{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T13:24:44Z","timestamp":1740144284371,"version":"3.37.3"},"reference-count":33,"publisher":"Springer Science and Business Media LLC","issue":"12","license":[{"start":{"date-parts":[[2023,8,21]],"date-time":"2023-08-21T00:00:00Z","timestamp":1692576000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,8,21]],"date-time":"2023-08-21T00:00:00Z","timestamp":1692576000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["91959127"],"award-info":[{"award-number":["91959127"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100020089","name":"Science and Technology Commission of Fengxian District, Shanghai Municipality","doi-asserted-by":"publisher","award":["20Z11900100"],"award-info":[{"award-number":["20Z11900100"]}],"id":[{"id":"10.13039\/501100020089","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Shanghai Municipal Science and Technology Major Project","award":["2018SHZDZX01"],"award-info":[{"award-number":["2018SHZDZX01"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Int J CARS"],"DOI":"10.1007\/s11548-023-02985-0","type":"journal-article","created":{"date-parts":[[2023,8,21]],"date-time":"2023-08-21T05:02:03Z","timestamp":1692594123000},"page":"2273-2286","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["OCIF: automatically learning the optimized clinical information fusion method for computer-aided diagnosis tasks"],"prefix":"10.1007","volume":"18","author":[{"given":"Zhaoyu","family":"Hu","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Leyin","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"An","family":"Sui","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Guoqing","family":"Wu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuanyuan","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhifeng","family":"Shi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0654-6034","authenticated-orcid":false,"given":"Jinhua","family":"Yu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Liang","family":"Chen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Guiguan","family":"Yang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuhao","family":"Sun","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,8,21]]},"reference":[{"key":"2985_CR1","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2020.103758","author":"MA Naser","year":"2020","unstructured":"Naser MA, Deen MJ (2020) Brain tumor segmentation and grading of lower-grade glioma using deep learning in MRI images. Comput Biol Med. https:\/\/doi.org\/10.1016\/j.compbiomed.2020.103758","journal-title":"Comput Biol Med"},{"key":"2985_CR2","doi-asserted-by":"publisher","first-page":"3509","DOI":"10.1007\/s00330-016-4653-3","volume":"27","author":"JH Yu","year":"2017","unstructured":"Yu JH, Shi ZF, Lian YX, Li ZJ, Liu TT, Gao Y, Wang YY, Chen L, Mao Y (2017) Noninvasive IDH1 mutation estimation based on a quantitative radiomics approach for grade II glioma. Eur Radiol 27:3509\u20133522. https:\/\/doi.org\/10.1007\/s00330-016-4653-3","journal-title":"Eur Radiol"},{"key":"2985_CR3","doi-asserted-by":"publisher","DOI":"10.3389\/fncom.2020.00025","author":"X Feng","year":"2020","unstructured":"Feng X, Tustison NJ, Patel SH, Meyer CH (2020) Brain tumor segmentation using an ensemble of 3D U-nets and overall survival prediction using radiomic features. Front Comput Neurosc. https:\/\/doi.org\/10.3389\/fncom.2020.00025","journal-title":"Front Comput Neurosc"},{"key":"2985_CR4","doi-asserted-by":"crossref","unstructured":"Zhou T, Fu H, Zhang Y, Zhang C, Lu X, Shen J, Shao L (2020) M2Net: multi-modal multi-channel network for overall survival time prediction of brain tumor patients. In: Proceedings of the international conference on medical image computing and computer-assisted intervention, pp 221\u2013231","DOI":"10.1007\/978-3-030-59713-9_22"},{"key":"2985_CR5","doi-asserted-by":"publisher","first-page":"352","DOI":"10.1007\/978-3-030-72087-2_31","volume":"12659","author":"L Chato","year":"2021","unstructured":"Chato L, Kachroo P, Latifi S (2021) An automatic overall survival time prediction system for glioma brain tumor patients based on volumetric and shape features. Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries (Brainles 2020) Pt Ii 12659:352\u2013365. https:\/\/doi.org\/10.1007\/978-3-030-72087-2_31","journal-title":"Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries (Brainles 2020) Pt Ii"},{"key":"2985_CR6","doi-asserted-by":"crossref","unstructured":"Tang W, Zhang H, Yu P, Kang H, Zhang R (2022) MMMNA-net for overall survival time prediction of brain tumor patients. In: Proceedings of the 2022 44th annual international conference of the IEEE engineering in medicine & biology society (EMBC), pp 3805\u20133808","DOI":"10.1109\/EMBC48229.2022.9871639"},{"key":"2985_CR7","first-page":"E364","volume":"49","author":"Z Hu","year":"2022","unstructured":"Hu Z, Yang Z, Zhao J, Zhang H, Vaios E, Lafata K, Yin F, Wang C (2022) A deep learning model with radiomics analysis integration for glioblastoma post-resection survival prediction. Med Phys 49:E364\u2013E364","journal-title":"Med Phys"},{"key":"2985_CR8","doi-asserted-by":"publisher","DOI":"10.1038\/s41467-021-24605-8","author":"XY Zheng","year":"2021","unstructured":"Zheng XY, Yao Z, Huang YN, Yu YY, Wang Y, Liu YB, Mao RS, Li F, Xiao Y, Wang YY et al (2021) Deep learning radiomics can predict axillary lymph node status in early-stage breast cancer. Nat Commun. https:\/\/doi.org\/10.1038\/s41467-021-24605-8","journal-title":"Nat Commun"},{"key":"2985_CR9","doi-asserted-by":"publisher","DOI":"10.1145\/3447582","author":"PZ Ren","year":"2021","unstructured":"Ren PZ, Xiao Y, Chang XJ, Huang PY, Li ZH, Chen XJ, Wang X (2021) A comprehensive survey of neural architecture search: challenges and solutions. Acm Comput Surv. https:\/\/doi.org\/10.1145\/3447582","journal-title":"Acm Comput Surv"},{"key":"2985_CR10","doi-asserted-by":"crossref","unstructured":"Elsken T, Metzen JH, Hutter F (2019) Neural architecture search: a survey. J Mach Learn Res 20","DOI":"10.1007\/978-3-030-05318-5_3"},{"key":"2985_CR11","doi-asserted-by":"publisher","first-page":"9061","DOI":"10.1109\/Access.2020.2964424","volume":"8","author":"A Kwasigroch","year":"2020","unstructured":"Kwasigroch A, Grochowski M, Mikolajczyk A (2020) Neural Architecture search for skin lesion classification. Ieee Access 8:9061\u20139071. https:\/\/doi.org\/10.1109\/Access.2020.2964424","journal-title":"Ieee Access"},{"key":"2985_CR12","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2021.107825","author":"HL Jiang","year":"2021","unstructured":"Jiang HL, Shen FH, Gao F, Han WD (2021) Learning efficient, explainable and discriminative representations for pulmonary nodules classification. Pattern Recogn. https:\/\/doi.org\/10.1016\/j.patcog.2021.107825","journal-title":"Pattern Recogn"},{"key":"2985_CR13","doi-asserted-by":"crossref","unstructured":"Liu Z, Wang H, Zhang S, Wang G, Qi J (2020) Nas-scam: neural architecture search-based spatial and channel joint attention module for nuclei semantic segmentation and classification. In: Proceedings of the international conference on medical image computing and computer-assisted intervention, pp 263\u2013272","DOI":"10.1007\/978-3-030-59710-8_26"},{"key":"2985_CR14","doi-asserted-by":"publisher","first-page":"2971","DOI":"10.1109\/Tpami.2021.3052758","volume":"43","author":"ZC Lu","year":"2021","unstructured":"Lu ZC, Sreekumar G, Goodman E, Banzhaf W, Deb K, Boddeti VN (2021) Neural architecture transfer. Ieee T Pattern Anal 43:2971\u20132989. https:\/\/doi.org\/10.1109\/Tpami.2021.3052758","journal-title":"Ieee T Pattern Anal"},{"key":"2985_CR15","doi-asserted-by":"publisher","first-page":"285","DOI":"10.1007\/978-3-030-46643-5_28","volume":"11993","author":"XQ Guo","year":"2020","unstructured":"Guo XQ, Yang C, Lam PL, Woo PYM, Yuan YX (2020) Domain knowledge based brain tumor segmentation and overall survival prediction. Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries (Brainles 2019) Pt Ii 11993:285\u2013295. https:\/\/doi.org\/10.1007\/978-3-030-46643-5_28","journal-title":"Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries (Brainles 2019) Pt Ii"},{"key":"2985_CR16","doi-asserted-by":"publisher","first-page":"199","DOI":"10.1007\/978-3-030-11726-9_18","volume":"11384","author":"E Puybareau","year":"2019","unstructured":"Puybareau E, Tochon G, Chazalon J, Fabrizio J (2019) Segmentation of gliomas and prediction of patient overall survival: a simple and fast procedure. Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries, Brainles 2018 Pt Ii 11384:199\u2013209. https:\/\/doi.org\/10.1007\/978-3-030-11726-9_18","journal-title":"Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries, Brainles 2018 Pt Ii"},{"key":"2985_CR17","doi-asserted-by":"publisher","first-page":"84","DOI":"10.1145\/3065386","volume":"60","author":"A Krizhevsky","year":"2017","unstructured":"Krizhevsky A, Sutskever I, Hinton GE (2017) ImageNet classification with deep convolutional neural networks. Commun Acm 60:84\u201390. https:\/\/doi.org\/10.1145\/3065386","journal-title":"Commun Acm"},{"key":"2985_CR18","volume-title":"Bayesian reasoning and Gaussian processes for machine learning applications","author":"S Tayal","year":"2022","unstructured":"Tayal S, George PM, Singla P, Kose U (2022) Bayesian reasoning and Gaussian processes for machine learning applications, 1st edn. Chapman & Hall\/CRC Press, Boca Raton","edition":"1"},{"key":"2985_CR19","doi-asserted-by":"publisher","first-page":"63","DOI":"10.1007\/978-3-540-28650-9_4","volume":"3176","author":"CE Rasmussen","year":"2004","unstructured":"Rasmussen CE (2004) Gaussian processes in machine learning. Adv Lect Mach Learn 3176:63\u201371. https:\/\/doi.org\/10.1007\/978-3-540-28650-9_4","journal-title":"Adv Lect Mach Learn"},{"key":"2985_CR20","doi-asserted-by":"publisher","first-page":"455","DOI":"10.1023\/A:1008306431147","volume":"13","author":"DR Jones","year":"1998","unstructured":"Jones DR, Schonlau M, Welch WJ (1998) Efficient global optimization of expensive black-box functions. J Global Optim 13:455\u2013492. https:\/\/doi.org\/10.1023\/A:1008306431147","journal-title":"J Global Optim"},{"key":"2985_CR21","doi-asserted-by":"publisher","first-page":"1993","DOI":"10.1109\/Tmi.2014.2377694","volume":"34","author":"BH Menze","year":"2015","unstructured":"Menze BH, Jakab A, Bauer S, Kalpathy-Cramer J, Farahani K, Kirby J, Burren Y, Porz N, Slotboom J, Wiest R et al (2015) The multimodal brain tumor image segmentation benchmark (BRATS). Ieee T Med Imaging 34:1993\u20132024. https:\/\/doi.org\/10.1109\/Tmi.2014.2377694","journal-title":"Ieee T Med Imaging"},{"key":"2985_CR22","doi-asserted-by":"publisher","first-page":"684996","DOI":"10.3389\/fonc.2021.684996","volume":"11","author":"ZH Wang","year":"2021","unstructured":"Wang ZH, Xiao XL, Zhang ZT, He K, Hu F (2021) A radiomics model for predicting early recurrence in grade II gliomas based on preoperative multiparametric magnetic resonance imaging. Front Oncol 11:684996. https:\/\/doi.org\/10.3389\/fonc.2021.684996","journal-title":"Front Oncol"},{"key":"2985_CR23","doi-asserted-by":"publisher","first-page":"274","DOI":"10.1007\/978-3-319-75238-9_24","volume":"10670","author":"F Zhou","year":"2017","unstructured":"Zhou F, Li TF, Li H, Zhu HT (2017) 2018, TPCNN: two-phase patch-based convolutional neural network for automatic brain tumor segmentation and survival prediction. Brainlesion Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries, Brainles 10670:274\u2013286. https:\/\/doi.org\/10.1007\/978-3-319-75238-9_24","journal-title":"Brainlesion Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries, Brainles"},{"key":"2985_CR24","doi-asserted-by":"publisher","first-page":"2269","DOI":"10.1109\/Cvpr.2017.243","volume":"2261","author":"G Huang","year":"2017","unstructured":"Huang G, Liu Z, van der Maaten L, Weinberger KQ (2017) Densely connected convolutional networks. Proc Cvpr Ieee 2261:2269. https:\/\/doi.org\/10.1109\/Cvpr.2017.243","journal-title":"Proc Cvpr Ieee"},{"key":"2985_CR25","doi-asserted-by":"publisher","first-page":"770","DOI":"10.1109\/Cvpr.2016.90","volume":"2016","author":"KM He","year":"2016","unstructured":"He KM, Zhang XY, Ren SQ, Sun J (2016) Deep residual learning for image recognition. Ieee Conf Comput Vis Pattern Recogn (Cvpr) 2016:770\u2013778. https:\/\/doi.org\/10.1109\/Cvpr.2016.90","journal-title":"Ieee Conf Comput Vis Pattern Recogn (Cvpr)"},{"key":"2985_CR26","doi-asserted-by":"publisher","first-page":"2352","DOI":"10.1162\/neco_a_00990","volume":"29","author":"W Rawat","year":"2017","unstructured":"Rawat W, Wang ZH (2017) Deep convolutional neural networks for image classification: a comprehensive review. Neural Comput 29:2352\u20132449. https:\/\/doi.org\/10.1162\/neco_a_00990","journal-title":"Neural Comput"},{"key":"2985_CR27","first-page":"2121","volume":"12","author":"J Duchi","year":"2011","unstructured":"Duchi J, Hazan E, Singer Y (2011) Adaptive subgradient methods for online learning and stochastic optimization. J Mach Learn Res 12:2121\u20132159","journal-title":"J Mach Learn Res"},{"key":"2985_CR28","unstructured":"Sulaiman S, Wahid RA, Ariffin AH, Zulkifli CZJTEM (2020) Question classification based on cognitive levels using linear svc. 83, 6463\u20136470"},{"key":"2985_CR29","doi-asserted-by":"publisher","first-page":"189","DOI":"10.1007\/978-981-13-6861-5_17","volume":"924","author":"A Sharaff","year":"2019","unstructured":"Sharaff A, Gupta H (2019) Extra-tree classifier with metaheuristics approach for email classification. Adv Intell Syst 924:189\u2013197. https:\/\/doi.org\/10.1007\/978-981-13-6861-5_17","journal-title":"Adv Intell Syst"},{"key":"2985_CR30","doi-asserted-by":"publisher","DOI":"10.1186\/s12859-018-2264-5","author":"R Couronne","year":"2018","unstructured":"Couronne R, Probst P, Boulesteix AL (2018) Random forest versus logistic regression: a large-scale benchmark experiment. Bmc Bioinform. https:\/\/doi.org\/10.1186\/s12859-018-2264-5","journal-title":"Bmc Bioinform"},{"key":"2985_CR31","doi-asserted-by":"publisher","first-page":"12777","DOI":"10.1007\/s11042-019-08453-9","volume":"79","author":"C Garbin","year":"2020","unstructured":"Garbin C, Zhu X, Marques O (2020) Dropout vs. batch normalization: an empirical study of their impact to deep learning. Multimed Tools Appl 79:12777\u201312815. https:\/\/doi.org\/10.1007\/s11042-019-08453-9","journal-title":"Multimed Tools Appl"},{"key":"2985_CR32","doi-asserted-by":"crossref","unstructured":"Wei X, Zhang T, Li Y, Zhang Y, Wu F (2020) Multi-modality cross attention network for image and sentence matching. In: Proceedings of the proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 10941\u201310950","DOI":"10.1109\/CVPR42600.2020.01095"},{"key":"2985_CR33","doi-asserted-by":"crossref","unstructured":"Lee K-H, Chen X, Hua G, Hu H, He X (2018) Stacked cross attention for image-text matching. In: Proceedings of the Proceedings of the European conference on computer vision (ECCV), pp 201\u2013216","DOI":"10.1007\/978-3-030-01225-0_13"}],"container-title":["International Journal of Computer Assisted Radiology and Surgery"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11548-023-02985-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11548-023-02985-0\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11548-023-02985-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,11,8]],"date-time":"2023-11-08T15:13:34Z","timestamp":1699456414000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11548-023-02985-0"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,8,21]]},"references-count":33,"journal-issue":{"issue":"12","published-online":{"date-parts":[[2023,12]]}},"alternative-id":["2985"],"URL":"https:\/\/doi.org\/10.1007\/s11548-023-02985-0","relation":{},"ISSN":["1861-6429"],"issn-type":[{"type":"electronic","value":"1861-6429"}],"subject":[],"published":{"date-parts":[[2023,8,21]]},"assertion":[{"value":"29 August 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"12 June 2023","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"21 August 2023","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare that they have no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}