{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,7]],"date-time":"2026-05-07T09:21:24Z","timestamp":1778145684398,"version":"3.51.4"},"publisher-location":"Cham","reference-count":24,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783030645557","type":"print"},{"value":"9783030645564","type":"electronic"}],"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\/978-3-030-64556-4_26","type":"book-chapter","created":{"date-parts":[[2020,12,11]],"date-time":"2020-12-11T18:04:01Z","timestamp":1607709841000},"page":"333-345","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":16,"title":["Offline Versus Online Triplet Mining Based on Extreme Distances of Histopathology Patches"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2778-9841","authenticated-orcid":false,"given":"Milad","family":"Sikaroudi","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9617-291X","authenticated-orcid":false,"given":"Benyamin","family":"Ghojogh","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6927-5617","authenticated-orcid":false,"given":"Amir","family":"Safarpoor","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6900-315X","authenticated-orcid":false,"given":"Fakhri","family":"Karray","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3921-4762","authenticated-orcid":false,"given":"Mark","family":"Crowley","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5488-601X","authenticated-orcid":false,"given":"Hamid R.","family":"Tizhoosh","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2020,12,7]]},"reference":[{"key":"26_CR1","doi-asserted-by":"publisher","first-page":"38","DOI":"10.4103\/jpi.jpi_53_18","volume":"9","author":"HR Tizhoosh","year":"2018","unstructured":"Tizhoosh, H.R., Pantanowitz, L.: Artificial intelligence and digital pathology: challenges and opportunities. J. Pathol. Inform. 9, 38 (2018)","journal-title":"J. Pathol. Inform."},{"key":"26_CR2","doi-asserted-by":"crossref","unstructured":"Sikaroudi, M., Safarpoor, A., Ghojogh, B., Shafiei, S., Crowley, M., Tizhoosh, H.: Supervision and source domain impact on representation learning: a histopathology case study. In: 2020 International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). IEEE (2020)","DOI":"10.1109\/EMBC44109.2020.9176279"},{"key":"26_CR3","doi-asserted-by":"crossref","unstructured":"Ghojogh, B., Sikaroudi, M., Shafiei, S., Tizhoosh, H., Karray, F., Crowley, M.: Fisher discriminant triplet and contrastive losses for training Siamese networks. In: 2020 International Joint Conference on Neural Networks (IJCNN). IEEE (2020)","DOI":"10.1109\/IJCNN48605.2020.9206833"},{"key":"26_CR4","unstructured":"Teh, E.W., Taylor, G.W.: Metric learning for patch classification in digital pathology. In: Medical Imaging with Deep Learning (MIDL) Conference (2019)"},{"key":"26_CR5","unstructured":"Koch, G., Zemel, R., Salakhutdinov, R.: Siamese neural networks for one-shot image recognition. In: ICML Deep Learning Workshop, vol. 2 (2015)"},{"key":"26_CR6","unstructured":"Medela, A., et al.: Few shot learning in histopathological images: reducing the need of labeled data on biological datasets. In: IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019), 1860\u20131864. IEEE (2019)"},{"key":"26_CR7","doi-asserted-by":"publisher","first-page":"137","DOI":"10.1016\/j.compbiomed.2017.03.024","volume":"84","author":"J Wang","year":"2017","unstructured":"Wang, J., Fang, Z., Lang, N., Yuan, H., Su, M.Y., Baldi, P.: A multi-resolution approach for spinal metastasis detection using deep Siamese neural networks. Comput. Biol. Med. 84, 137\u2013146 (2017)","journal-title":"Comput. Biol. Med."},{"key":"26_CR8","doi-asserted-by":"crossref","unstructured":"Schroff, F., Kalenichenko, D., Philbin, J.: FaceNet: a unified embedding for face recognition and clustering. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 815\u2013823 (2015)","DOI":"10.1109\/CVPR.2015.7298682"},{"issue":"10","key":"26_CR9","doi-asserted-by":"publisher","first-page":"2993","DOI":"10.1016\/j.patcog.2015.04.005","volume":"48","author":"S Ding","year":"2015","unstructured":"Ding, S., Lin, L., Wang, G., Chao, H.: Deep feature learning with relative distance comparison for person re-identification. Pattern Recogn. 48(10), 2993\u20133003 (2015)","journal-title":"Pattern Recogn."},{"key":"26_CR10","unstructured":"Hermans, A., Beyer, L., Leibe, B.: In defense of the triplet loss for person re-identification. arXiv preprint arXiv:1703.07737 (2017)"},{"key":"26_CR11","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"676","DOI":"10.1007\/978-3-030-32239-7_75","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2019","author":"T Peng","year":"2019","unstructured":"Peng, T., Boxberg, M., Weichert, W., Navab, N., Marr, C.: Multi-task learning of a deep K-nearest neighbour network for histopathological image classification and retrieval. In: Shen, D., et al. (eds.) MICCAI 2019. LNCS, vol. 11764, pp. 676\u2013684. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-32239-7_75"},{"key":"26_CR12","unstructured":"Goldberger, J., Hinton, G.E., Roweis, S.T., Salakhutdinov, R.R.: Neighbourhood components analysis. In: Advances in Neural Information Processing Systems, pp. 513\u2013520 (2005)"},{"key":"26_CR13","doi-asserted-by":"crossref","unstructured":"Movshovitz-Attias, Y., Toshev, A., Leung, T.K., Ioffe, S., Singh, S.: No fuss distance metric learning using proxies. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 360\u2013368 (2017)","DOI":"10.1109\/ICCV.2017.47"},{"key":"26_CR14","doi-asserted-by":"crossref","unstructured":"Teh, E.W., Taylor, G.W.: Learning with less data via weakly labeled patch classification in digital pathology. In: IEEE 17th International Symposium on Biomedical Imaging (ISBI 2020), pp. 471\u2013475. IEEE (2020)","DOI":"10.1109\/ISBI45749.2020.9098533"},{"key":"26_CR15","doi-asserted-by":"crossref","unstructured":"Xuan, H., Stylianou, A., Pless, R.: Improved embeddings with easy positive triplet mining. In: The IEEE Winter Conference on Applications of Computer Vision, pp. 2474\u20132482 (2020)","DOI":"10.1109\/WACV45572.2020.9093432"},{"key":"26_CR16","doi-asserted-by":"crossref","unstructured":"Wu, C.Y., Manmatha, R., Smola, A.J., Krahenbuhl, P.: Sampling matters in deep embedding learning. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2840\u20132848 (2017)","DOI":"10.1109\/ICCV.2017.309"},{"key":"26_CR17","doi-asserted-by":"crossref","unstructured":"Jimenez-del Toro, O., et al.: Analysis of histopathology images: from traditional machine learning to deep learning. In: Biomedical Texture Analysis, pp. 281\u2013314. Elsevier (2017)","DOI":"10.1016\/B978-0-12-812133-7.00010-7"},{"key":"26_CR18","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-47578-3","volume-title":"Outlier Analysis","author":"CC Aggarwal","year":"2017","unstructured":"Aggarwal, C.C.: Outlier Analysis, 2nd edn. Springer, Cham (2017). https:\/\/doi.org\/10.1007\/978-3-319-47578-3","edition":"2"},{"key":"26_CR19","doi-asserted-by":"crossref","unstructured":"Ye, M., Zhang, X., Yuen, P.C., Chang, S.F.: Unsupervised embedding learning via invariant and spreading instance feature. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 6210\u20136219 (2019)","DOI":"10.1109\/CVPR.2019.00637"},{"issue":"1","key":"26_CR20","doi-asserted-by":"publisher","first-page":"e1002730","DOI":"10.1371\/journal.pmed.1002730","volume":"16","author":"JN Kather","year":"2019","unstructured":"Kather, J.N., et al.: Predicting survival from colorectal cancer histology slides using deep learning: a retrospective multicenter study. PLoS Med. 16(1), e1002730 (2019)","journal-title":"PLoS Med."},{"key":"26_CR21","doi-asserted-by":"crossref","unstructured":"Tizhoosh, H.R.: Opposition-based learning: a new scheme for machine intelligence. In: International Conference on Computational Intelligence for Modelling, Control and Automation and International Conference on Intelligent Agents, Web Technologies and Internet Commerce (CIMCA-IAWTIC 2006), vol. 1, pp. 695\u2013701. IEEE (2005)","DOI":"10.1109\/CIMCA.2005.1631345"},{"key":"26_CR22","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-540-70829-2","volume-title":"Oppositional Concepts in Computational Intelligence","author":"HR Tizhoosh","year":"2008","unstructured":"Tizhoosh, H.R., Ventresca, M.: Oppositional Concepts in Computational Intelligence, vol. 155. Springer, Heidelberg (2008). https:\/\/doi.org\/10.1007\/978-3-540-70829-2"},{"key":"26_CR23","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770\u2013778 (2016)","DOI":"10.1109\/CVPR.2016.90"},{"issue":"1","key":"26_CR24","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41746-019-0211-0","volume":"3","author":"S Kalra","year":"2020","unstructured":"Kalra, S., et al.: Pan-cancer diagnostic consensus through searching archival histopathology images using artificial intelligence. NPJ Digit. Med. Nat. 3(1), 1\u201315 (2020)","journal-title":"NPJ Digit. Med. Nat."}],"container-title":["Lecture Notes in Computer Science","Advances in Visual Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-64556-4_26","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,8,18]],"date-time":"2024-08-18T22:53:22Z","timestamp":1724021602000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-64556-4_26"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020]]},"ISBN":["9783030645557","9783030645564"],"references-count":24,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-64556-4_26","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020]]},"assertion":[{"value":"7 December 2020","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ISVC","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Symposium on Visual Computing","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"San Diego, CA","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"USA","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2020","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"5 October 2020","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"7 October 2020","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"15","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"isvc2020","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/www.isvc.net\/","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":"EasyChair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"175","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":"114","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":"4","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":"65% - 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":"3","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)"}},{"value":"The symposium was held virtually due to the COVID-19 pandemic. 65 papers were accepted as oral presentations and 41 as posters. 12 special tracks papers are also included.","order":10,"name":"additional_info_on_review_process","label":"Additional Info on Review Process","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}