{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T07:25:53Z","timestamp":1740122753450,"version":"3.37.3"},"reference-count":35,"publisher":"Springer Science and Business Media LLC","issue":"4","license":[{"start":{"date-parts":[[2021,4,24]],"date-time":"2021-04-24T00:00:00Z","timestamp":1619222400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,4,24]],"date-time":"2021-04-24T00:00:00Z","timestamp":1619222400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neural Process Lett"],"published-print":{"date-parts":[[2021,8]]},"DOI":"10.1007\/s11063-021-10485-y","type":"journal-article","created":{"date-parts":[[2021,4,25]],"date-time":"2021-04-25T06:35:20Z","timestamp":1619332520000},"page":"2665-2685","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["DDV: A Taxonomy for Deep Learning Methods in Detecting Prostate Cancer"],"prefix":"10.1007","volume":"53","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2309-3540","authenticated-orcid":false,"given":"Abeer","family":"Alsadoon","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ghazi","family":"Al-Naymat","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Omar Hisham","family":"Alsadoon","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"P. W. C.","family":"Prasad","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2021,4,24]]},"reference":[{"key":"10485_CR1","doi-asserted-by":"crossref","unstructured":"R Seigl, K Miller, A Jemal (2016) Cancer statistics. CA A Cancer J. Clin. pp 66, 7\u201330","DOI":"10.3322\/caac.21332"},{"key":"10485_CR2","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s11063-017-9706-3","volume":"48","author":"L Lausser","year":"2018","unstructured":"Lausser L, Szekely R, Schirra L, Kestler H (2018) The influence of multi-class feature selection on the prediction of diagnostic phenotypes. Neural Process Lett 48:1\u201318","journal-title":"Neural Process Lett"},{"issue":"1","key":"10485_CR3","doi-asserted-by":"publisher","first-page":"35","DOI":"10.1186\/s41747-019-0109-2","volume":"3","author":"R Cuocolo","year":"2019","unstructured":"Cuocolo R, Cipullo MB, Stanzione A, Ugga L, Romeo V, Radice L, Brunetti A, Imbriaco M (2019) Machine learning applications in prostate cancer magnetic resonance imaging. Europ Radiol Exp 3(1):35. https:\/\/doi.org\/10.1186\/s41747-019-0109-2","journal-title":"Europ Radiol Exp"},{"key":"10485_CR4","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41598-019-55972-4","volume":"9","author":"S Yoo","year":"2019","unstructured":"Yoo S, Gujrathi I, Haider MA, Khalvati F (2019) Prostate cancer detection using deep convolutional neural networks. Sci Rep 9:1. https:\/\/doi.org\/10.1038\/s41598-019-55972-4","journal-title":"Sci Rep"},{"issue":"16","key":"10485_CR5","doi-asserted-by":"publisher","first-page":"5847","DOI":"10.3390\/ijms21165847","volume":"21","author":"O Snow","year":"2020","unstructured":"Snow O, Lallous N, Ester M, Cherkasov A (2020) Deep learning modeling of androgen receptor responses to prostate cancer therapies. Int J Mol Sci 21(16):5847","journal-title":"Int J Mol Sci"},{"key":"10485_CR6","doi-asserted-by":"crossref","unstructured":"Cheng H-T, Koc L, Harmsen J, Shaked T, Chandra T, Aradhye H, Anderson G, Corrado G, Chai W, Mustafa I, Rohan A, Zakaria H, Lichan H, Vihan J, Xiaobing L, Hemal S (2016) Wide deep learning for recommender systems. In: Proceedings of the 1st workshop on deep learning for recommender systems DLRS 2016Association for Computing Machinery, New York, NY, USA, pp 7\u201310","DOI":"10.1145\/2988450.2988454"},{"issue":"18","key":"10485_CR7","doi-asserted-by":"publisher","first-page":"6428","DOI":"10.3390\/app10186428","volume":"10","author":"R Thenault","year":"2020","unstructured":"Thenault R, Kaulanjan K, Darde T, Rioux-Leclercq N, Bensalah K, Mermier M, Khene Z, Peyronnet B, Shariat S, Prad\u00e8re B, Mathieu R (2020) The application of artificial intelligence in prostate cancer management\u2014what improvements can be expected? A systematic review. Appl Sci 10(18):6428","journal-title":"Appl Sci"},{"issue":"1","key":"10485_CR8","doi-asserted-by":"publisher","first-page":"74","DOI":"10.3390\/cancers13010074","volume":"13","author":"C-T Wu","year":"2020","unstructured":"Wu C-T, Huang Y-C, Chen W-C, Chen M-F (2020) The predictive role of prostate-specific antigen changes following transurethral resection of the prostate for patients with localized prostate cancer. Cancers 13(1):74","journal-title":"Cancers"},{"issue":"8","key":"10485_CR9","doi-asserted-by":"publisher","first-page":"3226","DOI":"10.1007\/s00330-016-4693-8","volume":"27","author":"RR Wildeboer","year":"2017","unstructured":"Wildeboer RR, Postema WA, Demi L, Kuenen PM, Wijkstra H, Mischi M (2017) Multiparametric dynamic contrast enhanced ultrasound imaging of prostate cancer. Eur Radiol 27(8):3226\u20133234","journal-title":"Eur Radiol"},{"key":"10485_CR10","doi-asserted-by":"publisher","first-page":"212","DOI":"10.1016\/j.media.2017.08.006","volume":"42","author":"X Yang","year":"2017","unstructured":"Yang X, Liu C, Wang Z, Yang J, Min HL, Wang L (2017) Co-trained convolutional neural networks for automated detection of prostate cancer in multi-parametric MRI. Med Image Anal 42:212\u2013227","journal-title":"Med Image Anal"},{"issue":"8","key":"10485_CR11","doi-asserted-by":"publisher","first-page":"532","DOI":"10.3390\/diagnostics10080532","volume":"10","author":"N Papandrianos","year":"2020","unstructured":"Papandrianos N, Papageorgiou E, Anagnostis A, Papageorgiou K (2020) Efficient bone metastasis diagnosis in bone scintigraphy using a fast convolutional neural network architecture. Diagnostics 10(8):532","journal-title":"Diagnostics"},{"issue":"3","key":"10485_CR12","doi-asserted-by":"publisher","first-page":"1154","DOI":"10.3390\/app10031154","volume":"10","author":"J L\u00e9ger","year":"2020","unstructured":"L\u00e9ger J, Brion E, Desbordes P, De Vleeschouwer C, Lee JA, Macq B (2020) Cross-domain data augmentation for deep-learning-based male pelvic organ segmentation in cone beam CT. Appl Sci 10(3):1154","journal-title":"Appl Sci"},{"key":"10485_CR13","doi-asserted-by":"crossref","unstructured":"Sivakumar K, Nithya NS, Revathy O (2019) Phenotype algorithm based big data analytics for cancer diagnose. J Med Syst, vol 43, no 264","DOI":"10.1007\/s10916-019-1409-z"},{"key":"10485_CR14","doi-asserted-by":"publisher","first-page":"125","DOI":"10.1016\/j.compmedimag.2018.08.003","volume":"69","author":"J Li","year":"2018","unstructured":"Li J, Speier W, Ho KC, Sarma KV, Gertych A, Knudsen BS, Arnold CW (2018) An EM-Based semi-suupervised deep learning appproach for semanric segmentation of histopathological images from radical prostatectomies. Comput Med Imaging Graph 69:125\u2013133","journal-title":"Comput Med Imaging Graph"},{"issue":"2","key":"10485_CR15","doi-asserted-by":"publisher","first-page":"343","DOI":"10.1148\/radiol.2017161684","volume":"285","author":"S Verma","year":"2017","unstructured":"Verma S, Choyke PL, Eberhardt SC, Oto A, Tenoat CM, Turkbey B, Rosenkrantz AB (2017) The current state of MR imaging\u2014targeted biopsy techniques for detection of prostate cancer. Radiology 285(2):343\u2013356","journal-title":"Radiology"},{"key":"10485_CR16","doi-asserted-by":"publisher","first-page":"1009","DOI":"10.1007\/s11548-019-01950-0","volume":"14","author":"A Sedghl","year":"2019","unstructured":"Sedghl A, Pestele M, Javadi G, Azizi S, Yan P, Kwak TJ, Xu S, Turkbey B, Choyke P, Wood B, Rohling R, Abolmaesuml P, Mousavi P (2019) Deep neural maps for unsupervised visualization of high grade cancer in prostate biopsies. Int J Comput Assist Radiol Surg 14:1009\u201310016","journal-title":"Int J Comput Assist Radiol Surg"},{"key":"10485_CR17","doi-asserted-by":"publisher","first-page":"60","DOI":"10.1016\/j.compmedimag.2018.08.006","volume":"69","author":"B Abraham","year":"2019","unstructured":"Abraham B, Nair MS (2019) Computer aided classification of prostate cancer grade groups from MRO images using texture features and stacked sparse auto encoder. Comput Med Imaging Graph 69:60\u201368","journal-title":"Comput Med Imaging Graph"},{"issue":"2","key":"10485_CR18","doi-asserted-by":"publisher","first-page":"1293","DOI":"10.1007\/s11548-017-1627-0","volume":"12","author":"S Azizi","year":"2017","unstructured":"Azizi S, Bayat S, Yan P, Tahmasebi A, Nir G, Kwak JT, Xu S, Wilson S, Iczkowski KA, Lucia MS, Goldenberg L, Salcidean SE, Pinto PA, Wood B, Abolmaesumi P, Mousavi P (2017) Detection and grading of prostate cancer using temporal enhanced ultrasound:combining deep neural networks and tissue mimicking simulations. Int J Comput Assist Radiol Surgery 12(2):1293\u20131305","journal-title":"Int J Comput Assist Radiol Surgery"},{"key":"10485_CR19","doi-asserted-by":"crossref","unstructured":"Zhu Q, Du B, Turkbey B, Choyke P, Yan P (2019) Exploiting interslice correlation for MRI prostate image segmentation, from recursive neural network aspect. Complexity, p 10","DOI":"10.1155\/2018\/4185279"},{"key":"10485_CR20","first-page":"73","volume":"3295","author":"R Alkadi","year":"2019","unstructured":"Alkadi R, Taher F, Weghi N (2019) A deep learning based approach for the detection and localization of prostate cancer in T2 magnetic resonance images. J Digital Imag 3295:73\u2013805","journal-title":"J Digital Imag"},{"key":"10485_CR21","doi-asserted-by":"crossref","unstructured":"Xu Y, Jia A, Wang L-B, Ai Y, Zhang F, Lai M, Chang EI-C (2017) Large scale tissue histopathology image classification, segmentation and visualization via deep convolutional activation features. BMC Bioinformatics, vol 18, no 281","DOI":"10.1186\/s12859-017-1685-x"},{"key":"10485_CR22","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1177\/1533034618775530","volume":"17","author":"I Reda","year":"2018","unstructured":"Reda I, Khalil A, Elmogy M, Abou A, Shalaby A, Abou EG (2018) Deep learning role in early diagnosis. Technol Cancer Res Treat 17:1\u201311","journal-title":"Technol Cancer Res Treat"},{"issue":"6","key":"10485_CR23","doi-asserted-by":"publisher","first-page":"1794","DOI":"10.1109\/TCBB.2018.2835444","volume":"16","author":"Y Feng","year":"2018","unstructured":"Feng Y, Yang F, Zhou X, Guo Y, Tang F, Ren F, Gui J, Ji S (2018) A deep learning approach for targeted contrast-enhanced ultrasound based prostate cancer detection. IEEE Trans Comput Biol Bioinf 16(6):1794\u20131801","journal-title":"IEEE Trans Comput Biol Bioinf"},{"issue":"2","key":"10485_CR24","doi-asserted-by":"publisher","first-page":"945","DOI":"10.1177\/1460458219855884","volume":"26","author":"O Eminaga","year":"2019","unstructured":"Eminaga O, Al-Hamad O, Boegemann M, Breil B, Semjonow A (2019) Combination possibility and deep learning model as clinical decision-aided approach for prostate Cancer. Health Informatics J 26(2):945\u2013962","journal-title":"Health Informatics J"},{"key":"10485_CR25","first-page":"101400H","volume":"10140","author":"N Kumar","year":"2017","unstructured":"Kumar N, Verma R, Arora A, Kumar A, Gupta S, Sethi A, Gann PH (2017) Convolutional neural networks for prostate cancer recurrence prediction. Med Imag Digital Pathol 10140:101400H","journal-title":"Med Imag Digital Pathol"},{"issue":"6","key":"10485_CR26","doi-asserted-by":"publisher","first-page":"2000","DOI":"10.3390\/app10062000","volume":"10","author":"J Park","year":"2020","unstructured":"Park J, Rho MJ, Moon HW, Lee JY (2020) Castration-resistant prostate cancer outcome prediction using phased long short-term memory with irregularly sampled serial data. Appl Sci 10(6):2000","journal-title":"Appl Sci"},{"issue":"11","key":"10485_CR27","doi-asserted-by":"publisher","first-page":"959","DOI":"10.3390\/diagnostics10110959","volume":"10","author":"T Kiljunen","year":"2020","unstructured":"Kiljunen T, Akram S, Niemel\u00e4 J, L\u00f6yttyniemi E, Sepp\u00e4l\u00e4 J, Heikkil\u00e4 J, Vuolukka K, K\u00e4\u00e4ri\u00e4inen O-S, Heikkil\u00e4 V-P, Lehti\u00f6 K, Nikkinen J, Gershkevitsh E, Borkvel A, Adamson M, Zolotuhhin D, Kolk K, Pang EPP, Tuan JKL, Master Z, Chua MLK, Joensuu T, Kononen J, Myllykangas M, Riener M, Mokka M, Keyril\u00e4inen J (2020) A deep learning-based automated CT segmentation of prostate cancer anatomy for radiation therapy planning-a retrospective multicenter study. Diagnostics 10(11):959","journal-title":"Diagnostics"},{"key":"10485_CR28","doi-asserted-by":"publisher","first-page":"303","DOI":"10.1016\/j.cmpb.2019.07.003","volume":"178","author":"AE Esteban","year":"2019","unstructured":"Esteban AE, Perez ML, Colomer A, Sales MA (2019) A new optical density granilometry based desciptop for the classification of prostate histological images using shallow and deep gaissian processes. Comput Methods Programs Biomed 178:303\u2013317","journal-title":"Comput Methods Programs Biomed"},{"key":"10485_CR29","unstructured":"Krizhevsky ISGEHA (2012) Imagenet classification with deep convolutional neural networks, Advances in neural information. Adv Neural Information, pp 1097\u20131105"},{"key":"10485_CR30","doi-asserted-by":"crossref","unstructured":"Abu Aas EM, Mousavi P, Aboulmaesumi P (2018) A deep learning approach for real time prostate segmentation in freehand ultrasound guided biopsy. Medical Image Analysis, pp 107\u2013116","DOI":"10.1016\/j.media.2018.05.010"},{"issue":"1","key":"10485_CR31","doi-asserted-by":"publisher","first-page":"76668","DOI":"10.1038\/s41598-019-43989-8","volume":"9","author":"E Arvaniti","year":"2019","unstructured":"Arvaniti E, Fricker KS, Moret M, Rupp N, Hermanns T, Fankhauser C, Wey N, Wild PJ, Ruschoff JH, Claassen M (2019) Automated Gleason grading of prostate cancer tissue microarrays via deep learning. Sci Rep 9(1):76668","journal-title":"Sci Rep"},{"key":"10485_CR32","doi-asserted-by":"crossref","unstructured":"Sivakumar K, Nithya N, Revathy O (2019) Phenotype Algorithm based big data analysis for cancer diagnosis. Image Signal Process, vol 43, no 264","DOI":"10.1007\/s10916-019-1409-z"},{"key":"10485_CR33","doi-asserted-by":"crossref","unstructured":"Schalk S, Demi L, Smeenge M (2014) Three dimensional contrast ultrasound dispersion imaging for prostate cancer localization, a feasibility study. In: 2014 IEEE international ultrasonics symposium, pp 616\u2013619","DOI":"10.1109\/ULTSYM.2014.0151"},{"issue":"3","key":"10485_CR34","doi-asserted-by":"publisher","first-page":"787","DOI":"10.1148\/radiol.13121454","volume":"267","author":"Y Peng","year":"2013","unstructured":"Peng Y (2013) Quantitative analysis of multiparametric prostate MR images: differentiation between prostate cancer and normal tissue and correlation with Gleason score\u2014a computer-aided diagnosis development study. Radiology 267(3):787\u2013796","journal-title":"Radiology"},{"issue":"8","key":"10485_CR35","doi-asserted-by":"publisher","first-page":"1312","DOI":"10.1016\/j.media.2014.04.008","volume":"18","author":"B Fuerst","year":"2014","unstructured":"Fuerst B et al (2014) Automatic ultrasound-MRI registration for neurosurgery using the 2d and 3d LC(2) metric. Med Image Anal 18(8):1312\u20131319","journal-title":"Med Image Anal"}],"container-title":["Neural Processing Letters"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11063-021-10485-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11063-021-10485-y\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11063-021-10485-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,7,21]],"date-time":"2021-07-21T12:13:26Z","timestamp":1626869606000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11063-021-10485-y"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,4,24]]},"references-count":35,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2021,8]]}},"alternative-id":["10485"],"URL":"https:\/\/doi.org\/10.1007\/s11063-021-10485-y","relation":{},"ISSN":["1370-4621","1573-773X"],"issn-type":[{"type":"print","value":"1370-4621"},{"type":"electronic","value":"1573-773X"}],"subject":[],"published":{"date-parts":[[2021,4,24]]},"assertion":[{"value":"1 March 2021","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"24 April 2021","order":2,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}