{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,9,11]],"date-time":"2024-09-11T13:26:55Z","timestamp":1726061215059},"publisher-location":"Cham","reference-count":19,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030361884"},{"type":"electronic","value":"9783030361891"}],"license":[{"start":{"date-parts":[[2019,1,1]],"date-time":"2019-01-01T00:00:00Z","timestamp":1546300800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2019]]},"DOI":"10.1007\/978-3-030-36189-1_13","type":"book-chapter","created":{"date-parts":[[2019,11,28]],"date-time":"2019-11-28T14:03:54Z","timestamp":1574949834000},"page":"155-164","update-policy":"http:\/\/dx.doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["A Bypass-Based U-Net for Medical Image Segmentation"],"prefix":"10.1007","author":[{"given":"Kaixuan","family":"Chen","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Gengxin","family":"Xu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jiaying","family":"Qian","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chuan-Xian","family":"Ren","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2019,11,29]]},"reference":[{"key":"13_CR1","doi-asserted-by":"publisher","first-page":"142","DOI":"10.3389\/fnana.2015.00142","volume":"9","author":"I Arganda-Carreras","year":"2015","unstructured":"Arganda-Carreras, I., Turaga, S., Berger, D., et al.: Crowdsourcing the creation of image segmentation algorithms for connectomics. Front. Neuroanat. 9, 142 (2015)","journal-title":"Front. Neuroanat."},{"key":"13_CR2","doi-asserted-by":"crossref","unstructured":"Chen, H., Qi, X., Cheng, J., Heng, P.: Deep contextual networks for neuronal structure segmentation. In: Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence, Phoenix, Arizona, USA, pp. 1167\u20131173. AAAI Press (2016)","DOI":"10.1609\/aaai.v30i1.10141"},{"key":"13_CR3","doi-asserted-by":"publisher","first-page":"18","DOI":"10.1016\/j.media.2016.05.004","volume":"35","author":"M Havaei","year":"2017","unstructured":"Havaei, M., Davy, A., Warde-Farely, D., et al.: Brain tumors segmentation with deep neural networks. Med. Image Anal. 35, 18\u201331 (2017)","journal-title":"Med. Image Anal."},{"key":"13_CR4","doi-asserted-by":"crossref","unstructured":"Yu, L., Yang, X., Chen, H., Qin, J., Heng, P.: Volumetric convnets with mixed residual connections for automated prostate segmentation from 3D MR images. In: Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence, California, USA, pp. 66\u201372. AAAI Press (2017)","DOI":"10.1609\/aaai.v31i1.10510"},{"key":"13_CR5","doi-asserted-by":"publisher","first-page":"1641","DOI":"10.1109\/TMI.2018.2796130","volume":"37","author":"Y Zheng","year":"2017","unstructured":"Zheng, Y., Jiang, Z., Zhang, H., Xie, F., et al.: Histopathological whole slide image analysis using context-based CBIR. IEEE Trans. Med. Imaging 37, 1641\u20131652 (2017)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"13_CR6","unstructured":"Saker, S.: Diabetic retinopathy: in vitro and clinical studies and mechanisms and pharmacological treatments. University of Nottingham (2016)"},{"issue":"4","key":"13_CR7","doi-asserted-by":"publisher","first-page":"225","DOI":"10.3322\/caac.20006","volume":"59","author":"A Jernal","year":"2009","unstructured":"Jernal, A., Siegel, R., Ward, E., Hao, Y., Xu, J., Thun, M.: Cancer statistics. CA Cancer J. Clin. 59(4), 225\u2013249 (2009)","journal-title":"CA Cancer J. Clin."},{"key":"13_CR8","unstructured":"Krizhevsky, A., Sutskever, I., Hinton, G.: ImageNet classification with deep convolutional neural networks. In: NIP\u2019S Proceedings of the 25th International Conference on Neural Information Processing Systems, Lake Tahoe, Nevada, LNCS, vol. 1, pp. 1097\u20131105 (2012)"},{"key":"13_CR9","doi-asserted-by":"crossref","unstructured":"Szegedy, C., Liu, W., Jia, Y., et al.: Going deeper with convolutions. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Boston, MA, USA, pp. 1\u20139. IEEE (2015)","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"13_CR10","unstructured":"Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. In: Computer Science (2014)"},{"key":"13_CR11","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, pp. 770\u2013778 (2016)","DOI":"10.1109\/CVPR.2016.90"},{"key":"13_CR12","doi-asserted-by":"crossref","unstructured":"Long, J., Shelhamer, E., Darrel, T.: Fully convolutional networks for semantic segmentation. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Boston, MA, USA, pp. 3431\u20133440. IEEE (2015)","DOI":"10.1109\/CVPR.2015.7298965"},{"key":"13_CR13","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"234","DOI":"10.1007\/978-3-319-24574-4_28","volume-title":"Medical Image Computing and Computer-Assisted Intervention \u2013 MICCAI 2015","author":"O Ronneberger","year":"2015","unstructured":"Ronneberger, O., Fischer, P., Brox, T.: U-Net: convolutional networks for biomedical image segmentation. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9351, pp. 234\u2013241. Springer, Cham (2015). https:\/\/doi.org\/10.1007\/978-3-319-24574-4_28"},{"key":"13_CR14","unstructured":"Oktay, O., et al.: Attention U-Net: learning where to look for the pancreas. In: 1st Conference on Medical Imaging with Deep Learning (MIDL 2018), Amsterdam, The Netherlands (2018)"},{"key":"13_CR15","unstructured":"Alom, M., Hasan, M., Yakopcic, C., Taha, T., Asari, V.: Recurrent residual convolutional neural network based on u-net (R2U-net) for medical image segmentation (2018). https:\/\/arxiv.org\/abs\/1802.06955"},{"key":"13_CR16","unstructured":"Web page of the digital retinal images for vessel extraction. http:\/\/www.isi.uu.nl\/Research\/Databases\/DRIVE\/ . Accessed 11 Mar 2019"},{"key":"13_CR17","unstructured":"Web page of the ISBI 2018: Skin lesion analysis towards melanoma detection. https:\/\/challenge.kitware.com\/#challenge\/5aab46f156357d5e82b00fe5 . Accessed 20 Apr 2019"},{"key":"13_CR18","unstructured":"Codella, N., Rotemberg, V., Tschandl, P., Emre Celebi, M., et al.: Skin lesion analysis toward melanoma detection 2018: a challenge hosted by the international skin imaging collaboration (ISIC) (2018). https:\/\/arxiv.org\/abs\/1902.03368"},{"key":"13_CR19","doi-asserted-by":"publisher","first-page":"180161","DOI":"10.1038\/sdata.2018.161","volume":"5","author":"P Tschandl","year":"2018","unstructured":"Tschandl, P., Rosendahl, C., Kittler, H.: The HAM10000 dataset, a large collection of multi-source dermatoscopic images of common pigmented skin lesions. Sci. Data 5, 180161 (2018)","journal-title":"Sci. Data"}],"container-title":["Lecture Notes in Computer Science","Intelligence Science and Big Data Engineering. Visual Data Engineering"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-36189-1_13","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,10,7]],"date-time":"2022-10-07T04:03:56Z","timestamp":1665115436000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/978-3-030-36189-1_13"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019]]},"ISBN":["9783030361884","9783030361891"],"references-count":19,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-36189-1_13","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2019]]},"assertion":[{"value":"29 November 2019","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"IScIDE","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Intelligent Science and Big Data Engineering","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Nanjing","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2019","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"18 October 2019","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"20 October 2019","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"9","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"iscide2019","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/iscide.njust.edu.cn\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}