient survival. SELECTOR comprises feature edge reconstruction, convolutional mask encoder, feature cross-fusion, and multimodal survival prediction modules. Initially, we construct a multimodal heterogeneous graph and employ the meta-path method for feature edge reconstruction, ensuring comprehensive incorporation of feature information from graph edges and effective embedding of nodes. To mitigate the impact of missing features within the modality on prediction accuracy, we devised a convolutional masked autoencoder (CMAE) to process the heterogeneous graph post-feature reconstruction. Subsequently, the feature cross-fusion module facilitates communication between modalities, ensuring that output features encompass all features of the modality and relevant information from other modalities. Extensive experiments and analysis on six cancer datasets from TCGA demonstrate that our method significantly outperforms state-of-the-art methods in both modality-missing and intra-modality information-confirmed cases. Our codes are made available at https://github.com/panliangrui/Selector.Accurately predicting the survival rate of cancer patients is crucial for aiding clinicians in planning appropriate treatment, reducing cancer-related medical expenses, and significantly enhancing patients' quality of life. Multimodal prediction of cancer patient survival offers a more comprehensive and precise approach. However, existing methods still grapple with challenges related to missing multimodal data and information interaction within modalities. This paper introduces SELECTOR, a heterogeneous graph-aware network based on convolutional mask encoders for robust multimodal prediction of cancer patient survival. SELECTOR comprises feature edge reconstruction, convolutional mask encoder, feature cross-fusion, and multimodal survival prediction modules. Initially, we construct a multimodal heterogeneous graph and employ the meta-path method for feature edge reconstruction, ensuring comprehensive incorporation of feature information from graph edges and effective embedding of nodes. To mitigate the impact of missing features within the modality on prediction accuracy, we devised a convolutional masked autoencoder (CMAE) to process the heterogeneous graph post-feature reconstruction. Subsequently, the feature cross-fusion module facilitates communication between modalities, ensuring that output features encompass all features of the modality and relevant information from other modalities. Extensive experiments and analysis on six cancer datasets from TCGA demonstrate that our method significantly outperforms state-of-the-art methods in both modality-missing and intra-modality information-confirmed cases. Our codes are made available at https://github.com/panliangrui/Selector. Accurately predicting the survival rate of cancer patients is crucial for aiding clinicians in planning appropriate treatment, reducing cancer-related medical expenses, and significantly enhancing patients' quality of life. Multimodal prediction of cancer patient survival offers a more comprehensive and precise approach. However, existing methods still grapple with challenges related to missing multimodal data and information interaction within modalities. This paper introduces SELECTOR, a heterogeneous graph-aware network based on convolutional mask encoders for robust multimodal prediction of cancer patient survival. SELECTOR comprises feature edge reconstruction, convolutional mask encoder, feature cross-fusion, and multimodal survival prediction modules. Initially, we construct a multimodal heterogeneous graph and employ the meta-path method for feature edge reconstruction, ensuring comprehensive incorporation of feature information from graph edges and effective embedding of nodes. To mitigate the impact of missing features within the modality on prediction accuracy, we devised a convolutional masked autoencoder (CMAE) to process the heterogeneous graph post-feature reconstruction. Subsequently, the feature cross-fusion module facilitates communication between modalities, ensuring that output features encompass all features of the modality and relevant information from other modalities. Extensive experiments and analysis on six cancer datasets from TCGA demonstrate that our method significantly outperforms state-of-the-art methods in both modality-missing and intra-modality information-confirmed cases. Our codes are made available at https://github.com/panliangrui/Selector. Accurately predicting the survival rate of cancer patients is crucial for aiding clinicians in planning appropriate treatment, reducing cancer-related medical expenses, and significantly enhancing patients’ quality of life. Multimodal prediction of cancer patient survival offers a more comprehensive and precise approach. However, existing methods still grapple with challenges related to missing multimodal data and information interaction within modalities. This paper introduces SELECTOR, a heterogeneous graph-aware network based on convolutional mask encoders for robust multimodal prediction of cancer patient survival. SELECTOR comprises feature edge reconstruction, convolutional mask encoder, feature cross-fusion, and multimodal survival prediction modules. Initially, we construct a multimodal heterogeneous graph and employ the meta-path method for feature edge reconstruction, ensuring comprehensive incorporation of feature information from graph edges and effective embedding of nodes. To mitigate the impact of missing features within the modality on prediction accuracy, we devised a convolutional masked autoencoder (CMAE) to process the heterogeneous graph post-feature reconstruction. Subsequently, the feature cross-fusion module facilitates communication between modalities, ensuring that output features encompass all features of the modality and relevant information from other modalities. Extensive experiments and analysis on six cancer datasets from TCGA demonstrate that our method significantly outperforms state-of-the-art methods in both modality-missing and intra-modality information-confirmed cases. Our codes are made available at https://github.com/panliangrui/Selector. [Display omitted] •Construct a multimodal heterogeneous graph and propose the idea of a meta-path to perform edge reconstruction of features to fully consider the feature information of the edge of the graph and the effective embedding of nodes.•We propose a convolutional masked autoencoder (CMAE) to process the heterogeneous image after feature reconstruction. It mainly relies on the idea of sparse convolution in feature extraction. Only unmasked features are extracted.•A feature cross-communication module is designed to establish communication between multiple modalities, so that the output features include all features of the modality as well as relevant information of other modalities.•SELECTOR can effectively predict cancer patient survival in the presence of both within-modality and missing-modality data. Through extensive experimental verification, we found that this framework achieved the best prediction accuracy on six cancer multimodal datasets of TCGA. AbstractAccurately predicting the survival rate of cancer patients is crucial for aiding clinicians in planning appropriate treatment, reducing cancer-related medical expenses, and significantly enhancing patients’ quality of life. Multimodal prediction of cancer patient survival offers a more comprehensive and precise approach. However, existing methods still grapple with challenges related to missing multimodal data and information interaction within modalities. This paper introduces SELECTOR, a heterogeneous graph-aware network based on convolutional mask encoders for robust multimodal prediction of cancer patient survival. SELECTOR comprises feature edge reconstruction, convolutional mask encoder, feature cross-fusion, and multimodal survival prediction modules. Initially, we construct a multimodal heterogeneous graph and employ the meta-path method for feature edge reconstruction, ensuring comprehensive incorporation of feature information from graph edges and effective embedding of nodes. To mitigate the impact of missing features within the modality on prediction accuracy, we devised a convolutional masked autoencoder (CMAE) to process the heterogeneous graph post-feature reconstruction. Subsequently, the feature cross-fusion module facilitates communication between modalities, ensuring that output features encompass all features of the modality and relevant information from other modalities. Extensive experiments and analysis on six cancer datasets from TCGA demonstrate that our method significantly outperforms state-of-the-art methods in both modality-missing and intra-modality information-confirmed cases. Our codes are made available at https://github.com/panliangrui/Selector. |
| ArticleNumber | 108301 |
| Author | Peng, Shaoliang Wang, Xiang Liang, Qingchun Peng, Yijun Li, Yan Liu, Wenjuan Pan, Liangrui Xu, Liwen |
| Author_xml | – sequence: 1 givenname: Liangrui orcidid: 0000-0003-0565-4217 surname: Pan fullname: Pan, Liangrui email: panlr@hnu.edu.cn organization: College of Computer Science and Electronic Engineering, Hunan University, Changsha, 410083, Hunan, China – sequence: 2 givenname: Yijun surname: Peng fullname: Peng, Yijun email: pengyijun@hnu.edu.cn organization: College of Computer Science and Electronic Engineering, Hunan University, Changsha, 410083, Hunan, China – sequence: 3 givenname: Yan surname: Li fullname: Li, Yan email: s2310w1062@hnu.edu.cn organization: College of Computer Science and Electronic Engineering, Hunan University, Changsha, 410083, Hunan, China – sequence: 4 givenname: Xiang surname: Wang fullname: Wang, Xiang email: wangxiang@csu.edu.cn organization: Department of Thoracic Surgery, The second xiangya hospital, Central South University, Changsha, 410011, Hunan, China – sequence: 5 givenname: Wenjuan surname: Liu fullname: Liu, Wenjuan email: liuwenjuan89@hnu.edu organization: College of Computer Science and Electronic Engineering, Hunan University, Changsha, 410083, Hunan, China – sequence: 6 givenname: Liwen surname: Xu fullname: Xu, Liwen email: xuliwen@hnu.edu.cn organization: College of Computer Science and Electronic Engineering, Hunan University, Changsha, 410083, Hunan, China – sequence: 7 givenname: Qingchun surname: Liang fullname: Liang, Qingchun email: 503079@csu.edu.cn organization: Department of Pathology, The second xiangya hospital, Central South University, Changsha, 410011, Hunan, China – sequence: 8 givenname: Shaoliang surname: Peng fullname: Peng, Shaoliang email: slpeng@hnu.edu.cn organization: College of Computer Science and Electronic Engineering, Hunan University, Changsha, 410083, Hunan, China |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/38492453$$D View this record in MEDLINE/PubMed |
| BookMark | eNqNks1qGzEUhYeS0jhpX6EIuulmXP2NLXURmhq3KRgCTboWGulOIntm5Eoah6z66tXUSQuGglcC8d0jnXPuWXHS-x6KAhE8JZjMPqynxnfb2vkO7JRiyvO1YJi8KCZEzGWJK8ZPignGBJdc0Oq0OItxjTHmmOFXxSkTXFJesUnx62a5Wi5ur79_RFeQIPg76MEPEd0Fvb1HPaQHHzbowaV7ZHy_8-2QnO91izodN2CRHpKH3ngLATU-oG5ok-u8zUTw9RAT2gawzoxTyDfI6N5kNA5h53a6fV28bHQb4c3TeV78-LK8XVyVq-uv3xaXq9JwKlPZSClA2IZoqi1rNDNcWGEo1NmRnPEZzpbqSjdMa2nmla6MZrW1jM0knmHDzov3e91t8D8HiEl1LhpoW_3HrqKyElSy-Vxk9N0BuvZDyJZHas44kZjQTL19ooY6t6C2wXU6PKrnaDNwsQdM8DEGaJRxSY8xpKBdqwhWY5dqrf51qcYu1b7LLCAOBJ7fOGL0834UcqQ7B0FF43JLuYgAJinr3TEiFwcipnW9M7rdwCPEv6EQFanC6mbctnHZKMe5jWoM8tP_BY77w28ulOvl |
| CitedBy_id | crossref_primary_10_1016_j_bspc_2025_108640 crossref_primary_10_1016_j_media_2024_103444 crossref_primary_10_1016_j_engappai_2024_109972 crossref_primary_10_1016_j_patcog_2025_111991 crossref_primary_10_12677_mos_2025_148562 |
| Cites_doi | 10.1158/1078-0432.CCR-23-2591 10.1609/aaai.v37i8.26192 10.1002/cac2.12328 10.1145/1390156.1390294 10.1109/CVPR42600.2020.00380 10.1038/s41598-021-92799-4 10.1038/s42256-023-00633-5 10.7150/ijbs.45050 10.1109/TBME.2020.2993278 10.1093/bioinformatics/btab185 10.1109/CVPR.2018.00961 10.1016/j.compbiomed.2015.02.006 10.1016/j.ygeno.2008.09.007 10.1093/bioinformatics/btac113 10.1093/bioinformatics/btad025 10.1109/TMI.2023.3253760 10.1038/s41698-022-00285-5 10.1109/TMI.2020.3021387 10.1038/s41596-018-0103-9 10.1609/aaai.v35i5.16600 10.1145/3292500.3330961 10.1109/TIP.2021.3139229 10.1186/s13073-021-00968-x 10.1109/CVPR52729.2023.02323 10.1093/bioinformatics/btab675 10.1016/j.bspc.2022.103824 10.1245/s10434-014-4281-6 10.1016/j.compbiomed.2014.02.006 10.1016/j.compbiomed.2023.106576 10.1038/s41551-020-00682-w 10.1200/JCO.2002.20.3.791 10.1016/j.ajog.2018.12.030 10.1016/j.media.2020.101789 10.1073/pnas.1717139115 10.1093/bioinformatics/btz342 10.5114/wo.2014.47136 10.1109/CVPR52729.2023.00213 10.1016/j.eswa.2022.117114 10.1016/j.ccell.2022.07.004 10.1016/j.ins.2021.06.058 10.1145/3214306 10.1002/sim.2174 10.1016/j.media.2021.102032 |
| ContentType | Journal Article |
| Copyright | 2024 Elsevier Ltd Elsevier Ltd Copyright © 2024 Elsevier Ltd. All rights reserved. 2024. Elsevier Ltd |
| Copyright_xml | – notice: 2024 Elsevier Ltd – notice: Elsevier Ltd – notice: Copyright © 2024 Elsevier Ltd. All rights reserved. – notice: 2024. Elsevier Ltd |
| DBID | AAYXX CITATION CGR CUY CVF ECM EIF NPM 8FD FR3 JQ2 K9. M7Z NAPCQ P64 7X8 |
| DOI | 10.1016/j.compbiomed.2024.108301 |
| DatabaseName | CrossRef Medline MEDLINE MEDLINE (Ovid) MEDLINE MEDLINE PubMed Technology Research Database Engineering Research Database ProQuest Computer Science Collection ProQuest Health & Medical Complete (Alumni) Biochemistry Abstracts 1 Nursing & Allied Health Premium Biotechnology and BioEngineering Abstracts MEDLINE - Academic |
| DatabaseTitle | CrossRef MEDLINE Medline Complete MEDLINE with Full Text PubMed MEDLINE (Ovid) Nursing & Allied Health Premium Technology Research Database ProQuest Computer Science Collection Biochemistry Abstracts 1 ProQuest Health & Medical Complete (Alumni) Engineering Research Database Biotechnology and BioEngineering Abstracts MEDLINE - Academic |
| DatabaseTitleList | MEDLINE - Academic MEDLINE Nursing & Allied Health Premium |
| Database_xml | – sequence: 1 dbid: NPM name: PubMed url: http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed sourceTypes: Index Database – sequence: 2 dbid: 7X8 name: MEDLINE - Academic url: https://search.proquest.com/medline sourceTypes: Aggregation Database |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Medicine |
| EISSN | 1879-0534 |
| EndPage | 108301 |
| ExternalDocumentID | 38492453 10_1016_j_compbiomed_2024_108301 S0010482524003858 1_s2_0_S0010482524003858 |
| Genre | Research Support, Non-U.S. Gov't Journal Article |
| GroupedDBID | --- --K --M --Z -~X .1- .55 .DC .FO .GJ .~1 0R~ 1B1 1P~ 1RT 1~. 1~5 29F 4.4 457 4G. 53G 5GY 5VS 7-5 71M 77I 7RV 7X7 88E 8AO 8FE 8FG 8FH 8FI 8FJ 8G5 8P~ 9JN AAEDT AAEDW AAIKJ AAKOC AALRI AAOAW AAQFI AAQXK AATTM AAXKI AAXUO AAYFN AAYWO ABBOA ABFNM ABJNI ABMAC ABMZM ABOCM ABUWG ABWVN ABXDB ACDAQ ACGFS ACIEU ACIUM ACIWK ACLOT ACNNM ACPRK ACRLP ACRPL ACVFH ACZNC ADBBV ADCNI ADEZE ADJOM ADMUD ADNMO AEBSH AEIPS AEKER AENEX AEUPX AEVXI AFJKZ AFKRA AFPUW AFRAH AFRHN AFTJW AFXIZ AGHFR AGQPQ AGUBO AGYEJ AHHHB AHMBA AHZHX AIALX AIEXJ AIGII AIIUN AIKHN AITUG AJRQY AJUYK AKBMS AKRWK AKYEP ALMA_UNASSIGNED_HOLDINGS AMRAJ ANKPU ANZVX AOUOD APXCP ARAPS ASPBG AVWKF AXJTR AZFZN AZQEC BBNVY BENPR BGLVJ BHPHI BKEYQ BKOJK BLXMC BNPGV BPHCQ BVXVI CCPQU CS3 DU5 DWQXO EBS EFJIC EFKBS EFLBG EJD EMOBN EO8 EO9 EP2 EP3 EX3 F5P FDB FEDTE FGOYB FIRID FNPLU FYGXN FYUFA G-2 G-Q GBLVA GBOLZ GNUQQ GUQSH HCIFZ HLZ HMCUK HMK HMO HVGLF HZ~ IHE J1W K6V K7- KOM LK8 LX9 M1P M29 M2O M41 M7P MO0 N9A NAPCQ O-L O9- OAUVE OZT P-8 P-9 P2P P62 PC. PHGZM PHGZT PJZUB PPXIY PQGLB PQQKQ PROAC PSQYO Q38 R2- ROL RPZ RXW SAE SBC SCC SDF SDG SDP SEL SES SEW SPC SPCBC SSH SSV SSZ SV3 T5K TAE UAP UKHRP WOW WUQ X7M XPP Z5R ZGI ~G- ~HD 3V. AACTN AFCTW AFKWA AJOXV ALIPV AMFUW M0N RIG 9DU AAYXX AFFHD CITATION CGR CUY CVF ECM EIF NPM 8FD FR3 JQ2 K9. M7Z P64 7X8 |
| ID | FETCH-LOGICAL-c429t-f998e8df1a2ad3fa3c48d8c2eb40396460384b5af3aa9c75a5ca3bdd3369060c3 |
| ISSN | 0010-4825 1879-0534 |
| IngestDate | Sun Sep 28 08:43:32 EDT 2025 Tue Oct 07 06:15:39 EDT 2025 Thu Apr 03 07:00:10 EDT 2025 Tue Nov 18 21:49:52 EST 2025 Sat Nov 29 05:31:46 EST 2025 Sat Oct 12 15:53:11 EDT 2024 Tue Feb 25 20:08:39 EST 2025 Tue Oct 14 19:37:57 EDT 2025 |
| IsPeerReviewed | true |
| IsScholarly | true |
| Keywords | Missing Heterogeneous graph Multimodal Robust Convolutional mask encoder |
| Language | English |
| License | Copyright © 2024 Elsevier Ltd. All rights reserved. |
| LinkModel | OpenURL |
| MergedId | FETCHMERGED-LOGICAL-c429t-f998e8df1a2ad3fa3c48d8c2eb40396460384b5af3aa9c75a5ca3bdd3369060c3 |
| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23 |
| ORCID | 0000-0003-0565-4217 |
| PMID | 38492453 |
| PQID | 2973419012 |
| PQPubID | 1226355 |
| PageCount | 1 |
| ParticipantIDs | proquest_miscellaneous_2958293778 proquest_journals_2973419012 pubmed_primary_38492453 crossref_citationtrail_10_1016_j_compbiomed_2024_108301 crossref_primary_10_1016_j_compbiomed_2024_108301 elsevier_sciencedirect_doi_10_1016_j_compbiomed_2024_108301 elsevier_clinicalkeyesjournals_1_s2_0_S0010482524003858 elsevier_clinicalkey_doi_10_1016_j_compbiomed_2024_108301 |
| PublicationCentury | 2000 |
| PublicationDate | 2024-04-01 |
| PublicationDateYYYYMMDD | 2024-04-01 |
| PublicationDate_xml | – month: 04 year: 2024 text: 2024-04-01 day: 01 |
| PublicationDecade | 2020 |
| PublicationPlace | United States |
| PublicationPlace_xml | – name: United States – name: Oxford |
| PublicationTitle | Computers in biology and medicine |
| PublicationTitleAlternate | Comput Biol Med |
| PublicationYear | 2024 |
| Publisher | Elsevier Ltd Elsevier Limited |
| Publisher_xml | – name: Elsevier Ltd – name: Elsevier Limited |
| References | Li, Wu, Li, Wang (b19) 2022; 38 Ma, Yan, Chai, Hou (b52) 2022; 38 Deng, Zeng, Luo, Kong, Shi, Li, Zhang (b7) 2021; 576 Liang, Wang, Lin, Chen, Yang, Zheng, Li (b13) 2020; 16 Lu, Williamson, Chen, Chen, Barbieri, Mahmood (b21) 2021; 5 Xie, Gu, Guo, Qi, Guibas, Litany (b45) 2020 Thorp (b38) 2023 Chen, Lu, Williamson, Chen, Lipkova, Noor, Shaban, Shady, Williams, Joo (b22) 2022; 40 Wang, Zeng, Chen, Zhao, Chen (b60) 2022; 202 Yao, Zhu, Jonnagaddala, Hawkins, Huang (b9) 2020; 65 Shao, Bian, Chen, Wang, Zhang, Ji (b27) 2021; 34 Tomczak, Czerwińska, Wiznerowicz (b47) 2015; 2015 Matsuo, Purushotham, Jiang, Mandelbaum, Takiuchi, Liu, Roman (b51) 2019; 220 García-Laencina, Abreu, Abreu, Afonoso (b4) 2015; 59 Xie, Liu (b54) 2005; 24 Benovoy, Drouin (b58) 2009; 93 Reimand, Isserlin, Voisin, Kucera, Tannus-Lopes, Rostamianfar, Wadi, Meyer, Wong, Xu (b48) 2019; 14 Cheerla, Gevaert (b31) 2019; 35 Pan, Liu, Feng, Liu, Peng (b6) 2023 Vale-Silva, Rohr (b30) 2021; 11 Wang, Li, Reddy (b50) 2019; 51 Wu, Shi, Wang, Li (b17) 2023; 39 Sanghyun Woo, Shoubhik Debnath, Ronghang Hu, Xinlei Chen, Zhuang Liu, In So Kweon, Saining Xie, Convnext v2: Co-designing and scaling convnets with masked autoencoders, in: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2023, pp. 16133–16142. Gao, Ma, Li, Lin, Dai, Qiao (b61) 2022 Pan, Dou, Feng, Xu, Peng (b12) 2023 Liu, Fu, Yang, Deng, Zhong, Zheng (b14) 2020; 68 Xin Lin, Changxing Ding, Jinquan Zeng, Dacheng Tao, Tao Gps-net: Graph property sensing network for scene graph generation, in: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2020, pp. 3746–3753. Chen, Ke, Chiu (b15) 2014; 48 Wang, Li, Wang, Li (b18) 2021; 37 Jaume, Vaidya, Chen, Williamson, Liang, Mahmood (b24) 2023 Islam, Seenivasan, Ming, Ren (b55) 2020 QuanLin Wu, Hang Ye, Yuntian Gu, Huishuai Zhang, Liwei Wang, Di He, Denoising masked autoencoders help robust classification, in: The Eleventh International Conference on Learning Representations, 2022. Christopher Choy, JunYoung Gwak, Silvio Savarese, 4d spatio-temporal convnets: Minkowski convolutional neural networks, in: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2019, pp. 3075–3084. Mobadersany, Yousefi, Amgad, Gutman, Barnholtz-Sloan, Velázquez Vega, Brat, Cooper (b29) 2018; 115 Yutong Bai, Zeyu Wang, Junfei Xiao, Chen Wei, Huiyu Wang, Alan L. Yuille, Yuyin Zhou, Cihang Xie, Masked autoencoders enable efficient knowledge distillers, in: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2023, pp. 24256–24265. Fu, Patrick, Yang, Feng, Kim (b20) 2023; 154 Kaiming He, Xinlei Chen, Saining Xie, Yanghao Li, Piotr Dollár, Ross Girshick, Masked autoencoders are scalable vision learners, in: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2022, pp. 16000–16009. Tianhong Li, Huiwen Chang, Shlok Mishra, Han Zhang, Dina Katabi, Dilip Krishnan, Mage: Masked generative encoder to unify representation learning and image synthesis, in: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2023, pp. 2142–2152. Riasatian, Babaie, Maleki, Kalra, Valipour, Hemati, Zaveri, Safarpoor, Shafiei, Afshari (b43) 2021; 70 Steyaert, Pizurica, Nagaraj, Khandelwal, Hernandez-Boussard, Gentles, Gevaert (b16) 2023; 5 Di, Zhang, Lei, Tian, Gao (b10) 2022; 31 Yijun Tian, Kaiwen Dong, Chunhui Zhang, Chuxu Zhang, Nitesh V. Chawla, Heterogeneous graph masked autoencoders, in: Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 37, 2023, pp. 9997–10005. Pan, Liu, Dou, Wang, Feng, Rong, Xu, Peng (b3) 2023 Richard J. Chen, Ming Y. Lu, Wei-Hung Weng, Tiffany Y. Chen, Drew F.K. Williamson, Trevor Manz, Maha Shady, Faisal Mahmood, Multimodal co-attention transformer for survival prediction in gigapixel whole slide images, in: Proceedings of the IEEE/CVF International Conference on Computer Vision, 2021, pp. 4015–4025. Jianan Zhao, Xiao Wang, Chuan Shi, Binbin Hu, Guojie Song, Yanfang Ye, Heterogeneous graph structure learning for graph neural networks, in: Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 35, 2021, pp. 4697–4705. Huang, Jin, Lu, Hou, Cheng, Fu, Shen, Feng (b41) 2023 Williams, Zaidi, Sengupta (b1) 2023; 29 Kattan, Leung, Brennan (b49) 2002; 20 Brenner, Schöttker, Niedermaier (b2) 2022; 42 Benjamin Graham, Martin Engelcke, Laurens Van Der Maaten, 3d semantic segmentation with submanifold sparse convolutional networks, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2018, pp. 9224–9232. Wang, Wang, Hu, Li, Fan, Otter, Sam, Gou, Hu, Kwok (b57) 2022; 6 Pan, Wang, Wang, Ji, Liu, Chongcheawchamnan, Yuan, Peng (b11) 2022; 77 Qiankun Liu, Zhentao Tan, Dongdong Chen, Qi Chu, Xiyang Dai, Yinpeng Chen, Mengchen Liu, Lu Yuan, Nenghai Yu, Reduce information loss in transformers for pluralistic image inpainting, in: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2022, pp. 11347–11357. Hou, Lin, Yu, Qin, Yu, Wang (b23) 2023 Ilse, Tomczak, Welling (b25) 2018 Chuxu Zhang, Dongjin Song, Chao Huang, Ananthram Swami, Nitesh V. Chawla, Heterogeneous graph neural network, in: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, 2019, pp. 793–803. Li, Zhou, Dvornek, Zhang, Zhuang, Ventola, Duncan (b56) 2020 He, Liu, Zuo, Shi, Jing (b8) 2023; Vol. 88 Tran, Kondrashova, Bradley, Williams, Pearson, Waddell (b5) 2021; 13 Miyamoto, Baba, Sakamoto, Ohuchi, Tokunaga, Kurashige, Hiyoshi, Iwagami, Yoshida, Yoshida (b53) 2015; 22 Pascal Vincent, Hugo Larochelle, Yoshua Bengio, Pierre-Antoine Manzagol, Extracting and composing robust features with denoising autoencoders, in: Proceedings of the 25th International Conference on Machine Learning, 2008, pp. 1096–1103. Chen, Lu, Wang, Williamson, Rodig, Lindeman, Mahmood (b28) 2020; 41 Xie (10.1016/j.compbiomed.2024.108301_b54) 2005; 24 Fu (10.1016/j.compbiomed.2024.108301_b20) 2023; 154 10.1016/j.compbiomed.2024.108301_b62 Jaume (10.1016/j.compbiomed.2024.108301_b24) 2023 Cheerla (10.1016/j.compbiomed.2024.108301_b31) 2019; 35 García-Laencina (10.1016/j.compbiomed.2024.108301_b4) 2015; 59 Li (10.1016/j.compbiomed.2024.108301_b56) 2020 Xie (10.1016/j.compbiomed.2024.108301_b45) 2020 Liang (10.1016/j.compbiomed.2024.108301_b13) 2020; 16 10.1016/j.compbiomed.2024.108301_b26 Brenner (10.1016/j.compbiomed.2024.108301_b2) 2022; 42 Benovoy (10.1016/j.compbiomed.2024.108301_b58) 2009; 93 He (10.1016/j.compbiomed.2024.108301_b8) 2023; Vol. 88 Pan (10.1016/j.compbiomed.2024.108301_b6) 2023 Wang (10.1016/j.compbiomed.2024.108301_b50) 2019; 51 Chen (10.1016/j.compbiomed.2024.108301_b15) 2014; 48 Huang (10.1016/j.compbiomed.2024.108301_b41) 2023 Thorp (10.1016/j.compbiomed.2024.108301_b38) 2023 Vale-Silva (10.1016/j.compbiomed.2024.108301_b30) 2021; 11 10.1016/j.compbiomed.2024.108301_b59 Hou (10.1016/j.compbiomed.2024.108301_b23) 2023 10.1016/j.compbiomed.2024.108301_b42 10.1016/j.compbiomed.2024.108301_b40 Li (10.1016/j.compbiomed.2024.108301_b19) 2022; 38 Wang (10.1016/j.compbiomed.2024.108301_b18) 2021; 37 Kattan (10.1016/j.compbiomed.2024.108301_b49) 2002; 20 Pan (10.1016/j.compbiomed.2024.108301_b3) 2023 Gao (10.1016/j.compbiomed.2024.108301_b61) 2022 Ma (10.1016/j.compbiomed.2024.108301_b52) 2022; 38 Wang (10.1016/j.compbiomed.2024.108301_b57) 2022; 6 10.1016/j.compbiomed.2024.108301_b46 Lu (10.1016/j.compbiomed.2024.108301_b21) 2021; 5 Tran (10.1016/j.compbiomed.2024.108301_b5) 2021; 13 10.1016/j.compbiomed.2024.108301_b44 Yao (10.1016/j.compbiomed.2024.108301_b9) 2020; 65 Chen (10.1016/j.compbiomed.2024.108301_b28) 2020; 41 Ilse (10.1016/j.compbiomed.2024.108301_b25) 2018 Deng (10.1016/j.compbiomed.2024.108301_b7) 2021; 576 Chen (10.1016/j.compbiomed.2024.108301_b22) 2022; 40 Mobadersany (10.1016/j.compbiomed.2024.108301_b29) 2018; 115 Tomczak (10.1016/j.compbiomed.2024.108301_b47) 2015; 2015 Reimand (10.1016/j.compbiomed.2024.108301_b48) 2019; 14 Wu (10.1016/j.compbiomed.2024.108301_b17) 2023; 39 Shao (10.1016/j.compbiomed.2024.108301_b27) 2021; 34 Riasatian (10.1016/j.compbiomed.2024.108301_b43) 2021; 70 Williams (10.1016/j.compbiomed.2024.108301_b1) 2023; 29 Di (10.1016/j.compbiomed.2024.108301_b10) 2022; 31 Steyaert (10.1016/j.compbiomed.2024.108301_b16) 2023; 5 Miyamoto (10.1016/j.compbiomed.2024.108301_b53) 2015; 22 Pan (10.1016/j.compbiomed.2024.108301_b11) 2022; 77 Pan (10.1016/j.compbiomed.2024.108301_b12) 2023 Matsuo (10.1016/j.compbiomed.2024.108301_b51) 2019; 220 Islam (10.1016/j.compbiomed.2024.108301_b55) 2020 Liu (10.1016/j.compbiomed.2024.108301_b14) 2020; 68 10.1016/j.compbiomed.2024.108301_b35 10.1016/j.compbiomed.2024.108301_b34 10.1016/j.compbiomed.2024.108301_b33 10.1016/j.compbiomed.2024.108301_b32 10.1016/j.compbiomed.2024.108301_b39 10.1016/j.compbiomed.2024.108301_b37 Wang (10.1016/j.compbiomed.2024.108301_b60) 2022; 202 10.1016/j.compbiomed.2024.108301_b36 |
| References_xml | – reference: Pascal Vincent, Hugo Larochelle, Yoshua Bengio, Pierre-Antoine Manzagol, Extracting and composing robust features with denoising autoencoders, in: Proceedings of the 25th International Conference on Machine Learning, 2008, pp. 1096–1103. – reference: QuanLin Wu, Hang Ye, Yuntian Gu, Huishuai Zhang, Liwei Wang, Di He, Denoising masked autoencoders help robust classification, in: The Eleventh International Conference on Learning Representations, 2022. – volume: 11 start-page: 13505 year: 2021 ident: b30 article-title: Long-term cancer survival prediction using multimodal deep learning publication-title: Sci. Rep. – reference: Tianhong Li, Huiwen Chang, Shlok Mishra, Han Zhang, Dina Katabi, Dilip Krishnan, Mage: Masked generative encoder to unify representation learning and image synthesis, in: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2023, pp. 2142–2152. – year: 2023 ident: b38 article-title: Chatgpt is fun, but not an author – year: 2023 ident: b6 article-title: Pacs: Prediction and analysis of cancer subtypes from multi-omics data based on a multi-head attention mechanism model – volume: 68 start-page: 148 year: 2020 end-page: 160 ident: b14 article-title: Optimizing survival analysis of xgboost for ties to predict disease progression of breast cancer publication-title: IEEE Trans. Biomed. Eng. – start-page: 627 year: 2020 end-page: 636 ident: b55 article-title: Learning and reasoning with the graph structure representation in robotic surgery publication-title: Medical Image Computing and Computer Assisted Intervention–MICCAI 2020: 23rd International Conference, Lima, Peru, October 4–8, 2020, Proceedings, Part III 23 – volume: 24 start-page: 3089 year: 2005 end-page: 3110 ident: b54 article-title: Adjusted kaplan–meier estimator and log-rank test with inverse probability of treatment weighting for survival data publication-title: Stat. Med. – volume: 2015 start-page: 68 year: 2015 end-page: 77 ident: b47 article-title: Review the cancer genome atlas (tcga): an immeasurable source of knowledge publication-title: Contemp. Oncol./Współczesna Onkol. – reference: Sanghyun Woo, Shoubhik Debnath, Ronghang Hu, Xinlei Chen, Zhuang Liu, In So Kweon, Saining Xie, Convnext v2: Co-designing and scaling convnets with masked autoencoders, in: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2023, pp. 16133–16142. – volume: 13 start-page: 1 year: 2021 end-page: 17 ident: b5 article-title: Deep learning in cancer diagnosis, prognosis and treatment selection publication-title: Genome Med. – volume: 5 start-page: 555 year: 2021 end-page: 570 ident: b21 article-title: Data-efficient and weakly supervised computational pathology on whole-slide images publication-title: Nat. Biomed. Eng. – volume: 42 start-page: 679 year: 2022 ident: b2 article-title: Vitamin d3 for reducing mortality from cancer and other outcomes before, during and beyond the covid-19 pandemic: A plea for harvesting low-hanging fruit publication-title: Cancer Commun. – reference: Christopher Choy, JunYoung Gwak, Silvio Savarese, 4d spatio-temporal convnets: Minkowski convolutional neural networks, in: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2019, pp. 3075–3084. – volume: 59 start-page: 125 year: 2015 end-page: 133 ident: b4 article-title: Missing data imputation on the 5-year survival prediction of breast cancer patients with unknown discrete values publication-title: Comput. Biol. Med. – reference: Richard J. Chen, Ming Y. Lu, Wei-Hung Weng, Tiffany Y. Chen, Drew F.K. Williamson, Trevor Manz, Maha Shady, Faisal Mahmood, Multimodal co-attention transformer for survival prediction in gigapixel whole slide images, in: Proceedings of the IEEE/CVF International Conference on Computer Vision, 2021, pp. 4015–4025. – reference: Jianan Zhao, Xiao Wang, Chuan Shi, Binbin Hu, Guojie Song, Yanfang Ye, Heterogeneous graph structure learning for graph neural networks, in: Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 35, 2021, pp. 4697–4705. – volume: 39 start-page: btad025 year: 2023 ident: b17 article-title: Camr: cross-aligned multimodal representation learning for cancer survival prediction publication-title: Bioinformatics – volume: 70 year: 2021 ident: b43 article-title: Fine-tuning and training of densenet for histopathology image representation using tcga diagnostic slides publication-title: Med. Image Anal. – volume: 22 start-page: 2663 year: 2015 end-page: 2668 ident: b53 article-title: Sarcopenia is a negative prognostic factor after curative resection of colorectal cancer publication-title: Anna. Surg. Oncol. – volume: 41 start-page: 757 year: 2020 end-page: 770 ident: b28 article-title: Pathomic fusion: an integrated framework for fusing histopathology and genomic features for cancer diagnosis and prognosis publication-title: IEEE Trans. Med. Imaging – volume: 20 start-page: 791 year: 2002 end-page: 796 ident: b49 article-title: Postoperative nomogram for 12-year sarcoma-specific death publication-title: J. Clin. Oncol. – year: 2023 ident: b12 article-title: Ldcsf: Local depth convolution-based swim framework for classifying multi-label histopathology images – volume: 576 start-page: 24 year: 2021 end-page: 36 ident: b7 article-title: Integrating multiple genomic imaging data for the study of lung metastasis in sarcomas using multi-dimensional constrained joint non-negative matrix factorization publication-title: Inform. Sci. – volume: 31 start-page: 1149 year: 2022 end-page: 1160 ident: b10 article-title: Big-hypergraph factorization neural network for survival prediction from whole slide image publication-title: IEEE Trans. Image Process. – reference: Chuxu Zhang, Dongjin Song, Chao Huang, Ananthram Swami, Nitesh V. Chawla, Heterogeneous graph neural network, in: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, 2019, pp. 793–803. – reference: Benjamin Graham, Martin Engelcke, Laurens Van Der Maaten, 3d semantic segmentation with submanifold sparse convolutional networks, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2018, pp. 9224–9232. – volume: 37 start-page: 2963 year: 2021 end-page: 2970 ident: b18 article-title: Gpdbn: deep bilinear network integrating both genomic data and pathological images for breast cancer prognosis prediction publication-title: Bioinformatics – volume: Vol. 88 start-page: 187 year: 2023 end-page: 200 ident: b8 article-title: Artificial intelligence-based multi-omics analysis fuels cancer precision medicine publication-title: Seminars in Cancer Biology – year: 2022 ident: b61 article-title: Convmae: Masked convolution meets masked autoencoders – volume: 77 year: 2022 ident: b11 article-title: Noise-reducing attention cross fusion learning transformer for histological image classification of osteosarcoma publication-title: Biomed. Signal Process. Control – volume: 115 start-page: E2970 year: 2018 end-page: E2979 ident: b29 article-title: Predicting cancer outcomes from histology and genomics using convolutional networks publication-title: Proc. Natl. Acad. Sci. – reference: Qiankun Liu, Zhentao Tan, Dongdong Chen, Qi Chu, Xiyang Dai, Yinpeng Chen, Mengchen Liu, Lu Yuan, Nenghai Yu, Reduce information loss in transformers for pluralistic image inpainting, in: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2022, pp. 11347–11357. – volume: 16 start-page: 2430 year: 2020 ident: b13 article-title: A novel ferroptosis-related gene signature for overall survival prediction in patients with hepatocellular carcinoma publication-title: Int. J. Biol. Sci. – volume: 154 year: 2023 ident: b20 article-title: Deep multimodal graph-based network for survival prediction from highly multiplexed images and patient variables publication-title: Comput. Biol. Med. – volume: 34 start-page: 2136 year: 2021 end-page: 2147 ident: b27 article-title: Transmil: Transformer based correlated multiple instance learning for whole slide image classification publication-title: Adv. Neural Inf. Process. Syst. – start-page: 574 year: 2020 end-page: 591 ident: b45 article-title: Pointcontrast: Unsupervised pre-training for 3d point cloud understanding publication-title: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part III 16 – volume: 14 start-page: 482 year: 2019 end-page: 517 ident: b48 article-title: Pathway enrichment analysis and visualization of omics data using g: Profiler, gsea, cytoscape and enrichmentmap publication-title: Nat. Protoc. – volume: 93 start-page: 27 year: 2009 end-page: 32 ident: b58 article-title: Ectopic gene conversions in the human genome publication-title: Genomics – year: 2023 ident: b3 article-title: Multi-head attention mechanism learning for cancer new subtypes and treatment based on cancer multi-omics data – start-page: 2127 year: 2018 end-page: 2136 ident: b25 article-title: Welling Attention-based deep multiple instance learning publication-title: International Conference on Machine Learning – reference: Xin Lin, Changxing Ding, Jinquan Zeng, Dacheng Tao, Tao Gps-net: Graph property sensing network for scene graph generation, in: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2020, pp. 3746–3753. – volume: 38 start-page: 2587 year: 2022 end-page: 2594 ident: b19 article-title: Hfbsurv: hierarchical multimodal fusion with factorized bilinear models for cancer survival prediction publication-title: Bioinformatics – volume: 40 start-page: 865 year: 2022 end-page: 878 ident: b22 article-title: Pan-cancer integrative histology-genomic analysis via multimodal deep learning publication-title: Cancer Cell – volume: 38 start-page: 410 year: 2022 end-page: 418 ident: b52 article-title: Xgblc: an improved survival prediction model based on xgboost publication-title: Bioinformatics – reference: Yutong Bai, Zeyu Wang, Junfei Xiao, Chen Wei, Huiyu Wang, Alan L. Yuille, Yuyin Zhou, Cihang Xie, Masked autoencoders enable efficient knowledge distillers, in: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2023, pp. 24256–24265. – year: 2023 ident: b23 article-title: Hybrid graph convolutional network with online masked autoencoder for robust multimodal cancer survival prediction publication-title: IEEE Trans. Med. Imaging – volume: 202 year: 2022 ident: b60 article-title: A spatiotemporal graph neural network for session-based recommendation publication-title: Expert Syst. Appl. – reference: Kaiming He, Xinlei Chen, Saining Xie, Yanghao Li, Piotr Dollár, Ross Girshick, Masked autoencoders are scalable vision learners, in: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2022, pp. 16000–16009. – volume: 48 start-page: 1 year: 2014 end-page: 7 ident: b15 article-title: Risk classification of cancer survival using ann with gene expression data from multiple laboratories publication-title: Comput. Biol. Med. – volume: 220 start-page: 381 year: 2019 end-page: e1 ident: b51 article-title: Survival outcome prediction in cervical cancer: Cox models vs deep-learning model publication-title: Am. J. Obstet. Gynecol. – volume: 5 start-page: 351 year: 2023 end-page: 362 ident: b16 article-title: Multimodal data fusion for cancer biomarker discovery with deep learning publication-title: Nat. Mach. Intell. – volume: 6 start-page: 45 year: 2022 ident: b57 article-title: Cell graph neural networks enable the precise prediction of patient survival in gastric cancer publication-title: NPJ Precis. Oncol. – volume: 35 start-page: i446 year: 2019 end-page: i454 ident: b31 article-title: Deep learning with multimodal representation for pancancer prognosis prediction publication-title: Bioinformatics – year: 2023 ident: b41 article-title: Contrastive masked autoencoders are stronger vision learners publication-title: IEEE Trans. Pattern Anal. Mach. Intell. – volume: 51 start-page: 1 year: 2019 end-page: 36 ident: b50 article-title: Machine learning for survival analysis: A survey publication-title: ACM Comput. Surv. – year: 2023 ident: b24 article-title: Modeling dense multimodal interactions between biological pathways and histology for survival prediction – reference: Yijun Tian, Kaiwen Dong, Chunhui Zhang, Chuxu Zhang, Nitesh V. Chawla, Heterogeneous graph masked autoencoders, in: Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 37, 2023, pp. 9997–10005. – start-page: 625 year: 2020 end-page: 635 ident: b56 article-title: Pooling regularized graph neural network for fmri biomarker analysis publication-title: Medical Image Computing and Computer Assisted Intervention–MICCAI 2020: 23rd International Conference, Lima, Peru, October 4–8, 2020, Proceedings, Part VII 23 – volume: 29 start-page: 3850 year: 2023 end-page: 3851 ident: b1 article-title: Aacr cancer progress report 2023: Advancing the frontiers of cancer science and medicine publication-title: Clin. Cancer Res. – volume: 65 year: 2020 ident: b9 article-title: Whole slide images based cancer survival prediction using attention guided deep multiple instance learning networks publication-title: Med. Image Anal. – ident: 10.1016/j.compbiomed.2024.108301_b46 – year: 2023 ident: 10.1016/j.compbiomed.2024.108301_b12 – volume: 29 start-page: 3850 issue: 19 year: 2023 ident: 10.1016/j.compbiomed.2024.108301_b1 article-title: Aacr cancer progress report 2023: Advancing the frontiers of cancer science and medicine publication-title: Clin. Cancer Res. doi: 10.1158/1078-0432.CCR-23-2591 – ident: 10.1016/j.compbiomed.2024.108301_b33 doi: 10.1609/aaai.v37i8.26192 – ident: 10.1016/j.compbiomed.2024.108301_b62 – volume: 42 start-page: 679 issue: 8 year: 2022 ident: 10.1016/j.compbiomed.2024.108301_b2 article-title: Vitamin d3 for reducing mortality from cancer and other outcomes before, during and beyond the covid-19 pandemic: A plea for harvesting low-hanging fruit publication-title: Cancer Commun. doi: 10.1002/cac2.12328 – ident: 10.1016/j.compbiomed.2024.108301_b37 doi: 10.1145/1390156.1390294 – ident: 10.1016/j.compbiomed.2024.108301_b59 doi: 10.1109/CVPR42600.2020.00380 – year: 2023 ident: 10.1016/j.compbiomed.2024.108301_b6 – volume: 11 start-page: 13505 issue: 1 year: 2021 ident: 10.1016/j.compbiomed.2024.108301_b30 article-title: Long-term cancer survival prediction using multimodal deep learning publication-title: Sci. Rep. doi: 10.1038/s41598-021-92799-4 – volume: Vol. 88 start-page: 187 year: 2023 ident: 10.1016/j.compbiomed.2024.108301_b8 article-title: Artificial intelligence-based multi-omics analysis fuels cancer precision medicine – start-page: 574 year: 2020 ident: 10.1016/j.compbiomed.2024.108301_b45 article-title: Pointcontrast: Unsupervised pre-training for 3d point cloud understanding – volume: 5 start-page: 351 issue: 4 year: 2023 ident: 10.1016/j.compbiomed.2024.108301_b16 article-title: Multimodal data fusion for cancer biomarker discovery with deep learning publication-title: Nat. Mach. Intell. doi: 10.1038/s42256-023-00633-5 – ident: 10.1016/j.compbiomed.2024.108301_b36 – volume: 16 start-page: 2430 issue: 13 year: 2020 ident: 10.1016/j.compbiomed.2024.108301_b13 article-title: A novel ferroptosis-related gene signature for overall survival prediction in patients with hepatocellular carcinoma publication-title: Int. J. Biol. Sci. doi: 10.7150/ijbs.45050 – volume: 68 start-page: 148 issue: 1 year: 2020 ident: 10.1016/j.compbiomed.2024.108301_b14 article-title: Optimizing survival analysis of xgboost for ties to predict disease progression of breast cancer publication-title: IEEE Trans. Biomed. Eng. doi: 10.1109/TBME.2020.2993278 – volume: 37 start-page: 2963 issue: 18 year: 2021 ident: 10.1016/j.compbiomed.2024.108301_b18 article-title: Gpdbn: deep bilinear network integrating both genomic data and pathological images for breast cancer prognosis prediction publication-title: Bioinformatics doi: 10.1093/bioinformatics/btab185 – ident: 10.1016/j.compbiomed.2024.108301_b32 doi: 10.1109/CVPR.2018.00961 – volume: 59 start-page: 125 year: 2015 ident: 10.1016/j.compbiomed.2024.108301_b4 article-title: Missing data imputation on the 5-year survival prediction of breast cancer patients with unknown discrete values publication-title: Comput. Biol. Med. doi: 10.1016/j.compbiomed.2015.02.006 – ident: 10.1016/j.compbiomed.2024.108301_b39 – year: 2023 ident: 10.1016/j.compbiomed.2024.108301_b3 – volume: 93 start-page: 27 issue: 1 year: 2009 ident: 10.1016/j.compbiomed.2024.108301_b58 article-title: Ectopic gene conversions in the human genome publication-title: Genomics doi: 10.1016/j.ygeno.2008.09.007 – volume: 38 start-page: 2587 issue: 9 year: 2022 ident: 10.1016/j.compbiomed.2024.108301_b19 article-title: Hfbsurv: hierarchical multimodal fusion with factorized bilinear models for cancer survival prediction publication-title: Bioinformatics doi: 10.1093/bioinformatics/btac113 – volume: 39 start-page: btad025 issue: 1 year: 2023 ident: 10.1016/j.compbiomed.2024.108301_b17 article-title: Camr: cross-aligned multimodal representation learning for cancer survival prediction publication-title: Bioinformatics doi: 10.1093/bioinformatics/btad025 – year: 2023 ident: 10.1016/j.compbiomed.2024.108301_b24 – start-page: 627 year: 2020 ident: 10.1016/j.compbiomed.2024.108301_b55 article-title: Learning and reasoning with the graph structure representation in robotic surgery – year: 2023 ident: 10.1016/j.compbiomed.2024.108301_b23 article-title: Hybrid graph convolutional network with online masked autoencoder for robust multimodal cancer survival prediction publication-title: IEEE Trans. Med. Imaging doi: 10.1109/TMI.2023.3253760 – year: 2022 ident: 10.1016/j.compbiomed.2024.108301_b61 – volume: 6 start-page: 45 issue: 1 year: 2022 ident: 10.1016/j.compbiomed.2024.108301_b57 article-title: Cell graph neural networks enable the precise prediction of patient survival in gastric cancer publication-title: NPJ Precis. Oncol. doi: 10.1038/s41698-022-00285-5 – volume: 41 start-page: 757 issue: 4 year: 2020 ident: 10.1016/j.compbiomed.2024.108301_b28 article-title: Pathomic fusion: an integrated framework for fusing histopathology and genomic features for cancer diagnosis and prognosis publication-title: IEEE Trans. Med. Imaging doi: 10.1109/TMI.2020.3021387 – volume: 14 start-page: 482 issue: 2 year: 2019 ident: 10.1016/j.compbiomed.2024.108301_b48 article-title: Pathway enrichment analysis and visualization of omics data using g: Profiler, gsea, cytoscape and enrichmentmap publication-title: Nat. Protoc. doi: 10.1038/s41596-018-0103-9 – ident: 10.1016/j.compbiomed.2024.108301_b35 doi: 10.1609/aaai.v35i5.16600 – start-page: 625 year: 2020 ident: 10.1016/j.compbiomed.2024.108301_b56 article-title: Pooling regularized graph neural network for fmri biomarker analysis – ident: 10.1016/j.compbiomed.2024.108301_b34 doi: 10.1145/3292500.3330961 – volume: 31 start-page: 1149 year: 2022 ident: 10.1016/j.compbiomed.2024.108301_b10 article-title: Big-hypergraph factorization neural network for survival prediction from whole slide image publication-title: IEEE Trans. Image Process. doi: 10.1109/TIP.2021.3139229 – ident: 10.1016/j.compbiomed.2024.108301_b44 – volume: 13 start-page: 1 issue: 1 year: 2021 ident: 10.1016/j.compbiomed.2024.108301_b5 article-title: Deep learning in cancer diagnosis, prognosis and treatment selection publication-title: Genome Med. doi: 10.1186/s13073-021-00968-x – volume: 34 start-page: 2136 year: 2021 ident: 10.1016/j.compbiomed.2024.108301_b27 article-title: Transmil: Transformer based correlated multiple instance learning for whole slide image classification publication-title: Adv. Neural Inf. Process. Syst. – ident: 10.1016/j.compbiomed.2024.108301_b40 doi: 10.1109/CVPR52729.2023.02323 – volume: 38 start-page: 410 issue: 2 year: 2022 ident: 10.1016/j.compbiomed.2024.108301_b52 article-title: Xgblc: an improved survival prediction model based on xgboost publication-title: Bioinformatics doi: 10.1093/bioinformatics/btab675 – volume: 77 year: 2022 ident: 10.1016/j.compbiomed.2024.108301_b11 article-title: Noise-reducing attention cross fusion learning transformer for histological image classification of osteosarcoma publication-title: Biomed. Signal Process. Control doi: 10.1016/j.bspc.2022.103824 – volume: 22 start-page: 2663 year: 2015 ident: 10.1016/j.compbiomed.2024.108301_b53 article-title: Sarcopenia is a negative prognostic factor after curative resection of colorectal cancer publication-title: Anna. Surg. Oncol. doi: 10.1245/s10434-014-4281-6 – year: 2023 ident: 10.1016/j.compbiomed.2024.108301_b38 – volume: 48 start-page: 1 year: 2014 ident: 10.1016/j.compbiomed.2024.108301_b15 article-title: Risk classification of cancer survival using ann with gene expression data from multiple laboratories publication-title: Comput. Biol. Med. doi: 10.1016/j.compbiomed.2014.02.006 – volume: 154 year: 2023 ident: 10.1016/j.compbiomed.2024.108301_b20 article-title: Deep multimodal graph-based network for survival prediction from highly multiplexed images and patient variables publication-title: Comput. Biol. Med. doi: 10.1016/j.compbiomed.2023.106576 – volume: 5 start-page: 555 issue: 6 year: 2021 ident: 10.1016/j.compbiomed.2024.108301_b21 article-title: Data-efficient and weakly supervised computational pathology on whole-slide images publication-title: Nat. Biomed. Eng. doi: 10.1038/s41551-020-00682-w – volume: 20 start-page: 791 issue: 3 year: 2002 ident: 10.1016/j.compbiomed.2024.108301_b49 article-title: Postoperative nomogram for 12-year sarcoma-specific death publication-title: J. Clin. Oncol. doi: 10.1200/JCO.2002.20.3.791 – volume: 220 start-page: 381 issue: 4 year: 2019 ident: 10.1016/j.compbiomed.2024.108301_b51 article-title: Survival outcome prediction in cervical cancer: Cox models vs deep-learning model publication-title: Am. J. Obstet. Gynecol. doi: 10.1016/j.ajog.2018.12.030 – volume: 65 year: 2020 ident: 10.1016/j.compbiomed.2024.108301_b9 article-title: Whole slide images based cancer survival prediction using attention guided deep multiple instance learning networks publication-title: Med. Image Anal. doi: 10.1016/j.media.2020.101789 – volume: 115 start-page: E2970 issue: 13 year: 2018 ident: 10.1016/j.compbiomed.2024.108301_b29 article-title: Predicting cancer outcomes from histology and genomics using convolutional networks publication-title: Proc. Natl. Acad. Sci. doi: 10.1073/pnas.1717139115 – volume: 35 start-page: i446 issue: 14 year: 2019 ident: 10.1016/j.compbiomed.2024.108301_b31 article-title: Deep learning with multimodal representation for pancancer prognosis prediction publication-title: Bioinformatics doi: 10.1093/bioinformatics/btz342 – start-page: 2127 year: 2018 ident: 10.1016/j.compbiomed.2024.108301_b25 article-title: Welling Attention-based deep multiple instance learning – volume: 2015 start-page: 68 issue: 1 year: 2015 ident: 10.1016/j.compbiomed.2024.108301_b47 article-title: Review the cancer genome atlas (tcga): an immeasurable source of knowledge publication-title: Contemp. Oncol./Współczesna Onkol. doi: 10.5114/wo.2014.47136 – ident: 10.1016/j.compbiomed.2024.108301_b42 doi: 10.1109/CVPR52729.2023.00213 – volume: 202 year: 2022 ident: 10.1016/j.compbiomed.2024.108301_b60 article-title: A spatiotemporal graph neural network for session-based recommendation publication-title: Expert Syst. Appl. doi: 10.1016/j.eswa.2022.117114 – ident: 10.1016/j.compbiomed.2024.108301_b26 – volume: 40 start-page: 865 issue: 8 year: 2022 ident: 10.1016/j.compbiomed.2024.108301_b22 article-title: Pan-cancer integrative histology-genomic analysis via multimodal deep learning publication-title: Cancer Cell doi: 10.1016/j.ccell.2022.07.004 – volume: 576 start-page: 24 year: 2021 ident: 10.1016/j.compbiomed.2024.108301_b7 article-title: Integrating multiple genomic imaging data for the study of lung metastasis in sarcomas using multi-dimensional constrained joint non-negative matrix factorization publication-title: Inform. Sci. doi: 10.1016/j.ins.2021.06.058 – volume: 51 start-page: 1 issue: 6 year: 2019 ident: 10.1016/j.compbiomed.2024.108301_b50 article-title: Machine learning for survival analysis: A survey publication-title: ACM Comput. Surv. doi: 10.1145/3214306 – year: 2023 ident: 10.1016/j.compbiomed.2024.108301_b41 article-title: Contrastive masked autoencoders are stronger vision learners publication-title: IEEE Trans. Pattern Anal. Mach. Intell. – volume: 24 start-page: 3089 issue: 20 year: 2005 ident: 10.1016/j.compbiomed.2024.108301_b54 article-title: Adjusted kaplan–meier estimator and log-rank test with inverse probability of treatment weighting for survival data publication-title: Stat. Med. doi: 10.1002/sim.2174 – volume: 70 year: 2021 ident: 10.1016/j.compbiomed.2024.108301_b43 article-title: Fine-tuning and training of densenet for histopathology image representation using tcga diagnostic slides publication-title: Med. Image Anal. doi: 10.1016/j.media.2021.102032 |
| SSID | ssj0004030 |
| Score | 2.426881 |
| Snippet | Accurately predicting the survival rate of cancer patients is crucial for aiding clinicians in planning appropriate treatment, reducing cancer-related medical... AbstractAccurately predicting the survival rate of cancer patients is crucial for aiding clinicians in planning appropriate treatment, reducing cancer-related... |
| SourceID | proquest pubmed crossref elsevier |
| SourceType | Aggregation Database Index Database Enrichment Source Publisher |
| StartPage | 108301 |
| SubjectTerms | Cancer Coders Convolutional mask encoder Embedding Graph theory Heterogeneous graph Humans Internal Medicine Missing Modules Multimodal Neoplasms - diagnostic imaging Other Patients Predictions Quality of Life Reconstruction Robust Robustness Survival |
| Title | SELECTOR: Heterogeneous graph network with convolutional masked autoencoder for multimodal robust prediction of cancer survival |
| URI | https://www.clinicalkey.com/#!/content/1-s2.0-S0010482524003858 https://www.clinicalkey.es/playcontent/1-s2.0-S0010482524003858 https://dx.doi.org/10.1016/j.compbiomed.2024.108301 https://www.ncbi.nlm.nih.gov/pubmed/38492453 https://www.proquest.com/docview/2973419012 https://www.proquest.com/docview/2958293778 |
| Volume | 172 |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| journalDatabaseRights | – providerCode: PRVESC databaseName: Elsevier SD Freedom Collection Journals 2021 customDbUrl: eissn: 1879-0534 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0004030 issn: 0010-4825 databaseCode: AIEXJ dateStart: 19950101 isFulltext: true titleUrlDefault: https://www.sciencedirect.com providerName: Elsevier |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwtV3Pb9MwFLa6DiEuiJ-jMCYjcZuK2jhubDhNU9FAY0ysQHeynMRBLSOpmqbajRt_N-_FTtKyTSpCXKIqjWPL3-eXZ_t7z4S8DOPAjyUgwLhgXd_AUNSSJ10-CH3DJXfJqr8cBycnYjyWp63WryoWZnkRpKm4vJSz_wo13AOwMXT2L-CuXwo34DeADleAHa4bAX82PB4ejj5-wrn-EYpdMnjUoNK1TE69n1rhdyM6d63BMBKdfwcHVBeLDPNbYpoJVCGWosMfWYxnAGRhkS8ws0A8iSpnM0LmzPfzAszO0jWvSn7gDo0oVberCZ_-3NM_dTEQwNZv82LSWGxri84n06IRD5UKhPOG11_dovcYS6-uY3ir8pdyca0KsGnUTKXBhs-EL2xsdG2w7WE_V4y_XYeYInYzm7zgFVaEMkrmalpPrX1WZieCt6OQFndIt8i2F3Ap2mT74N1w_L6JsO0xG8zkmuM0YVYpeH19Nzk6N01kSodmdI_cdTMRemAZdJ-0TPqA3P7gcHlIflZEek3XaERLGlFHI4o0oms0opZGdIVGFGhEGxpRSyPa0IhmCbU0ohWNHpHPb4ejw6OuO66jG4FTs-gmMHM3Ik762tMxSzSLfBGLyDMh9J4c-APoYj_kOmFayyjgmkeahXHMGCbL7kXsMWmnWWqeENrDZQYPnKdeaPyBBEQEGBChDRMm0UncIUHVtypyuezxSJULVYkWp6pBRSEqyqLSIf265Mzmc9mgjKzgU1W8MnxhFfBug7LBdWVN7ixGrvoq91RPXeFih7ypSzpv2Hq5G9a7W_FM1VXhOXU-zgC8DnlR_w3fE9wk1CWH4BkuYAoQBNCAHcvPuqMAP-n5nD39p6Y9I3ea8b9L2ot5YZ6TW9FyMcnne2QrGIs9N_5-Ax_uA5U |
| linkProvider | Elsevier |
| 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=SELECTOR%3A+Heterogeneous+graph+network+with+convolutional+masked+autoencoder+for+multimodal+robust+prediction+of+cancer+survival&rft.jtitle=Computers+in+biology+and+medicine&rft.au=Pan%2C+Liangrui&rft.au=Peng%2C+Yijun&rft.au=Li%2C+Yan&rft.au=Wang%2C+Xiang&rft.date=2024-04-01&rft.pub=Elsevier+Ltd&rft.issn=0010-4825&rft.volume=172&rft_id=info:doi/10.1016%2Fj.compbiomed.2024.108301&rft.externalDocID=S0010482524003858 |
| thumbnail_m | http://cvtisr.summon.serialssolutions.com/2.0.0/image/custom?url=https%3A%2F%2Fcdn.clinicalkey.com%2Fck-thumbnails%2F00104825%2Fcov200h.gif |