{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,22]],"date-time":"2026-04-22T16:16:30Z","timestamp":1776874590800,"version":"3.51.2"},"reference-count":65,"publisher":"Springer Science and Business Media LLC","issue":"2","license":[{"start":{"date-parts":[[2022,12,11]],"date-time":"2022-12-11T00:00:00Z","timestamp":1670716800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2022,12,11]],"date-time":"2022-12-11T00:00:00Z","timestamp":1670716800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100017621","name":"Shenzhen Key Laboratory Foundation","doi-asserted-by":"crossref","award":["ZDSYS20200811143757022"],"award-info":[{"award-number":["ZDSYS20200811143757022"]}],"id":[{"id":"10.13039\/501100017621","id-type":"DOI","asserted-by":"crossref"}]},{"name":"Key-Area Research and Development Program of Guangdong Province","award":["2021B0101420005"],"award-info":[{"award-number":["2021B0101420005"]}]},{"name":"Key Technology Development Program of Shenzhen","award":["JSGG20210713091811036"],"award-info":[{"award-number":["JSGG20210713091811036"]}]},{"DOI":"10.13039\/501100010226","name":"Department of Education of Guangdong Province","doi-asserted-by":"publisher","award":["2017KZDXM072"],"award-info":[{"award-number":["2017KZDXM072"]}],"id":[{"id":"10.13039\/501100010226","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61601302"],"award-info":[{"award-number":["61601302"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100017621","name":"Shenzhen Key Laboratory Foundation","doi-asserted-by":"crossref","award":["ZDSYS20200811143757022"],"award-info":[{"award-number":["ZDSYS20200811143757022"]}],"id":[{"id":"10.13039\/501100017621","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100012234","name":"Shenzhen Peacock Plan","doi-asserted-by":"publisher","award":["KQTD2016053112051497"],"award-info":[{"award-number":["KQTD2016053112051497"]}],"id":[{"id":"10.13039\/501100012234","id-type":"DOI","asserted-by":"publisher"}]},{"name":"SZU Top Ranking Project","award":["86000000210"],"award-info":[{"award-number":["86000000210"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Med Biol Eng Comput"],"published-print":{"date-parts":[[2023,2]]},"DOI":"10.1007\/s11517-022-02711-z","type":"journal-article","created":{"date-parts":[[2022,12,11]],"date-time":"2022-12-11T03:02:12Z","timestamp":1670727732000},"page":"457-473","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Expectation-maximization algorithm leads to domain adaptation for a perineural invasion and nerve extraction task in whole slide digital pathology images"],"prefix":"10.1007","volume":"61","author":[{"given":"Xue","family":"Li","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jun","family":"Huang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Cuiting","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaxia","family":"Yu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tianhao","family":"Zhao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chuan","family":"Huang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0094-8136","authenticated-orcid":false,"given":"Yi","family":"Gao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2022,12,11]]},"reference":[{"key":"2711_CR1","doi-asserted-by":"crossref","unstructured":"Bray F, Ferlay J, Soerjomataram I, Siegel RL (2018) Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. C: A cancer Journal for Clinicians","DOI":"10.3322\/caac.21492"},{"key":"2711_CR2","doi-asserted-by":"crossref","unstructured":"Mete MMM (2006) Head and neck cancer detection in histopathological slides. In: IEEE international conference on data mining-workshops","DOI":"10.1109\/ICDMW.2006.90"},{"key":"2711_CR3","doi-asserted-by":"crossref","unstructured":"Ang K, Smith B, Haffty B (2009) Prognostic factors in patients with head and neck cancer. Head and Neck Cancer: Multidiscip Approach, 51\u201375","DOI":"10.1016\/S1041-892X(08)79300-4"},{"issue":"2","key":"2711_CR4","doi-asserted-by":"publisher","first-page":"104","DOI":"10.1002\/jso.21190","volume":"99","author":"S Sethi","year":"2010","unstructured":"Sethi S, Lu M, Kapke A, Benninger MS, Worsham MJ (2010) Patient and tumor factors at diagnosis in a multi-ethnic primary head and neck squamous cell carcinoma cohort. J Surg Oncol 99(2):104","journal-title":"J Surg Oncol"},{"issue":"10","key":"2711_CR5","doi-asserted-by":"crossref","first-page":"1145","DOI":"10.1288\/00005537-198610000-00015","volume":"96","author":"K Soo","year":"2010","unstructured":"Soo K, Carter RL, O\u2019Brien CJ, Barr L, Shaw HJ (2010) Prognostic implications of perineural spread in squamous carcinomas of the head and neck. Laryngoscope 96(10):1145","journal-title":"Laryngoscope"},{"issue":"1","key":"2711_CR6","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1186\/s12894-018-0319-6","volume":"18","author":"LJ Zhang","year":"2018","unstructured":"Zhang LJ, Wu B, Zha ZL, Qu W, Zhao H, Yuan J, Feng YJ (2018) Perineural invasion as an independent predictor of biochemical recurrence in prostate cancer following radical prostatectomy or radiotherapy: a systematic review and meta-analysis. BMC Urol 18(1):5","journal-title":"BMC Urol"},{"issue":"10","key":"2711_CR7","doi-asserted-by":"publisher","first-page":"630","DOI":"10.1136\/jclinpath-2019-206300","volume":"73","author":"P Str\u00f6m","year":"2020","unstructured":"Str\u00f6m P, Nordstr\u00f6m T, Delahunt B, Samaratunga H, Gr\u00f6nberg H, Egevad L, Eklund M (2020) Prognostic value of perineural invasion in prostate needle biopsies: a population-based study of patients treated by radical prostatectomy. J Clin Pathol 73(10):630\u2013635. https:\/\/doi.org\/10.1136\/jclinpath-2019-206300, https:\/\/europepmc.org\/articles\/PMC7513266","journal-title":"J Clin Pathol"},{"issue":"15","key":"2711_CR8","doi-asserted-by":"publisher","first-page":"3379","DOI":"10.1002\/cncr.24396","volume":"115","author":"C Liebig","year":"2009","unstructured":"Liebig C, Ayala G, Wilks JA, Berger DH, Albo D (2009) Perineural invasion in cancer. Cancer 115(15):3379. https:\/\/doi.org\/10.1002\/cncr.24396, https:\/\/acsjournals.onlinelibrary.wiley.com\/doi\/abs\/10.1002\/cncr.24396","journal-title":"Cancer"},{"key":"2711_CR9","doi-asserted-by":"crossref","unstructured":"Aurello P, Berardi G, Tierno SM, Vinciguerra GLR, Socciarelli F, Laracca GG, Giulitti D, Pilozzi E, Ramacciato G (2017) Influence of perineural invasion in predicting overall survival and disease-free survival in patients with locally advanced gastric cancer. The American Journal of Surgery","DOI":"10.1016\/j.amjsurg.2016.05.022"},{"issue":"2","key":"2711_CR10","doi-asserted-by":"publisher","first-page":"181","DOI":"10.1097\/DCR.0000000000001258","volume":"62","author":"M Skancke","year":"2019","unstructured":"Skancke M, Arnott SM, Amdur RL, Siegel RS, Obias VJ, Umapathi BA (2019) Lymphovascular invasion and perineural invasion negatively impact overall survival for stage II adenocarcinoma of the colon. Dis Colon Rectum 62(2):181","journal-title":"Dis Colon Rectum"},{"issue":"3","key":"2711_CR11","doi-asserted-by":"publisher","first-page":"550","DOI":"10.1002\/1097-0142(19940201)73:3<550::AID-CNCR2820730309>3.0.CO;2-0","volume":"73","author":"A Tanaka","year":"1994","unstructured":"Tanaka A, Watanabe T, Okuno K, Yasutomi M (1994) Perineural invasion as a predictor of recurrence of gastric cancer. Cancer 73(3):550","journal-title":"Cancer"},{"issue":"3","key":"2711_CR12","doi-asserted-by":"publisher","first-page":"409","DOI":"10.1097\/PAS.0b013e31824104c5","volume":"36","author":"D Chatterjee","year":"2012","unstructured":"Chatterjee D, Katz MH, Rashid A, Wang H, Iuga AC, Varadhachary GR, Wolff RA, Lee JE, Pisters PW, Crane CH (2012) Perineural and intraneural invasion in posttherapy pancreaticoduodenectomy specimens predicts poor prognosis in patients with pancreatic ductal adenocarcinoma. Am J Surg Pathol 36(3):409","journal-title":"Am J Surg Pathol"},{"key":"2711_CR13","doi-asserted-by":"crossref","unstructured":"Zhao B, Lv W, Mei D, Luo R, Lin J (2020) Perineural invasion as a predictive factor for survival outcome in gastric cancer patients: a systematic review and meta-analysis. Journal of Clinical Pathology pp. jclinpath\u20132019\u2013206,372","DOI":"10.1136\/jclinpath-2019-206372"},{"issue":"17","key":"2711_CR14","doi-asserted-by":"publisher","first-page":"6082","DOI":"10.1158\/0008-5472.CAN-04-0838","volume":"64","author":"EG Ayala","year":"2004","unstructured":"Ayala EG (2004) Growth and survival mechanisms associated with perineural invasion in prostate cancer. Cancer Res 64(17):6082","journal-title":"Cancer Res"},{"issue":"1","key":"2711_CR15","doi-asserted-by":"publisher","first-page":"70","DOI":"10.1097\/MPA.0000000000001194","volume":"48","author":"C Luchini","year":"2019","unstructured":"Luchini C, Veronese N, Nottegar A, Riva G, Pilati C, Mafficini A, Stubbs B, Simbolo M, Mombello A, Corbo VA (2019) Perineural invasion is a strong prognostic moderator in ampulla of vater carcinoma. Pancreas 48(1):70","journal-title":"Pancreas"},{"issue":"6","key":"2711_CR16","doi-asserted-by":"publisher","first-page":"637","DOI":"10.1001\/archotol.124.6.637","volume":"124","author":"JJ Fagan","year":"1998","unstructured":"Fagan JJ, Collins B, Barnes L, D\u201dAmico F, Myers EN, Johnson JT (1998) Perineural invasion in squamous cell carcinoma of the head and neck. Arch Otolaryngol Head Neck Surg 124(6):637","journal-title":"Arch Otolaryngol Head Neck Surg"},{"issue":"1","key":"2711_CR17","doi-asserted-by":"publisher","first-page":"96","DOI":"10.1016\/j.adro.2018.09.006","volume":"4","author":"RD Kraus","year":"2019","unstructured":"Kraus RD, Barsky A, Ji L, Garcia Santos PM, Cheng N, Groshen S, Vapiwala N, Ballas LK (2019) The perineural invasion paradox: Is perineural invasion an independent prognostic indicator of biochemical recurrence risk in patients with pT2N0R0 prostate cancer? A multi-institutional study. Adv Radiat Oncol 4(1):96","journal-title":"Adv Radiat Oncol"},{"key":"2711_CR18","unstructured":"Bulten W, Balkenhol M, Belinga JJA, Brilhante A, Litjens G (2020) Artificial intelligence assistance significantly improves Gleason grading of prostate biopsies by pathologists. Modern Pathology"},{"key":"2711_CR19","unstructured":"Egevad L, Swanberg D, Delahunt B, Strm P, Eklund M (2020) Identification of areas of grading difficulties in prostate cancer and comparison with artificial intelligence assisted grading. Archiv f\u00fcr Pathologische Anatomie und Physiologie und f\u00fcr Klinische Medicin (6)"},{"issue":"1","key":"2711_CR20","doi-asserted-by":"publisher","first-page":"299","DOI":"10.1016\/j.patcog.2009.05.012","volume":"43","author":"HD Cheng","year":"2010","unstructured":"Cheng HD, Shan J, Ju W, Guo Y, Zhang L (2010) Automated breast cancer detection and classification using ultrasound images: a survey. Pattern Recognit 43(1):299","journal-title":"Pattern Recognit"},{"issue":"3","key":"2711_CR21","doi-asserted-by":"publisher","first-page":"990","DOI":"10.1016\/j.eswa.2014.09.020","volume":"42","author":"R Rouhi","year":"2015","unstructured":"Rouhi R, Jafari M, Kasaei S, Keshavarzian P (2015) Benign and malignant breast tumors classification based on region growing and CNN segmentation. Expert Syst Appl 42(3):990","journal-title":"Expert Syst Appl"},{"issue":"4","key":"2711_CR22","doi-asserted-by":"publisher","first-page":"539","DOI":"10.1016\/j.ultrasmedbio.2011.01.006","volume":"37","author":"WK Moon","year":"2011","unstructured":"Moon WK, Shen YW, Huang C, Chiang LR, Chang R (2011) Computer-aided diagnosis for the classification of breast masses in automated whole breast ultrasound images. Ultrasound Med Biol 37 (4):539","journal-title":"Ultrasound Med Biol"},{"key":"2711_CR23","first-page":"1","volume":"2017","author":"D Wei","year":"2017","unstructured":"Wei D, Liangping L, Xiaoyi L, Tianqi F, Dexiang L, Guo D, Hanwei C (2017) Head and neck cancer tumor segmentation using support vector machine in dynamic contrast-enhanced MRI. Contrast Media Mol Imaging 2017:1","journal-title":"Contrast Media Mol Imaging"},{"key":"2711_CR24","doi-asserted-by":"publisher","first-page":"104129","DOI":"10.1016\/j.compbiomed.2020.104129","volume":"128","author":"M Salvi","year":"2021","unstructured":"Salvi M, Acharya UR, Molinari F, Meiburger KM (2021) The impact of pre- and post-image processing techniques on deep learning frameworks: a comprehensive review for digital pathology image analysis. Comput Biol Med 128:104129","journal-title":"Comput Biol Med"},{"key":"2711_CR25","unstructured":"Alom MZ, Aspiras TH, Taha TM, Asari VK, Bowen TJ, Billiter D, Arkell S (2019) Advanced deep convolutional neural network approaches for digital pathology image analysis: a comprehensive evaluation with different use cases. arXiv:1904.09075"},{"key":"2711_CR26","doi-asserted-by":"publisher","first-page":"147","DOI":"10.1109\/RBME.2009.2034865","volume":"2","author":"MN Gurcan","year":"2009","unstructured":"Gurcan MN, Boucheron LE, Can A, Madabhushi A, Rajpoot NM, Yener B (2009) Histopathological image analysis: a review. IEEE Rev Biomed Eng 2:147","journal-title":"IEEE Rev Biomed Eng"},{"key":"2711_CR27","doi-asserted-by":"publisher","unstructured":"Zhou N, Gao Y (2017) Optimized color decomposition of localized whole slide images and convolutional neural network for intermediate prostate cancer classification. In: Society of photo-optical instrumentation engineers (SPIE) conference series. https:\/\/doi.org\/10.1117\/12.2254216, vol 10140, p 101400W","DOI":"10.1117\/12.2254216"},{"key":"2711_CR28","unstructured":"Naik S, Doyle S, Madabhushi A, Tomaszewski JE, Feldman MD, Automated gland segmentation and Gleason grading of prostate histology by integrating low- high-level and domain specific information (2007). In: Workshop on microscopic image analysis with applications in biology, Piscataway"},{"key":"2711_CR29","doi-asserted-by":"publisher","first-page":"489","DOI":"10.1016\/j.media.2016.08.008","volume":"35","author":"K Sirinukunwattana","year":"2017","unstructured":"Sirinukunwattana K, Pluim JP, Chen H, Qi X, Heng PA, Guo YB, Wang LY, Matuszewski BJ, Bruni E, Sanchez U, B\u00f6hm A., Ronneberger O, Cheikh BB, Racoceanu D, Kainz P, Pfeiffer M, Urschler M, Snead DR, Rajpoot NM (2017) Gland segmentation in colon histology images: the glas challenge contest. Med Image Anal 35:489. https:\/\/doi.org\/10.1016\/j.media.2016.08.008, https:\/\/www.sciencedirect.com\/science\/article\/pii\/S1361841516301542","journal-title":"Med Image Anal"},{"key":"2711_CR30","doi-asserted-by":"publisher","unstructured":"Giusti A, Cire\u015fan DC, Masci J, Gambardella LM, Schmidhuber J (2013) Fast image scanning with deep max-pooling convolutional neural networks. In: 2013 IEEE international conference on image processing, pp 4034\u20134038. https:\/\/doi.org\/10.1109\/ICIP.2013.6738831","DOI":"10.1109\/ICIP.2013.6738831"},{"key":"2711_CR31","doi-asserted-by":"publisher","unstructured":"Su H, Liu F, Xie Y, Xing F, Meyyappan S, Yang L (2015) Region segmentation in histopathological breast cancer images using deep convolutional neural network. In: 2015 IEEE 12th international symposium on biomedical imaging (ISBI), pp 55\u201358. https:\/\/doi.org\/10.1109\/ISBI.2015.7163815","DOI":"10.1109\/ISBI.2015.7163815"},{"key":"2711_CR32","unstructured":"Wang D, Khosla A, Gargeya R, Irshad H, Beck AH (2016) Deep learning for identifying metastatic breast cancer. arXiv:1606.05718"},{"key":"2711_CR33","doi-asserted-by":"crossref","unstructured":"Duong QD, Vu DQ, Lee D, Hewitt SM, Kwak JT (2019) Scale embedding shared neural networks for multiscale histological analysis of prostate cancer. In: Digital pathology","DOI":"10.1117\/12.2512807"},{"issue":"4","key":"2711_CR34","doi-asserted-by":"publisher","first-page":"945","DOI":"10.1109\/TMI.2018.2875868","volume":"38","author":"W Li","year":"2019","unstructured":"Li W, Li J, Sarma KV, Ho KC, Shen S, Knudsen BS, Gertych A, Arnold CW (2019) Path R-CNN for prostate cancer diagnosis and Gleason grading of histological images. IEEE Trans Med Imaging 38(4):945. https:\/\/doi.org\/10.1109\/TMI.2018.2875868","journal-title":"IEEE Trans Med Imaging"},{"key":"2711_CR35","doi-asserted-by":"publisher","first-page":"19518","DOI":"10.1038\/s41598-019-55972-4","volume":"9","author":"S Yoo","year":"2019","unstructured":"Yoo S, Gujrathi I, Haider M, Khalvati F (2019) Prostate cancer detection using deep convolutional neural networks. Sci Rep 9:19518. https:\/\/doi.org\/10.1038\/s41598-019-55972-4","journal-title":"Sci Rep"},{"key":"2711_CR36","doi-asserted-by":"crossref","unstructured":"Cao R, Zhong X, Shakeri S, Bajgiran AM, Sung K (2019) Prostate cancer detection and segmentation in multi-parametric MRI via CNN and conditional random field. In: 2019 IEEE 16th international symposium on biomedical imaging (ISBI)","DOI":"10.1109\/ISBI.2019.8759584"},{"key":"2711_CR37","doi-asserted-by":"publisher","unstructured":"Zhao B, Soraghan J, Caterina GD, Grose D (2019) Segmentation of head and neck tumours using modified U-net. In: 2019 27th European signal processing conference (EUSIPCO), pp 1\u20134. https:\/\/doi.org\/10.23919\/EUSIPCO.2019.8902637","DOI":"10.23919\/EUSIPCO.2019.8902637"},{"issue":"99","key":"2711_CR38","first-page":"1","volume":"PP","author":"Y Wang","year":"2019","unstructured":"Wang Y, Zhao L, Song Z, Wang M (2019) Organ at risk segmentation in head and neck CT images by using a two-stage segmentation framework based on 3D U-Net. IEEE Access PP(99):1","journal-title":"IEEE Access"},{"issue":"7","key":"2711_CR39","doi-asserted-by":"publisher","first-page":"1","DOI":"10.3109\/0284186X.2016.1173723","volume":"55","author":"JY Lim","year":"2016","unstructured":"Lim JY, Leech M (2016) Use of auto-segmentation in the delineation of target volumes and organs at risk in head and neck. Acta Oncol 55(7):1","journal-title":"Acta Oncol"},{"key":"2711_CR40","doi-asserted-by":"crossref","unstructured":"Tappeiner E, Pr\u00f6ll S, H\u00f6nig M, Raudaschl PF, Zaffino P, Spadea MF, Sharp GC, Schubert R, Fritscher K (2019) Multi-organ segmentation of the head and neck area: an efficient hierarchical neural networks approach. International Journal of Computer Assisted Radiology and Surgery","DOI":"10.1007\/s11548-019-01922-4"},{"issue":"7","key":"2711_CR41","doi-asserted-by":"publisher","first-page":"1012","DOI":"10.3390\/biom10071012","volume":"10","author":"N Yamato","year":"2020","unstructured":"Yamato N, Matsuya M, Niioka H, Miyake J, Hashimoto M (2020) Nerve segmentation with deep learning from label-free endoscopic images obtained using coherent anti-stokes raman scattering. Biomolecules 10(7):1012","journal-title":"Biomolecules"},{"key":"2711_CR42","doi-asserted-by":"crossref","unstructured":"Wang R, Shen H, Zhou M (2019) Ultrasound nerve segmentation of brachial plexus based on optimized ResU-Net. In: 2019 IEEE international conference on imaging systems and techniques (IST)","DOI":"10.1109\/IST48021.2019.9010317"},{"key":"2711_CR43","doi-asserted-by":"publisher","first-page":"406","DOI":"10.1016\/j.wneu.2016.06.008","volume":"95","author":"P Dolati","year":"2016","unstructured":"Dolati P, Eichberg D, Golby A, Zamani A, Laws E (2016) Multimodal navigation in endoscopic transsphenoidal resection of pituitary tumors using image-based vascular and cranial nerve segmentation: a prospective validation study. World Neurosurg 95:406","journal-title":"World Neurosurg"},{"issue":"1","key":"2711_CR44","doi-asserted-by":"publisher","first-page":"103","DOI":"10.1097\/PAS.0000000000000518","volume":"40","author":"N Knijn","year":"2016","unstructured":"Knijn N, Mogk SC, Teerenstra S, Simmer F, Nagtegaal ID (2016) Perineural invasion is a strong prognostic factor in colorectal cancer: A systematic review. Am J Surg Pathol 40(1):103","journal-title":"Am J Surg Pathol"},{"key":"2711_CR45","doi-asserted-by":"crossref","unstructured":"Park J, Megow A, Swalling A, Hodge J, Foreman A, Boase S, Valentine R, Krishnan S, Ooi EH (2020) Prognosis of oral squamous cell carcinoma with perineural invasion: a comparative study of classification types. Clin Otolaryngol 45","DOI":"10.1111\/coa.13472"},{"key":"2711_CR46","doi-asserted-by":"crossref","unstructured":"Alkhadar H, Macluskey M, White S, Ellis I (2020) Perineural invasion in oral squamous cell carcinoma: incidence, prognostic impact and molecular insight. J Oral Pathol Med (4)","DOI":"10.1016\/j.oooo.2021.03.032"},{"issue":"11","key":"2711_CR47","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1080\/00016489.2019.1655167","volume":"139","author":"J Zhu","year":"2019","unstructured":"Zhu J, Zhou R, Wang Y, Yu M (2019) Perineural invasion as a prognostic factor in head and neck squamous cell carcinoma: a systematic review and meta-analysis. Acta Otolaryngol 139(11):1","journal-title":"Acta Otolaryngol"},{"issue":"5","key":"2711_CR48","doi-asserted-by":"publisher","first-page":"1109","DOI":"10.1016\/j.ijrobp.2018.12.009","volume":"103","author":"RL Bakst","year":"2019","unstructured":"Bakst RL, Glastonbury CM, Parvathaneni U, Katabi N, Hu KS, Yom SS (2019) Perineural invasion and perineural tumor spread in head and neck cancer. Int J Radiat Oncol Biol Phys 103(5):1109","journal-title":"Int J Radiat Oncol Biol Phys"},{"key":"2711_CR49","doi-asserted-by":"crossref","unstructured":"Cao Y, Cheng R, Chen N, Dahai J (2019) p75 Nerve growth factor receptor as a specific nerve marker in the diagnosis of perineural invasion of squamous cell Carcinoma. Am J Clin Pathol","DOI":"10.1093\/ajcp\/aqz011"},{"key":"2711_CR50","doi-asserted-by":"crossref","unstructured":"Li J, Jiang Y, Chen C, Tan W, Yin W (2020) Integrin \u03b24 is an effective and efficient marker in synchronously highlighting lymphatic and blood vascular invasion, and perineural aggression in malignancy. Am J Surg Pathol 44(5)","DOI":"10.1097\/PAS.0000000000001451"},{"issue":"14","key":"2711_CR51","doi-asserted-by":"publisher","first-page":"9915","DOI":"10.1007\/s00521-019-04516-y","volume":"32","author":"MM Fraz","year":"2020","unstructured":"Fraz MM, Khurram SA, Graham S, Shaban M, Hassan M, Loya A, Rajpoot NM (2020) FABnet: feature attention-based network for simultaneous segmentation of microvessels and nerves in routine histology images of oral cancer. Neural Comput Appl 32(14):9915","journal-title":"Neural Comput Appl"},{"key":"2711_CR52","doi-asserted-by":"crossref","unstructured":"Hou L, Samaras D, Kurc TM, Gao Y, Davis JE, Saltz JH (2016) Patch-based convolutional neural network for whole slide tissue image classification. In: 2016 IEEE conference on computer vision and pattern recognition (CVPR), pp 2424\u20132433","DOI":"10.1109\/CVPR.2016.266"},{"key":"2711_CR53","doi-asserted-by":"crossref","unstructured":"Ronneberger O, Fischer P, Brox T (2015) Convolutional networks for biomedical image segmentation. In: Navab N, Hornegger J, Wells WM, Frangi AF (eds) Medical image computing and computer-assisted intervention \u2013 MICCAI 2015. Springer International Publishing, Cham, pp 234\u2013241","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"2711_CR54","doi-asserted-by":"publisher","unstructured":"Tian M, He J, Yu X, Cai C, Gao Y (2021) MCMC guided CNN training and segmentation for pancreas extraction. IEEE Access, 1\u20131. https:\/\/doi.org\/10.1109\/ACCESS.2021.3070391","DOI":"10.1109\/ACCESS.2021.3070391"},{"key":"2711_CR55","doi-asserted-by":"crossref","unstructured":"Long J, Shelhamer E, Darrell T (2017) Fully convolutional networks for semantic segmentation. IEEE Transactions on Pattern Analysis & Machine Intelligence","DOI":"10.1109\/TPAMI.2016.2572683"},{"key":"2711_CR56","unstructured":"Cai J, Lu L, Xie Y, Xing F, Yang L (2017) Improving deep pancreas segmentation in CT and MRI images via recurrent neural contextual learning and direct loss function. arXiv:1707.04912"},{"issue":"3","key":"2711_CR57","first-page":"383","volume":"16","author":"X Yuan","year":"2018","unstructured":"Yuan X, Tao X, Han Z, Long LR, Huang X (2018) SegAN: Adversarial network with multi-scale L 1 loss for medical image segmentation. Neuroinformatics 16(3):383","journal-title":"Neuroinformatics"},{"key":"2711_CR58","doi-asserted-by":"crossref","unstructured":"Hu K, Zhang Z, Niu X, Zhang Y, Cao C, Xiao F, Gao X (2018) Retinal vessel segmentation of color fundus images using multiscale convolutional neural network with an improved cross-entropy loss function. Neurocomputing :S0925231218305423","DOI":"10.1016\/j.neucom.2018.05.011"},{"key":"2711_CR59","doi-asserted-by":"crossref","unstructured":"Gebru T, Hoffman J, Li FF (2017) Fine-grained recognition in the wild: a multi-task domain adaptation approach. In: 2017 IEEE international conference on computer vision (ICCV)","DOI":"10.1109\/ICCV.2017.151"},{"key":"2711_CR60","doi-asserted-by":"crossref","unstructured":"Sun B, Saenko K (2016) Deep CORAL: Correlation alignment for deep domain adaptation. In: Hua G, J\u00e9gou H (eds) Computer vision \u2013 ECCV 2016, Workshops. Springer International Publishing, Cham, pp 443\u2013450","DOI":"10.1007\/978-3-319-49409-8_35"},{"issue":"5","key":"2711_CR61","doi-asserted-by":"publisher","first-page":"2916","DOI":"10.1214\/10-AOS799","volume":"38","author":"ZI Botev","year":"2010","unstructured":"Botev ZI, Grotowski JF, Kroese DP (2010) Kernel density estimation via diffusion. Ann Stat 38(5):2916","journal-title":"Ann Stat"},{"issue":"2","key":"2711_CR62","doi-asserted-by":"publisher","first-page":"442","DOI":"10.1016\/0005-2795(75)90109-9","volume":"405","author":"BW Matthews","year":"1975","unstructured":"Matthews BW (1975) Comparison of the predicted and observed secondary structure of T4 phage lysozyme. Biochim Biophys Acta 405(2):442","journal-title":"Biochim Biophys Acta"},{"key":"2711_CR63","unstructured":"Iglovikov V, Shvets A (2018) TernausNet: U-Net with VGG11 encoder pre-trained on ImageNet for image segmentation. arXiv:1801.05746"},{"key":"2711_CR64","unstructured":"Simonyan K, Zisserman A (2014) Very deep convolutional networks for large-scale image recognition. arXiv:1409.1556"},{"key":"2711_CR65","unstructured":"Chen L, Papandreou G, Schroff F, Adam H (2017) Rethinking Atrous convolution for semantic image segmentation. arXiv:1706.05587"}],"container-title":["Medical &amp; Biological Engineering &amp; Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11517-022-02711-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11517-022-02711-z\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11517-022-02711-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,12,2]],"date-time":"2023-12-02T19:58:58Z","timestamp":1701547138000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11517-022-02711-z"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,12,11]]},"references-count":65,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2023,2]]}},"alternative-id":["2711"],"URL":"https:\/\/doi.org\/10.1007\/s11517-022-02711-z","relation":{},"ISSN":["0140-0118","1741-0444"],"issn-type":[{"value":"0140-0118","type":"print"},{"value":"1741-0444","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,12,11]]},"assertion":[{"value":"28 March 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"22 October 2022","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"11 December 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":"Chuan Huang and Tianhao Zhao have no conflict of interest to declare.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"<!--Emphasis Type='Bold' removed-->Conflict of interest"}}]}}