{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,27]],"date-time":"2025-03-27T16:19:07Z","timestamp":1743092347371,"version":"3.40.3"},"publisher-location":"Cham","reference-count":29,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030504014"},{"type":"electronic","value":"9783030504021"}],"license":[{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"vor","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":[[2020]]},"DOI":"10.1007\/978-3-030-50402-1_8","type":"book-chapter","created":{"date-parts":[[2020,6,23]],"date-time":"2020-06-23T23:04:44Z","timestamp":1592953484000},"page":"118-135","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["OBDEX \u2013 Open Block Data Exchange System"],"prefix":"10.1007","author":[{"given":"Bj\u00f6rn","family":"Lindequist","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Norman","family":"Zerbe","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Peter","family":"Hufnagl","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2020,6,24]]},"reference":[{"key":"8_CR1","doi-asserted-by":"publisher","first-page":"60","DOI":"10.1016\/j.media.2017.07.005","volume":"42","author":"L Geert","year":"2017","unstructured":"Geert, L., et al.: A survey on deep learning in medical image analysis. Med. Image Anal. 42, 60\u201388 (2017)","journal-title":"Med. Image Anal."},{"unstructured":"Song, Y., et al.: A deep learning based framework for accurate segmentation of cervical cytoplasm and nuclei. In: 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 2903\u20132906, IEEE (2014)","key":"8_CR2"},{"key":"8_CR3","doi-asserted-by":"publisher","first-page":"43","DOI":"10.1016\/j.compmedimag.2018.08.010","volume":"70","author":"H Henning","year":"2018","unstructured":"Henning, H., et al.: Deep learning nuclei detection: a simple approach can deliver state-of-the-art results. Comput. Med. Imaging Graph. 70, 43\u201352 (2018)","journal-title":"Comput. Med. Imaging Graph."},{"issue":"2","key":"8_CR4","doi-asserted-by":"publisher","first-page":"151","DOI":"10.1016\/j.semcancer.2018.07.001","volume":"52","author":"F Klauschen","year":"2018","unstructured":"Klauschen, F., et al.: Scoring of tumor-infiltrating lymphocytes: from visual estimation to machine learning. Semin. Cancer Biol. 52(2), 151\u2013157 (2018)","journal-title":"Semin. Cancer Biol."},{"key":"8_CR5","doi-asserted-by":"publisher","first-page":"2","DOI":"10.1016\/j.compmedimag.2017.06.001","volume":"61","author":"H Sharma","year":"2017","unstructured":"Sharma, H., et al.: Deep convolutional neural networks for automatic classification of gastric carcinoma using whole slide images in digital histopathology. Comput. Med. Imaging Graph. 61, 2\u201313 (2017)","journal-title":"Comput. Med. Imaging Graph."},{"unstructured":"Hufnagl, P., et al.: Virtual microscopy in modern tissue-biobanks - the ZeBanC example. In: 27th European Congress of Pathology, Extended Abstracts, pp. 41\u201345. Springer (2015)","key":"8_CR6"},{"issue":"8","key":"8_CR7","doi-asserted-by":"publisher","first-page":"1057","DOI":"10.1016\/j.humpath.2009.04.006","volume":"40","author":"RS Weinstein","year":"2009","unstructured":"Weinstein, R.S., et al.: Overview of telepathology, virtual microscopy, and whole slide imaging: prospects for the future. Human Pathol. 40(8), 1057\u20131069 (2009)","journal-title":"Human Pathol."},{"unstructured":"Kairos - Biobanking 3.0. \nhttps:\/\/www.kairos.de\/referenzen\/konsortium\/biobanking-3-0\n\n. Accessed 29 Nov 2019","key":"8_CR8"},{"issue":"3","key":"8_CR9","doi-asserted-by":"publisher","first-page":"341","DOI":"10.1007\/s10278-018-0082-y","volume":"31","author":"S Jodogne","year":"2018","unstructured":"Jodogne, S.: The orthanc ecosystem for medical imaging. J. Digit. Imaging 31(3), 341\u2013352 (2018)","journal-title":"J. Digit. Imaging"},{"doi-asserted-by":"crossref","unstructured":"Clunie, D., et al.: Digital imaging and communications in medicine whole slide imaging connectathon at digital pathology association pathology visions 2017. J. Pathol. Inform. 9 (2018)","key":"8_CR10","DOI":"10.4103\/jpi.jpi_1_18"},{"doi-asserted-by":"crossref","unstructured":"Herrmann, M., D. et al.: Implementing the DICOM standard for digital pathology. J. Pathol. Inform. 9 (2018)","key":"8_CR11","DOI":"10.4103\/jpi.jpi_42_18"},{"unstructured":"DICOM Working Group 26. \nhttps:\/\/www.dicomstandard.org\/wgs\/wg-26\/\n\n. Accessed 29 Nov 2019","key":"8_CR12"},{"key":"8_CR13","doi-asserted-by":"publisher","first-page":"190","DOI":"10.1016\/j.jbi.2017.06.009","volume":"71","author":"TM Godinho","year":"2017","unstructured":"Godinho, T.M., et al.: An efficient architecture to support digital pathology in standard medical imaging repositories. J. Biomed. Informat. 71, 190\u2013197 (2017)","journal-title":"J. Biomed. Informat."},{"unstructured":"DICOM PS3.1 2019d - Introduction and Overview - 1 Scope and Field of Application. \ndicom.nema.org\/medical\/dicom\/current\/output\/chtml\/part01\/chapter1.html\n\n. Accessed 29 Nov 2019","key":"8_CR14"},{"unstructured":"Wilkinson, M.D., et al.: The FAIR Guiding Principles for scientific data management and stewardship. Sci. Data 3 (2016)","key":"8_CR15"},{"doi-asserted-by":"crossref","unstructured":"Janardhan, K.S., et al.: Looking forward: cutting-edge technologies and skills for pathologists in the future. Toxicologic pathology (2019). 0192623319873855","key":"8_CR16","DOI":"10.1177\/0192623319873855"},{"key":"8_CR17","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"13","DOI":"10.1007\/978-3-319-69775-8_2","volume-title":"Towards Integrative Machine Learning and Knowledge Extraction","author":"A Holzinger","year":"2017","unstructured":"Holzinger, A., et al.: Machine learning and knowledge extraction in digital pathology needs an integrative approach. In: Holzinger, A., Goebel, R., Ferri, M., Palade, V. (eds.) Towards Integrative Machine Learning and Knowledge Extraction. LNCS (LNAI), vol. 10344, pp. 13\u201350. Springer, Cham (2017). \nhttps:\/\/doi.org\/10.1007\/978-3-319-69775-8_2"},{"doi-asserted-by":"crossref","unstructured":"Pohn, B., et al.: Visualization of histopathological decision making using a roadbook metaphor. In: 23rd International Conference Information Visualisation (IV). IEEE (2019)","key":"8_CR18","DOI":"10.1109\/IV.2019.00073"},{"key":"8_CR19","first-page":"7","volume":"10","author":"S Bach","year":"2015","unstructured":"Bach, S., et al.: On pixel-wise explanations for non-linear classifier decisions by layer-wise relevance propagation. PLoS ONE 10, 7 (2015)","journal-title":"PLoS ONE"},{"issue":"2","key":"8_CR20","doi-asserted-by":"publisher","first-page":"143","DOI":"10.1002\/path.5310","volume":"249","author":"R Colling","year":"2019","unstructured":"Colling, R., et al.: Artificial intelligence in digital pathology: a roadmap to routine use in clinical practice. J. Pathol. 249(2), 143\u2013150 (2019)","journal-title":"J. Pathol."},{"unstructured":"Norman, Z., et al.: Creation and exploration of augmented whole slide images with application to mouse stroke models. In: Modern Pathology, vol. 31 Supplement 2, p. 602 (2018)","key":"8_CR21"},{"doi-asserted-by":"crossref","unstructured":"Sharma, H., et al.: A comparative study of cell nuclei attributed relational graphs for knowledge description and categorization in histopathological gastric cancer whole slide images. In: 2017 IEEE 30th International Symposium on Computer-Based Medical Systems (CBMS), pp. 61\u201366. IEEE (2017)","key":"8_CR22","DOI":"10.1109\/CBMS.2017.25"},{"unstructured":"Flask (A Python Microframework). \nhttp:\/\/flask.pocoo.org\/\n\n. Accessed 29 Nov 2019","key":"8_CR23"},{"unstructured":"NGINX. \nhttps:\/\/www.nginx.com\/\n\n. Accessed 29 Nov 2019","key":"8_CR24"},{"unstructured":"The uWSGI project. \nhttps:\/\/uwsgi-docs.readthedocs.io\/en\/latest\/\n\n. Accessed 29 Nov 2019","key":"8_CR25"},{"unstructured":"SQLAlchemy - The Database Toolkit for Python. \nhttps:\/\/www.sqlalchemy.org\/\n\n. Accessed 29 Nov 2019","key":"8_CR26"},{"issue":"16","key":"8_CR27","doi-asserted-by":"publisher","first-page":"3651","DOI":"10.1158\/1078-0432.CCR-14-1283","volume":"21","author":"F Klauschen","year":"2015","unstructured":"Klauschen, F., et al.: Standardized Ki67 diagnostics using automated scoring\u2013clinical validation in the GeparTrio breast cancer study. Clin. Cancer Res. 21(16), 3651\u20133657 (2015)","journal-title":"Clin. Cancer Res."},{"unstructured":"CoPaW - Collective Pathology Wisdom, A Platform for Collaborative Whole Slide Image based Case Discussions and Second Opinion. \nhttp:\/\/digitalpathology.charite.de\/CoPaW\n\n. Accessed 29 Nov 2019","key":"8_CR28"},{"unstructured":"ONNX: Open Neural Network Exchange Format. \nhttps:\/\/onnx.ai\/\n\n. Accessed 29 Nov 2019","key":"8_CR29"}],"container-title":["Lecture Notes in Computer Science","Artificial Intelligence and Machine Learning for Digital Pathology"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-50402-1_8","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2020,6,23]],"date-time":"2020-06-23T23:07:55Z","timestamp":1592953675000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/978-3-030-50402-1_8"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020]]},"ISBN":["9783030504014","9783030504021"],"references-count":29,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-50402-1_8","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2020]]},"assertion":[{"value":"24 June 2020","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}}]}}