{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,27]],"date-time":"2025-03-27T23:13:58Z","timestamp":1743117238220,"version":"3.40.3"},"publisher-location":"Cham","reference-count":19,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783031140532"},{"type":"electronic","value":"9783031140549"}],"license":[{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"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":[],"published-print":{"date-parts":[[2022]]},"DOI":"10.1007\/978-3-031-14054-9_7","type":"book-chapter","created":{"date-parts":[[2022,8,10]],"date-time":"2022-08-10T17:02:44Z","timestamp":1660150964000},"page":"65-75","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["SpringNet: A Novel Deep Neural Network Architecture for Histopathological Image Analysis"],"prefix":"10.1007","author":[{"given":"Matej","family":"Halinkovic","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wanda","family":"Benesova","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2022,8,11]]},"reference":[{"issue":"12","key":"7_CR1","doi-asserted-by":"publisher","first-page":"772","DOI":"10.1038\/s42256-020-00265-z","volume":"2","author":"Z Chen","year":"2020","unstructured":"Chen, Z., Bei, Y., Rudin, C.: Concept whitening for interpretable image recognition. Nat. Mach. Intell. 2(12), 772\u2013782 (2020)","journal-title":"Nat. Mach. Intell."},{"key":"7_CR2","unstructured":"Chen, C., Li, O., Tao, C., Barnett, A.J., Su, J., Rudin, C.: This Looks like That: Deep Learning for Interpretable Image Recognition. Curran Associates Inc., Red Hook (2019)"},{"key":"7_CR3","doi-asserted-by":"crossref","unstructured":"Lu, W., Graham, S., Bilal, M., Rajpoot, N., Minhas, F.: Capturing cellular topology in multi-gigapixel pathology images. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition Workshops, pp. 260\u2013261 (2020)","DOI":"10.1109\/CVPRW50498.2020.00138"},{"key":"7_CR4","doi-asserted-by":"crossref","unstructured":"Dey, N., Ashour, A., Ashour, A., Singh, A.: Digital analysis of microscopic images in medicine. J. Adv. Microsc. Res. 10(1), 1\u201313 (2015)","DOI":"10.1166\/jamr.2015.1229"},{"key":"7_CR5","doi-asserted-by":"crossref","unstructured":"Graham, S., et al.: Lizard: a large-scale dataset for colonic nuclear instance segmentation and classification. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 684\u2013693 (2021)","DOI":"10.1109\/ICCVW54120.2021.00082"},{"key":"7_CR6","doi-asserted-by":"publisher","unstructured":"Ronneberger, O., Fischer, P., Brox, T.: U-Net: convolutional networks for biomedical image segmentation. In: Navab, N., Hornegger, J., Wells, W., Frangi, A. (eds.) MICCAI 2015. LNCS, vol. 9351, pp. 234\u2013241. Springer, Cham (2015). https:\/\/doi.org\/10.1007\/978-3-319-24574-4_28","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"7_CR7","doi-asserted-by":"crossref","unstructured":"Krithiga, R., Geetha, P.: Breast cancer detection, segmentation and classification on histopathology images analysis: a systematic review. Arch. Comput. Methods Eng. 28(4), 2607\u20132619 (2021)","DOI":"10.1007\/s11831-020-09470-w"},{"key":"7_CR8","doi-asserted-by":"crossref","unstructured":"Graham, S., et al.: Hover-net: simultaneous segmentation and classification of nuclei in multi-tissue histology images. Med. Image Anal. 58, 101563 (2019)","DOI":"10.1016\/j.media.2019.101563"},{"key":"7_CR9","doi-asserted-by":"crossref","unstructured":"Van Rijthoven, M., Balkenhol, M., Sili\u0146a, K., Van Der Laak, J., Ciompi, F.: Hooknet: multi-resolution convolutional neural networks for semantic segmentation in histopathology whole-slide images. Med. Image Anal. 68, 101890 (2021)","DOI":"10.1016\/j.media.2020.101890"},{"key":"7_CR10","doi-asserted-by":"crossref","unstructured":"Shao, W., Sun, L., Zhang, D.: Deep active learning for nucleus classification in pathology images. In: 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018), pp. 199\u2013202 (2018)","DOI":"10.1109\/ISBI.2018.8363554"},{"key":"7_CR11","doi-asserted-by":"publisher","unstructured":"Li, R., Yao, J., Zhu, X., Li, Y., Huang, J.: Graph CNN for survival analysis on whole slide pathological images. In: Frangi, A., Schnabel, J., Davatzikos, C., Alberola-L\u00f3pez, C., Fichtinger, G. (eds.) MICCAI 2018. LNCS, vol. 11071, pp. 174\u2013182. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-00934-2_20","DOI":"10.1007\/978-3-030-00934-2_20"},{"key":"7_CR12","unstructured":"Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014)"},{"key":"7_CR13","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2020.101771","volume":"65","author":"NA Koohbanani","year":"2020","unstructured":"Koohbanani, N.A., Jahanifar, M., Tajadin, N.Z., Rajpoot, N.: Nuclick: a deep learning framework for interactive segmentation of microscopic images. Med. Image Anal. 65, 101771 (2020)","journal-title":"Med. Image Anal."},{"key":"7_CR14","doi-asserted-by":"crossref","unstructured":"Macenko, M., et al.: A method for normalizing histology slides for quantitative analysis. In: 2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, pp. 1107\u20131110. IEEE (2009)","DOI":"10.1109\/ISBI.2009.5193250"},{"key":"7_CR15","doi-asserted-by":"crossref","unstructured":"Litjens, G., et al.: A survey on deep learning in medical image analysis. Med. Image Anal. 42, 60\u201388 (2017)","DOI":"10.1016\/j.media.2017.07.005"},{"key":"7_CR16","doi-asserted-by":"crossref","unstructured":"Siddique, N., Paheding, S., Elkin, C.P., Devabhaktuni, V.: U-net and its variants for medical image segmentation: a review of theory and applications. IEEE Access 9, 82031\u201382057 (2021)","DOI":"10.1109\/ACCESS.2021.3086020"},{"key":"7_CR17","doi-asserted-by":"crossref","unstructured":"Dice, L.R.: Measures of the amount of ecologic association between species. Ecology 26(3), 297\u2013302 (1945)","DOI":"10.2307\/1932409"},{"issue":"1","key":"7_CR18","doi-asserted-by":"publisher","first-page":"37","DOI":"10.1177\/001316446002000104","volume":"20","author":"J Cohen","year":"1960","unstructured":"Cohen, J.: A coefficient of agreement for nominal scales. Educ. Psychol. Meas. 20(1), 37\u201346 (1960)","journal-title":"Educ. Psychol. Meas."},{"key":"7_CR19","unstructured":"Iandola, F., Moskewicz, M., Karayev, S., Girshick, R., Darrell, T., Keutzer, K.:Densenet: implementing efficient convNet descriptor pyramids. arXiv preprint arXiv:1404.1869 (2014)"}],"container-title":["Advances in Intelligent Systems and Computing","Proceedings of the ICR\u201922 International Conference on Innovations in Computing Research"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-14054-9_7","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,8,10]],"date-time":"2022-08-10T17:03:47Z","timestamp":1660151027000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-14054-9_7"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783031140532","9783031140549"],"references-count":19,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-14054-9_7","relation":{},"ISSN":["2194-5357","2194-5365"],"issn-type":[{"type":"print","value":"2194-5357"},{"type":"electronic","value":"2194-5365"}],"subject":[],"published":{"date-parts":[[2022]]},"assertion":[{"value":"11 August 2022","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICR","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"The International Conference on Innovations in Computing Research","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Athens","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Greece","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2022","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"29 August 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"31 August 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"icr12022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/iicser.org\/icr22","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}