{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,26]],"date-time":"2025-03-26T02:20:15Z","timestamp":1742955615213,"version":"3.40.3"},"publisher-location":"Cham","reference-count":22,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031314377"},{"type":"electronic","value":"9783031314384"}],"license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023]]},"DOI":"10.1007\/978-3-031-31438-4_20","type":"book-chapter","created":{"date-parts":[[2023,4,26]],"date-time":"2023-04-26T08:02:53Z","timestamp":1682496173000},"page":"293-308","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Systematic Augmentation in\u00a0HSV Space for\u00a0Semantic Segmentation of\u00a0Prostate Biopsies"],"prefix":"10.1007","author":[{"given":"Filip","family":"Winzell","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ida","family":"Arvidsson","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Niels Christian","family":"Overgaard","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Karl","family":"\u00c5str\u00f6m","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Felicia-Elena","family":"Marginean","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Anders","family":"Bjartell","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Agnieszka","family":"Krzyzanowska","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Athanasios","family":"Simoulis","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Anders","family":"Heyden","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,4,27]]},"reference":[{"key":"20_CR1","doi-asserted-by":"publisher","unstructured":"Arvidsson, I., Overgaard, N.C., \u00c5str\u00f6m, K., Heyden, A.: Comparison of different augmentation techniques for improved generalization performance for gleason grading. In: 2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019), pp. 923\u2013927 (2019). https:\/\/doi.org\/10.1109\/ISBI.2019.8759264","DOI":"10.1109\/ISBI.2019.8759264"},{"key":"20_CR2","doi-asserted-by":"publisher","unstructured":"Bulten, W., et al.: Automated deep-learning system for gleason grading of prostate cancer using biopsies: a diagnostic study. Lancet Oncol. 21(2), 233\u2013241 (2020). https:\/\/doi.org\/10.1016\/S1470-2045(19)30739-9","DOI":"10.1016\/S1470-2045(19)30739-9"},{"key":"20_CR3","unstructured":"Casselman, B.: Feature column February 2014: stereographic projection (2014). http:\/\/www.ams.org\/publicoutreach\/feature-column\/fc-2014-02. Accessed Dec 2022"},{"key":"20_CR4","unstructured":"Chen, X., et al.: Recent advances and clinical applications of deep learning in medical image analysis. CoRR abs\/2105.13381 (2021). https:\/\/arxiv.org\/abs\/2105.13381"},{"issue":"9","key":"20_CR5","doi-asserted-by":"publisher","first-page":"1228","DOI":"10.1097\/01.pas.0000173646.99337.b1","volume":"29","author":"JI Epstein","year":"2005","unstructured":"Epstein, J.I., Allsbrook, W.C.J., Amin, M.B., Egevad, L.L.: The 2005 international society of urological pathology (isup) consensus conference on gleason grading of prostatic carcinoma. Am. J. Surg. Pathol. 29(9), 1228\u20131242 (2005). https:\/\/doi.org\/10.1097\/01.pas.0000173646.99337.b1","journal-title":"Am. J. Surg. Pathol."},{"key":"20_CR6","unstructured":"Eurostat EU: cancer statistics: specific cancers (2022). https:\/\/ec.europa.eu\/eurostat\/statistics-explained\/index.php?title=Cancer_statistics"},{"key":"20_CR7","doi-asserted-by":"publisher","unstructured":"Isaksson, J., Arvidsson, I., \u00c5str\u00f6m, K., Heyden, A.: Semantic segmentation of microscopic images of h &e stained prostatic tissue using CNN. In: 2017 International Joint Conference on Neural Networks (IJCNN), pp. 1252\u20131256 (2017). https:\/\/doi.org\/10.1109\/IJCNN.2017.7965996","DOI":"10.1109\/IJCNN.2017.7965996"},{"key":"20_CR8","doi-asserted-by":"publisher","unstructured":"Kartasalo, K., et al.: Artificial intelligence for diagnosis and gleason grading of prostate cancer in biopsies-current status and next steps. Europ. Urol. Focus 7(4), 687\u2013691 (2021). https:\/\/doi.org\/10.1016\/j.euf.2021.07.002. https:\/\/www.sciencedirect.com\/science\/article\/pii\/S2405456921001814","DOI":"10.1016\/j.euf.2021.07.002"},{"issue":"7","key":"20_CR9","doi-asserted-by":"publisher","first-page":"1550","DOI":"10.1109\/TMI.2017.2677499","volume":"36","author":"N Kumar","year":"2017","unstructured":"Kumar, N., Verma, R., Sharma, S., Bhargava, S., Vahadane, A., Sethi, A.: A dataset and a technique for generalized nuclear segmentation for computational pathology. IEEE Trans. Med. Imaging 36(7), 1550\u20131560 (2017). https:\/\/doi.org\/10.1109\/TMI.2017.2677499","journal-title":"IEEE Trans. Med. Imaging"},{"key":"20_CR10","doi-asserted-by":"publisher","unstructured":"Liu, Y., et al.: Detecting cancer metastases on gigapixel pathology images (2017). https:\/\/doi.org\/10.48550\/ARXIV.1703.02442. https:\/\/arxiv.org\/abs\/1703.02442","DOI":"10.48550\/ARXIV.1703.02442"},{"key":"20_CR11","doi-asserted-by":"publisher","unstructured":"Marginean, F., et al.: An artificial intelligence-based support tool for automation and standardisation of gleason grading in prostate biopsies. Europ. Urol. Focus 7(5), 995\u20131001 (2021). https:\/\/doi.org\/10.1016\/j.euf.2020.11.001. https:\/\/www.sciencedirect.com\/science\/article\/pii\/S2405456920302960","DOI":"10.1016\/j.euf.2020.11.001"},{"key":"20_CR12","doi-asserted-by":"publisher","unstructured":"Nagpal, K., Foote, D., Tan, F., et al.: Development and validation of a deep learning algorithm for gleason grading of prostate cancer from biopsy specimens. JAMA Oncol. 6(9), 1372\u20131380 (2020). https:\/\/doi.org\/10.1001\/jamaoncol.2020.2485","DOI":"10.1001\/jamaoncol.2020.2485"},{"key":"20_CR13","unstructured":"PyTorch documentation: colorjitter - torchvision main documentation (2022). https:\/\/pytorch.org\/vision\/main\/generated\/torchvision.transforms.ColorJitter.html"},{"key":"20_CR14","doi-asserted-by":"publisher","unstructured":"Rizwan-i-Haque, I., Neubert, J.: Deep learning approaches to biomedical image segmentation. Inf. Med. Unlocked 18, 100297 (2020). https:\/\/doi.org\/10.1016\/j.imu.2020.100297. https:\/\/www.sciencedirect.com\/science\/article\/pii\/S235291481930214X","DOI":"10.1016\/j.imu.2020.100297"},{"key":"20_CR15","unstructured":"Ronneberger, O., Fischer, P., Brox, T.: U-net: convolutional networks for biomedical image segmentation. CoRR abs\/1505.04597 (2015). http:\/\/arxiv.org\/abs\/1505.04597"},{"issue":"1","key":"20_CR16","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s40537-019-0197-0","volume":"6","author":"C Shorten","year":"2019","unstructured":"Shorten, C., Khoshgoftaar, T.M.: A survey on image data augmentation for deep learning. J. Big Data 6(1), 1\u201348 (2019). https:\/\/doi.org\/10.1186\/s40537-019-0197-0","journal-title":"J. Big Data"},{"key":"20_CR17","doi-asserted-by":"publisher","unstructured":"Srigley, J.R., Delahunt, B., Egevad, L., Samaratunga, H., Yaxley, J., Evans, A.J.: One is the new six: The international society of urological pathology (isup) patient-focused approach to gleason grading. Canad. Urol. Assoc. J. 10(9\u201310), 339\u201341 (2016). https:\/\/doi.org\/10.5489\/cuaj.4146. https:\/\/cuaj.ca\/index.php\/journal\/article\/view\/4146","DOI":"10.5489\/cuaj.4146"},{"key":"20_CR18","doi-asserted-by":"publisher","unstructured":"Str\u00f6m, P., Kartasalo, K., Olsson, H., Solorzano, L., et al.: Artificial intelligence for diagnosis and grading of prostate cancer in biopsies: a population-based, diagnostic study. Lancet Oncol. 21(2), 222\u2013232 (2020). https:\/\/doi.org\/10.1016\/S1470-2045(19)30738-7. https:\/\/www.sciencedirect.com\/science\/article\/pii\/S1470204519307387","DOI":"10.1016\/S1470-2045(19)30738-7"},{"key":"20_CR19","doi-asserted-by":"crossref","unstructured":"Sudre, C.H., Li, W., Vercauteren, T., Ourselin, S., Cardoso, M.J.: Generalised dice overlap as a deep learning loss function for highly unbalanced segmentations. CoRR abs\/1707.03237 (2017). http:\/\/arxiv.org\/abs\/1707.03237","DOI":"10.1007\/978-3-319-67558-9_28"},{"key":"20_CR20","doi-asserted-by":"publisher","unstructured":"Wang, S., Yang, D.M., Rong, R., Zhan, X., Xiao, G.: Pathology image analysis using segmentation deep learning algorithms. Am. J. Pathol. 189(9), 1686\u20131698 (2019). https:\/\/doi.org\/10.1016\/j.ajpath.2019.05.007. https:\/\/www.sciencedirect.com\/science\/article\/pii\/S0002944018311210","DOI":"10.1016\/j.ajpath.2019.05.007"},{"key":"20_CR21","unstructured":"Wikipedia: HSL and HSV (2022). https:\/\/en.wikipedia.org\/wiki\/HSL_and_HSV. Accessed Dec 2022"},{"key":"20_CR22","unstructured":"World cancer research fund international: worldwide cancer data (2022). https:\/\/www.wcrf.org\/cancer-trends\/worldwide-cancer-data\/"}],"container-title":["Lecture Notes in Computer Science","Image Analysis"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-31438-4_20","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,5,29]],"date-time":"2023-05-29T09:06:39Z","timestamp":1685351199000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-31438-4_20"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031314377","9783031314384"],"references-count":22,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-31438-4_20","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"27 April 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"SCIA","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Scandinavian Conference on Image Analysis","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Lapland","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Finland","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2023","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"18 April 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"21 April 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"22","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"scia2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/sites.google.com\/view\/scia2023\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"CMT 3","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"108","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"67","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"62% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"5","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}