{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,9]],"date-time":"2026-07-09T06:09:00Z","timestamp":1783577340306,"version":"3.55.0"},"reference-count":72,"publisher":"Springer Science and Business Media LLC","issue":"11","license":[{"start":{"date-parts":[[2022,1,24]],"date-time":"2022-01-24T00:00:00Z","timestamp":1642982400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2022,1,24]],"date-time":"2022-01-24T00:00:00Z","timestamp":1642982400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"name":"National Cancer Institute","award":["1R01CA19572"],"award-info":[{"award-number":["1R01CA19572"]}]},{"DOI":"10.13039\/100007345","name":"University of Arizona Cancer Center","doi-asserted-by":"publisher","award":["3P30CA02307"],"award-info":[{"award-number":["3P30CA02307"]}],"id":[{"id":"10.13039\/100007345","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000001","name":"National Science Foundation","doi-asserted-by":"publisher","award":["DGE-1143"],"award-info":[{"award-number":["DGE-1143"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000015","name":"U.S. Department of Energy","doi-asserted-by":"publisher","award":["DE-NA0003946"],"award-info":[{"award-number":["DE-NA0003946"]}],"id":[{"id":"10.13039\/100000015","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100008982","name":"National Science Foundation","doi-asserted-by":"publisher","award":["1943552"],"award-info":[{"award-number":["1943552"]}],"id":[{"id":"10.13039\/501100008982","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neural Comput & Applic"],"published-print":{"date-parts":[[2022,6]]},"DOI":"10.1007\/s00521-022-06920-3","type":"journal-article","created":{"date-parts":[[2022,1,24]],"date-time":"2022-01-24T18:14:01Z","timestamp":1643048041000},"page":"8977-8987","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":51,"title":["Ovarian cancer detection using optical coherence tomography and convolutional neural networks"],"prefix":"10.1007","volume":"34","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7548-6373","authenticated-orcid":false,"given":"David","family":"Schwartz","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Travis W.","family":"Sawyer","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Noah","family":"Thurston","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jennifer","family":"Barton","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Gregory","family":"Ditzler","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2022,1,24]]},"reference":[{"key":"6920_CR1","unstructured":"US Cancer Statistics Working Group, \u201cUS cancer statistics data visualizations tool, based on november 2018 submission data (1999-2016): US Department of Health and Human services, Centers for Disease Control and Prevention and National Cancer Institute,\u201d Centers for Disease Control and Prevention and National Cancer Institute, 2019"},{"issue":"22","key":"6920_CR2","doi-asserted-by":"publisher","first-page":"2295","DOI":"10.1001\/jama.2011.766","volume":"305","author":"SS Buys","year":"2011","unstructured":"Buys SS, Partridge E, Black A, Johnson CC, Lamerato L, Isaacs C, Reding DJ, Greenlee RT, Yokochi LA, Kessel B et al (2011) Effect of screening on ovarian cancer mortality: the prostate, lung, colorectal and ovarian (plco) cancer screening randomized controlled trial. Jama 305(22):2295\u20132303","journal-title":"Jama"},{"issue":"21","key":"6920_CR3","doi-asserted-by":"publisher","first-page":"1864","DOI":"10.1364\/OL.18.001864","volume":"18","author":"Ea Swanson","year":"1993","unstructured":"Swanson Ea, Izatt Ja, Hee MR, Huang D, Lin CP, Schuman JS, Puliafito Ca, Fujimoto JG (1993) In vivo retinal imaging by optical coherence tomography. Opt Lett 18(21):1864\u20136","journal-title":"Opt Lett"},{"issue":"3","key":"6920_CR4","doi-asserted-by":"publisher","first-page":"325","DOI":"10.1001\/archopht.1995.01100030081025","volume":"113","author":"MR Hee","year":"1995","unstructured":"Hee MR, Izatt JA, Swanson EA, Huang D, Schuman JS, Lin CP, Puliafito CA, Fujimoto JG (1995) Optical coherence tomography of the human retina. Arch Ophthalmol 113(3):325","journal-title":"Arch Ophthalmol"},{"issue":"3","key":"6920_CR5","doi-asserted-by":"publisher","first-page":"169","DOI":"10.1109\/RBME.2010.2084567","volume":"1","author":"M Abr\u00e0moff","year":"2010","unstructured":"Abr\u00e0moff M, Garvin MK, Sonka M (2010) Retinal imaging and image analysis. IEEE Rev Biomed Eng 1(3):169\u2013208","journal-title":"IEEE Rev Biomed Eng"},{"issue":"3","key":"6920_CR6","doi-asserted-by":"publisher","first-page":"387","DOI":"10.1016\/j.lungcan.2005.04.007","volume":"49","author":"M Tsuboi","year":"2005","unstructured":"Tsuboi M, Hayashi A, Ikeda N, Honda H, Kato Y, Ichinose S, Kato H (2005) Optical coherence tomography in the diagnosis of bronchial lesions. Lung Cancer 49(3):387\u2013394","journal-title":"Lung Cancer"},{"key":"6920_CR7","doi-asserted-by":"crossref","unstructured":"Otte S, Otte C, Schlaefer A, Wittig L, H\u00fcttmann G, Dromann D, Zeli A (2013) \u201cOCT A-Scan based lung tumor tissue classification with Bidirectional Long Short Term Memory networks,\u201d In 2013 IEEE International Workshop on Machine Learning for Signal Processing (MLSP), pp. 1\u20136","DOI":"10.1109\/MLSP.2013.6661944"},{"issue":"3","key":"6920_CR8","doi-asserted-by":"publisher","first-page":"549","DOI":"10.1016\/j.giec.2013.03.007","volume":"23","author":"CJ Lightdale","year":"2013","unstructured":"Lightdale CJ (2013) Optical coherence tomography in Barrett\u2019s esophagus. Gastrointest Endosc Clin N Am 23(3):549\u2013563","journal-title":"Gastrointest Endosc Clin N Am"},{"key":"6920_CR9","doi-asserted-by":"crossref","unstructured":"Ferrante G, Presbitero P, Whitbourn R, Barlis P (2013) \u201cCurrent applications of optical coherence tomography for coronary intervention\u201d","DOI":"10.1016\/j.ijcard.2012.02.013"},{"issue":"2","key":"6920_CR10","doi-asserted-by":"publisher","first-page":"1203","DOI":"10.1364\/BOE.8.001203","volume":"8","author":"A Abdolmanafi","year":"2017","unstructured":"Abdolmanafi A, Duong L, Dahdah N, Cheriet F (2017) Deep feature learning for automatic tissue classification of coronary artery using optical coherence tomography. Biomed Opt Express 8(2):1203","journal-title":"Biomed Opt Express"},{"issue":"5","key":"6920_CR11","doi-asserted-by":"publisher","first-page":"438","DOI":"10.4161\/cbt.10.5.12531","volume":"10","author":"LP Hariri","year":"2010","unstructured":"Hariri LP, Liebmann ER, Marion SL, Hoyer PB, Davis JR, Brewer MA, Barton JK (2010) Simultaneous optical coherence tomography and laser induced fluorescence imaging in rat model of ovarian carcinogenesis. Cancer Biol Ther 10(5):438\u2013447","journal-title":"Cancer Biol Ther"},{"issue":"1","key":"6920_CR12","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1002\/wnan.1306","volume":"7","author":"T Wang","year":"2015","unstructured":"Wang T (2015) An overview of optical coherence tomography for ovarian tissue imaging and characterization. Wiley Interdiscip Rev Nanomed Nanobiotechnol 7(1):1\u201316","journal-title":"Wiley Interdiscip Rev Nanomed Nanobiotechnol"},{"issue":"7","key":"6920_CR13","doi-asserted-by":"publisher","first-page":"071412","DOI":"10.1117\/1.JBO.19.7.071412","volume":"19","author":"W Drexler","year":"2014","unstructured":"Drexler W, Liu M, Kumar A, Kamali T, Unterhuber A, Leitgeb RA (2014) Optical coherence tomography today: speed, contrast, and multimodality. J Biomed Opt 19(7):071412","journal-title":"J Biomed Opt"},{"issue":"4","key":"6920_CR14","doi-asserted-by":"publisher","first-page":"1205","DOI":"10.1109\/2944.796348","volume":"5","author":"J Schmitt","year":"1999","unstructured":"Schmitt J (1999) Optical Coherence Tomography (OCT): a review. IEEE J Sel Top Quantum Electron 5(4):1205\u20131215","journal-title":"IEEE J Sel Top Quantum Electron"},{"key":"6920_CR15","doi-asserted-by":"publisher","first-page":"23","DOI":"10.1088\/1361-6560\/aaefd2","volume":"63","author":"T Sawyer","year":"2018","unstructured":"Sawyer T, Chandra S, Rice P, Koevary J, Barton J (2018) Three-dimensional texture analysis of optical coherence tomography images of ovarian tissue. Phys Med Biol 63:23","journal-title":"Phys Med Biol"},{"issue":"2","key":"6920_CR16","doi-asserted-by":"publisher","first-page":"025501","DOI":"10.1117\/1.JMI.1.2.025501","volume":"1","author":"WA Welge","year":"2014","unstructured":"Welge WA, DeMarco AT, Watson JM, Rice PS, Barton JK, Kupinski MA (2014) Diagnostic potential of multimodal imaging of ovarian tissue using optical coherence tomography and second-harmonic generation microscopy. J Med Imag 1(2):025501","journal-title":"J Med Imag"},{"issue":"6","key":"6920_CR17","doi-asserted-by":"publisher","first-page":"617","DOI":"10.1177\/153303460400300612","volume":"3","author":"Ma Brewer","year":"2004","unstructured":"Brewer Ma, Utzinger U, Barton JK, Hoying JB, Kirkpatrick ND, Brands WR, Davis JR, Hunt K, Stevens SJ, Gmitro AF (2004) Imaging of the ovary. Technol Cancer Res Treat 3(6):617\u2013627","journal-title":"Technol Cancer Res Treat"},{"issue":"19","key":"6920_CR18","doi-asserted-by":"publisher","first-page":"6111","DOI":"10.1364\/AO.54.006111","volume":"54","author":"Y Watanabe","year":"2015","unstructured":"Watanabe Y, Takakura K, Kurotani R, Abe H, Atanabe YUW, Akakura KEIT, Urotani REK (2015) Optical coherence tomography imaging for analysis of follicular development in ovarian tissue. App Opt 54(19):6111","journal-title":"App Opt"},{"key":"6920_CR19","first-page":"1047204","volume":"10472","author":"TW Sawyer","year":"2018","unstructured":"Sawyer TW, Rice PF, Sawyer DM, Koevary JW, Barton JK (2018) Evaluation of segmentation algorithms for optical coherence tomography images of ovarian tissue. Diagn Treat Dis Breast Reprod Syst IV 10472:1047204","journal-title":"Diagn Treat Dis Breast Reprod Syst IV"},{"issue":"8","key":"6920_CR20","first-page":"409","volume":"8","author":"W Alakwaa","year":"2017","unstructured":"Alakwaa W, Nassef M, Badr A (2017) Lung cancer detection and classification with 3d convolutional neural network (3d-cnn). Lung Cancer 8(8):409","journal-title":"Lung Cancer"},{"key":"6920_CR21","unstructured":"S. Xingjian, Z. Chen, H. Wang, D.-Y. Yeung, W.-K. Wong, and W.-c. Woo (2015) \u201cConvolutional lstm network: A machine learning approach for precipitation nowcasting,\u201d In Advances in neural information processing systems, 802\u2013810"},{"issue":"3","key":"6920_CR22","doi-asserted-by":"publisher","first-page":"570","DOI":"10.1117\/1.1577575","volume":"8","author":"KW Gossage","year":"2003","unstructured":"Gossage KW, Tkaczyk TS, Rodriguez JJ, Barton JK (2003) Texture analysis of optical coherence tomography images: feasibility for tissue classification. J Biomed Opt 8(3):570\u2013575","journal-title":"J Biomed Opt"},{"issue":"5","key":"6920_CR23","doi-asserted-by":"publisher","first-page":"277","DOI":"10.1016\/0262-8856(92)90042-2","volume":"10","author":"P Miller","year":"1992","unstructured":"Miller P, Astley S (1992) Classification of breast tissue by texture analysis. Image Vis Comput 10(5):277\u2013282","journal-title":"Image Vis Comput"},{"issue":"1","key":"6920_CR24","doi-asserted-by":"publisher","first-page":"2190","DOI":"10.1038\/srep02190","volume":"3","author":"LB Mosta\u00e7o-Guidolin","year":"2013","unstructured":"Mosta\u00e7o-Guidolin LB, Ko AC-T, Wang F, Xiang B, Hewko M, Tian G, Major A, Shiomi M, Sowa MG (2013) Collagen morphology and texture analysis: from statistics to classification. Sci Rep 3(1):2190","journal-title":"Sci Rep"},{"key":"6920_CR25","doi-asserted-by":"crossref","unstructured":"Ran AR, Tham CC, Chan PP, Cheng C-Y, Tham Y-C, Rim TH, Cheung CY (2020) \u201cDeep learning in glaucoma with optical coherence tomography: a review,\u201d Eye","DOI":"10.1038\/s41433-020-01191-5"},{"issue":"11","key":"6920_CR26","doi-asserted-by":"publisher","first-page":"4147","DOI":"10.1167\/iovs.05-0366","volume":"46","author":"Z Burgansky-Eliash","year":"2005","unstructured":"Burgansky-Eliash Z, Wollstein G, Chu T, Ramsey JD, Glymour C, Noecker RJ, Ishikawa H, Schuman JS (2005) Optical coherence tomography machine learning classifiers for glaucoma detection: a preliminary study. Invest Ophthalmol Vis Sci 46(11):4147\u201352","journal-title":"Invest Ophthalmol Vis Sci"},{"key":"6920_CR27","doi-asserted-by":"publisher","first-page":"11","DOI":"10.1167\/tvst.9.2.11","volume":"9","author":"RT Yanagihara","year":"2020","unstructured":"Yanagihara RT, Lee CS, Ting DSW, Lee AY (2020) Methodological challenges of deep learning in optical coherence tomography for retinal diseases: a review. Trans Vision Sci Technol 9:11\u20132","journal-title":"Trans Vision Sci Technol"},{"issue":"3","key":"6920_CR28","doi-asserted-by":"publisher","first-page":"254","DOI":"10.1097\/ICU.0000000000000470","volume":"29","author":"E Rahimy","year":"2018","unstructured":"Rahimy E (2018) Deep learning applications in ophthalmology. Current Opin Ophthalmol 29(3):254\u2013260","journal-title":"Current Opin Ophthalmol"},{"key":"6920_CR29","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s13040-018-0188-2","volume":"12","author":"G Ditzler","year":"2019","unstructured":"Ditzler G, Bouaynaya N, Fathallah Shaykh HM (2019) Sparse kalman filtering for time-varying networks. BMC BioData Min 12:1\u201314","journal-title":"BMC BioData Min"},{"key":"6920_CR30","doi-asserted-by":"publisher","first-page":"265","DOI":"10.1016\/j.sigpro.2017.10.020","volume":"144","author":"G Ditzler","year":"2018","unstructured":"Ditzler G, Bouaynaya N, Shterenberg R (2018) AKRON: an algorithm for approximating sparse kernel reconstruction. Signal Process 144:265\u2013270","journal-title":"Signal Process"},{"key":"6920_CR31","doi-asserted-by":"crossref","unstructured":"Johri A, Tripathi A (2019) et al., \u201cParkinson disease detection using deep neural networks,\u201d In 2019 Twelfth International Conference on Contemporary Computing (IC3), pp. 1\u20134, IEEE","DOI":"10.1109\/IC3.2019.8844941"},{"key":"6920_CR32","doi-asserted-by":"crossref","unstructured":"Yasir R, Rahman MA, Ahmed N (2014) \u201cDermatological disease detection using image processing and artificial neural network,\u201d In 8th International Conference on Electrical and Computer Engineering, pp. 687\u2013690, IEEE","DOI":"10.1109\/ICECE.2014.7026918"},{"key":"6920_CR33","doi-asserted-by":"publisher","first-page":"6497","DOI":"10.1364\/BOE.10.006497","volume":"10","author":"J Lee","year":"2019","unstructured":"Lee J, Prabhu D, Kolluru C, Gharaibeh Y, Zimin VN, Bezerra HG, Wilson DL (2019) Automated plaque characterization using deep learning on coronary intravascular optical coherence tomographic images. Biomed Opt Express 10:6497\u20136515, 11","journal-title":"Biomed Opt Express"},{"key":"6920_CR34","doi-asserted-by":"publisher","first-page":"2596","DOI":"10.1038\/s41598-020-59315-6","volume":"10","author":"J Lee","year":"2020","unstructured":"Lee J, Prabhu D, Kolluru C, Gharaibeh Y, Zimin VN, Dallan LAP, Bezerra HG, Wilson DL (2020) Fully automated plaque characterization in intravascular OCT images using hybrid convolutional and lumen morphology features. Sci Rep 10:2596","journal-title":"Sci Rep"},{"key":"6920_CR35","doi-asserted-by":"crossref","unstructured":"He C, Li Z, Wang J, Huang Y, Yin Y, Li Z (2020) \u201cAtherosclerotic Plaque Tissue Characterization: An OCT-Based Machine Learning Algorithm With ex vivo Validation \u201d","DOI":"10.3389\/fbioe.2020.00749"},{"key":"6920_CR36","doi-asserted-by":"publisher","first-page":"106580","DOI":"10.1016\/j.asoc.2020.106580","volume":"97","author":"M Nour","year":"2020","unstructured":"Nour M, C\u00f6mert Z, Polat K (2020) A novel medical diagnosis model for covid-19 infection detection based on deep features and bayesian optimization. Appl Soft Comput 97:106580","journal-title":"Appl Soft Comput"},{"issue":"1","key":"6920_CR37","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s40537-016-0043-6","volume":"3","author":"K Weiss","year":"2016","unstructured":"Weiss K, Khoshgoftaar TM, Wang D (2016) A survey of transfer learning. J Big Data 3(1):1\u201340","journal-title":"J Big Data"},{"issue":"10","key":"6920_CR38","doi-asserted-by":"publisher","first-page":"1345","DOI":"10.1109\/TKDE.2009.191","volume":"22","author":"SJ Pan","year":"2010","unstructured":"Pan SJ, Yang Q (2010) A survey on transfer learning. IEEE Trans Knowl Data Eng 22(10):1345\u20131359","journal-title":"IEEE Trans Knowl Data Eng"},{"issue":"3","key":"6920_CR39","doi-asserted-by":"publisher","first-page":"211","DOI":"10.1007\/s11263-015-0816-y","volume":"115","author":"O Russakovsky","year":"2015","unstructured":"Russakovsky O, Deng J, Su H, Krause J, Satheesh S, Ma S, Huang Z, Karpathy A, Khosla A, Bernstein M, Berg AC, Fei-Fei L (2015) Imagenet large scale visual recognition challenge. Int J Comput Vision 115(3):211\u2013252","journal-title":"Int J Comput Vision"},{"issue":"6","key":"6920_CR40","first-page":"1389","volume":"63","author":"DC Connolly","year":"2003","unstructured":"Connolly DC, Bao R, Nikitin AY, Stephens KC, Poole TW, Hua X, Harris SS, Vanderhyden BC, Hamilton TC (2003) Female mice chimeric for expression of the simian virus 40 TAg under control of the MISIIR promoter develop epithelial ovarian cancer. Cancer Res. 63(6):1389\u20131397","journal-title":"Cancer Res."},{"issue":"1","key":"6920_CR41","doi-asserted-by":"publisher","first-page":"24","DOI":"10.1186\/1757-2215-3-24","volume":"3","author":"BA Quinn","year":"2010","unstructured":"Quinn BA, Xiao F, Bickel L, Martin L, Hua X, Klein-Szanto A, Connolly DC (2010) Development of a syngeneic mouse model of epithelial ovarian cancer. J Ovarian Res 3(1):24","journal-title":"J Ovarian Res"},{"key":"6920_CR42","doi-asserted-by":"publisher","first-page":"78900W","DOI":"10.1117\/12.875577","volume-title":"Advanced biomedical and clinical diagnostic systems IX","author":"JM Watson","year":"2011","unstructured":"Watson JM, Rice  PF, Marion SL, Bentley DL, Brewer MA, Utzinger U, Hoyer PB,  Barton JK (2011) Multi-modality optical imaging of ovarian cancer in a post-menopausal mouse model. In: Advanced biomedical and clinical diagnostic systems IX, vol 7890. International Society for Optics and Photonics, p 78900W"},{"issue":"9","key":"6920_CR43","doi-asserted-by":"publisher","first-page":"096010","DOI":"10.1117\/1.JBO.24.9.096010","volume":"24","author":"T Sawyer","year":"2019","unstructured":"Sawyer T, Koevary J, Rice F, Howard C, Austin O, Connolly D, Cai K, Barton J (2019) Quantification of multiphoton and fluorescence images of reproductive tissues from a mouse ovarian cancer model shows promise for early disease detection. J Biomed Opt 24(9):096010","journal-title":"J Biomed Opt"},{"issue":"11","key":"6920_CR44","doi-asserted-by":"publisher","first-page":"2278","DOI":"10.1109\/5.726791","volume":"86","author":"Y LeCun","year":"1998","unstructured":"LeCun Y, Bottou L, Bengio Y, Haffner P (1998) Gradient-based learning applied to document recognition. Proc IEEE 86(11):2278\u20132324","journal-title":"Proc IEEE"},{"key":"6920_CR45","doi-asserted-by":"crossref","unstructured":"Jaworek-Korjakowska J, Kleczek P, Gorgon M (2019) \u201cMelanoma thickness prediction based on convolutional neural network with vgg-19 model transfer learning,\u201d In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, pp. 0\u20130","DOI":"10.1109\/CVPRW.2019.00333"},{"key":"6920_CR46","unstructured":"Simonyan K, Zisserman A (2014) \u201cVery deep convolutional networks for large-scale image recognition,\u201d arXiv preprint arXiv:1409.1556"},{"key":"6920_CR47","doi-asserted-by":"crossref","unstructured":"Deng J, Dong W, Socher R, Li L-J, Li K, Fei-Fei L (2009) \u201cImagenet: A large-scale hierarchical image database,\u201d In 2009 IEEE conference on computer vision and pattern recognition, pp. 248\u2013255, Ieee","DOI":"10.1109\/CVPR.2009.5206848"},{"issue":"6","key":"6920_CR48","doi-asserted-by":"publisher","first-page":"770","DOI":"10.1109\/TKDE.2010.31","volume":"22","author":"EW Xiang","year":"2010","unstructured":"Xiang EW, Cao B, Hu DH, Yang Q (2010) Bridging domains using world wide knowledge for transfer learning. IEEE Trans Knowl Data Eng 22(6):770\u2013783","journal-title":"IEEE Trans Knowl Data Eng"},{"issue":"2","key":"6920_CR49","doi-asserted-by":"publisher","first-page":"199","DOI":"10.1109\/TNN.2010.2091281","volume":"22","author":"S Pan","year":"2011","unstructured":"Pan S, Tsang I, Kwok J, Yang Q (2011) Domain adaptation via transfer component analysis. IEEE Trans Neural Netw 22(2):199\u2013210","journal-title":"IEEE Trans Neural Netw"},{"key":"6920_CR50","first-page":"1433","volume-title":"NIPS","author":"G Schweikert","year":"2008","unstructured":"Schweikert G, Widmer C, Sch\u00f6lkopf B, R\u00e4tsch G (2008) An empirical analysis of domain adaptation algorithms for genomic sequence analysis. In: NIPS, vol 8. Citeseer, pp 1433\u20131440"},{"key":"6920_CR51","doi-asserted-by":"crossref","unstructured":"Ahmed A, Yu K, Xu W, Gong Y, Xing E (2008) \u201cTraining hierarchical feed-forward visual recognition models using transfer learning from pseudo-tasks,\u201d In European Conference on Computer Vision, pp. 69\u201382","DOI":"10.1007\/978-3-540-88690-7_6"},{"key":"6920_CR52","doi-asserted-by":"crossref","unstructured":"Guo J, Liang Z, Scribner E, Ditzler G, Bouaynaya N, Fathallah-Shaykh H (2018) \u201cNonlinear brain tumor model estimation with long short-term memory neural networks,\u201d In IEEE\/INNS International Joint Conference on Neural Networks","DOI":"10.1109\/IJCNN.2018.8489616"},{"key":"6920_CR53","unstructured":"Zhang Z, Sabuncu M (2018) \u201cGeneralized cross entropy loss for training deep neural networks with noisy labels,\u201d In Advances in neural information processing systems, pp. 8778\u20138788"},{"key":"6920_CR54","doi-asserted-by":"crossref","unstructured":"Wang Y, Ma X, Chen Z, Luo Y, Yi J, Bailey J (2019) \u201cSymmetric cross entropy for robust learning with noisy labels,\u201d In Proceedings of the IEEE International Conference on Computer Vision, pp. 322\u2013330","DOI":"10.1109\/ICCV.2019.00041"},{"key":"6920_CR55","doi-asserted-by":"crossref","unstructured":"Gers FA, Schmidhuber J, Cummins F (1999) \u201cLearning to forget: Continual prediction with lstm,\u201d 1999 Ninth International Conference on Artificial Neural Networks ICANN 99. (Conf. Publ. No. 470)","DOI":"10.1049\/cp:19991218"},{"key":"6920_CR56","doi-asserted-by":"crossref","unstructured":"Luo W, Liu W, Gao S (2017) \u201cRemembering history with convolutional lstm for anomaly detection,\u201d In 2017 IEEE International Conference on Multimedia and Expo (ICME), pp. 439\u2013444, IEEE","DOI":"10.1109\/ICME.2017.8019325"},{"key":"6920_CR57","doi-asserted-by":"crossref","unstructured":"Graves A, Fern\u00e1ndez S, Schmidhuber J (2005) \u201cBidirectional lstm networks for improved phoneme classification and recognition,\u201d In International Conference on Artificial Neural Networks, pp. 799\u2013804, Springer","DOI":"10.1007\/11550907_126"},{"key":"6920_CR58","unstructured":"Clevert D-A, Unterthiner T, Hochreiter S (2015) \u201cFast and accurate deep network learning by exponential linear units (elus),\u201d arXiv preprint arXiv:1511.07289"},{"key":"6920_CR59","doi-asserted-by":"crossref","unstructured":"Mehta D, Rhodin H, Casas D, Fua P, Sotnychenko O, Xu W, Theobalt C (2017) \u201cMonocular 3d human pose estimation in the wild using improved cnn supervision,\u201d In 2017 international conference on 3D vision (3DV), pp. 506\u2013516, IEEE","DOI":"10.1109\/3DV.2017.00064"},{"issue":"5","key":"6920_CR60","doi-asserted-by":"publisher","first-page":"2063","DOI":"10.1002\/mp.12837","volume":"45","author":"X Ren","year":"2018","unstructured":"Ren X, Xiang L, Nie D, Shao Y, Zhang H, Shen D, Wang Q (2018) Interleaved 3d-cnn s for joint segmentation of small-volume structures in head and neck ct images. Med Phys 45(5):2063\u20132075","journal-title":"Med Phys"},{"issue":"1","key":"6920_CR61","first-page":"1929","volume":"15","author":"N Srivastava","year":"2014","unstructured":"Srivastava N, Hinton G, Krizhevsky A, Sutskever I, Salakhutdinov R (2014) Dropout: a simple way to prevent neural networks from overfitting. J Mach Learn Res 15(1):1929\u20131958","journal-title":"J Mach Learn Res"},{"key":"6920_CR62","unstructured":"Dozat T (2016) \u201cIncorporating nesterov momentum into adam,\u201d International Conference on Learning Representations (ICLR)"},{"key":"6920_CR63","unstructured":"Santurkar S, Tsipras D, Ilyas A, Madry A (2018) \u201cHow does batch normalization help optimization,\u201d In Advances in Neural Information Processing Systems, pp. 2483\u20132493"},{"key":"6920_CR64","unstructured":"Zeile MD (2012) \u201cAdadelta: an adaptive learning rate method,\u201d arXiv preprint arXiv:1212.5701"},{"key":"6920_CR65","unstructured":"Ruder S (2016) \u201cAn overview of gradient descent optimization algorithms,\u201d arXiv preprint arXiv:1609.04747"},{"issue":"1","key":"6920_CR66","doi-asserted-by":"publisher","first-page":"17","DOI":"10.1016\/0893-6080(88)90021-4","volume":"1","author":"S Grossberg","year":"1988","unstructured":"Grossberg S (1988) Nonlinear neural networks: principles, mechanisms, and architectures. Neural Netw 1(1):17\u201361","journal-title":"Neural Netw"},{"key":"6920_CR67","doi-asserted-by":"publisher","first-page":"861","DOI":"10.1016\/j.patrec.2005.10.010","volume":"27","author":"T Fawcett","year":"2006","unstructured":"Fawcett T (2006) An introduction to ROC analysis. Pattern Recognit Lett 27:861\u2013874","journal-title":"Pattern Recognit Lett"},{"issue":"10","key":"6920_CR68","doi-asserted-by":"publisher","first-page":"993","DOI":"10.1002\/lsm.23251","volume":"52","author":"TW Sawyer","year":"2020","unstructured":"Sawyer TW, Koevary JW, Howard CC, Austin OJ, Rice PF, Hutchens GV, Chambers SK, Connolly DC, Barton JK (2020) Fluorescence and multiphoton imaging for tissue characterization of a model of postmenopausal ovarian cancer. Lasers Surg Med 52(10):993\u20131009","journal-title":"Lasers Surg Med"},{"key":"6920_CR69","first-page":"1085605","volume":"10856","author":"TW Sawyer","year":"2019","unstructured":"Sawyer TW, Rice FF, Koevary JW, Connolly DC, Cai KQ, Barton JK (2019) In vivo multiphoton imaging of an ovarian cancer mouse model. Dis Breast Reprod Syst V 10856:1085605","journal-title":"Dis Breast Reprod Syst V"},{"issue":"23","key":"6920_CR70","doi-asserted-by":"publisher","first-page":"235020","DOI":"10.1088\/1361-6560\/aaefd2","volume":"63","author":"TW Sawyer","year":"2018","unstructured":"Sawyer TW, Chandra S, Rice PF, Koevary JW, Barton JK (2018) Three-dimensional texture analysis of optical coherence tomography images of ovarian tissue. Phys Med Biol 63(23):235020","journal-title":"Phys Med Biol"},{"issue":"12","key":"6920_CR71","doi-asserted-by":"publisher","first-page":"5182","DOI":"10.1364\/BOE.7.005182","volume":"7","author":"S Nandy","year":"2016","unstructured":"Nandy S, Sanders M, Zhu Q (2016) Classification and analysis of human ovarian tissue using full field optical coherence tomography. Biomed Opt Express 7(12):5182\u20135187","journal-title":"Biomed Opt Express"},{"issue":"16","key":"6920_CR72","doi-asserted-by":"publisher","first-page":"5882","DOI":"10.1158\/0008-5472.CAN-04-0746","volume":"64","author":"Z Zhang","year":"2004","unstructured":"Zhang Z, Bast RC, Yu Y, Li J, Sokoll LJ, Rai AJ, Rosenzweig JM, Cameron B, Wang YY, Meng X-Y et al (2004) Three biomarkers identified from serum proteomic analysis for the detection of early stage ovarian cancer. Cancer Res 64(16):5882\u20135890","journal-title":"Cancer Res"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-022-06920-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00521-022-06920-3\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-022-06920-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,4,9]],"date-time":"2025-04-09T10:59:27Z","timestamp":1744196367000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00521-022-06920-3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,1,24]]},"references-count":72,"journal-issue":{"issue":"11","published-print":{"date-parts":[[2022,6]]}},"alternative-id":["6920"],"URL":"https:\/\/doi.org\/10.1007\/s00521-022-06920-3","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"value":"0941-0643","type":"print"},{"value":"1433-3058","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,1,24]]},"assertion":[{"value":"16 June 2021","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"4 January 2022","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"24 January 2022","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 have no conflicts of interest to report.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflicts of interest"}},{"value":"All experiments were performed per NIH guidelines, and protocols were approved by the University of Arizona Institutional Animal Care and Use Committee under protocol 06-183.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval"}}]}}