{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,27]],"date-time":"2025-03-27T13:46:06Z","timestamp":1743083166283,"version":"3.40.3"},"publisher-location":"Cham","reference-count":25,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783031024436"},{"type":"electronic","value":"9783031024443"}],"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\/978-3-031-02444-3_38","type":"book-chapter","created":{"date-parts":[[2022,5,9]],"date-time":"2022-05-09T12:02:50Z","timestamp":1652097770000},"page":"504-517","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Interactive Learning for Assisting Whole Slide Image Annotation"],"prefix":"10.1007","author":[{"given":"Ashish","family":"Menon","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Piyush","family":"Singh","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"P. K.","family":"Vinod","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"C. V.","family":"Jawahar","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2022,5,10]]},"reference":[{"issue":"6","key":"38_CR1","doi-asserted-by":"publisher","first-page":"825","DOI":"10.1177\/0192623316653492","volume":"44","author":"F Aeffner","year":"2016","unstructured":"Aeffner, F., et al.: Commentary: roles for pathologists in a high-throughput image analysis team. Toxicologic Pathol. 44(6), 825\u201334 (2016)","journal-title":"Toxicologic Pathol."},{"key":"38_CR2","doi-asserted-by":"publisher","first-page":"550","DOI":"10.1109\/TMI.2018.2867350","volume":"38","author":"P B\u00e1ndi","year":"2019","unstructured":"B\u00e1ndi, P., et al.: From detection of individual metastases to classification of Lymph node status at the patient level: The CAMELYON17 challenge. IEEE Trans. Med. Imaging 38, 550\u2013560 (2019)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"38_CR3","doi-asserted-by":"publisher","first-page":"2199","DOI":"10.1001\/jama.2017.14585","volume":"318","author":"BE Bejnordi","year":"2017","unstructured":"Bejnordi, B.E., et al.: Diagnostic assessment of deep learning algorithms for detection of Lymph node metastases in women with Breast Cancer. JAMA 318, 2199\u20132210 (2017)","journal-title":"JAMA"},{"key":"38_CR4","doi-asserted-by":"crossref","unstructured":"Cho, S., et al.: DeepScribble: interactive pathology image segmentation using deep neural networks with scribbles. In: 2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI), pp. 761\u2013765 (2021)","DOI":"10.1109\/ISBI48211.2021.9434105"},{"issue":"10","key":"38_CR5","doi-asserted-by":"publisher","first-page":"1559","DOI":"10.1038\/s41591-018-0177-5","volume":"24","author":"N Coudray","year":"2018","unstructured":"Coudray, N., et al.: Classification and mutation prediction from non-small cell lung cancer histopathology images using deep learning. Nat. Med. 24(10), 1559\u20131567 (2018)","journal-title":"Nat. Med."},{"key":"38_CR6","doi-asserted-by":"publisher","first-page":"27","DOI":"10.1111\/myc.12251","volume":"58","author":"S Jeelani","year":"2015","unstructured":"Jeelani, S., et al.: Histopathological examination of nail clippings using PAS staining (HPE-PAS): gold standard in diagnosis of Onychomycosis. Mycoses 58, 27\u201332 (2015)","journal-title":"Mycoses"},{"key":"38_CR7","unstructured":"Johnson, J., Douze, M., J\u00e9gou, H.: Billion-scale similarity search with GPUs. arXiv preprint arXiv:1702.08734 (2017)"},{"issue":"1","key":"38_CR8","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":"38_CR9","doi-asserted-by":"publisher","unstructured":"Kather, J.N., Halama, N., Marx, A.: 100,000 histological images of human colorectal cancer and healthy tissue. Version v0.1, April 2018. https:\/\/doi.org\/10.5281\/zenodo.1214456","DOI":"10.5281\/zenodo.1214456"},{"key":"38_CR10","doi-asserted-by":"publisher","unstructured":"Kather, D.J.N., et al.: Collection of textures in colorectal cancer histology, May 2016. https:\/\/doi.org\/10.5281\/zenodo.53169","DOI":"10.5281\/zenodo.53169"},{"key":"38_CR11","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1007\/978-3-030-59710-8_1","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2020","author":"H Li","year":"2020","unstructured":"Li, H., Yin, Z.: Attention, suggestion and annotation: a deep active learning framework for biomedical image segmentation. In: Martel, A.L., et al. (eds.) MICCAI 2020. LNCS, vol. 12261, pp. 3\u201313. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-59710-8_1"},{"key":"38_CR12","doi-asserted-by":"publisher","first-page":"945","DOI":"10.1109\/TMI.2018.2875868","volume":"38","author":"W Li","year":"2019","unstructured":"Li, W., et al.: Path R-CNN for prostate cancer diagnosis and Gleason grading of histological images. IEEE Trans. Med. Imaging 38, 945\u2013954 (2019)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"38_CR13","doi-asserted-by":"publisher","first-page":"e102","DOI":"10.1186\/1479-5876-10-102","volume":"10","author":"H Liao","year":"2020","unstructured":"Liao, H., et al.: Deep learning-based classification and mutation prediction from histopathological images of hepatocellular carcinoma. Clin. Transl. Med. 10, e102 (2020)","journal-title":"Clin. Transl. Med."},{"key":"38_CR14","doi-asserted-by":"publisher","first-page":"27","DOI":"10.4103\/jpi.jpi_5_20","volume":"11","author":"M Lindvall","year":"2020","unstructured":"Lindvall, M., et al.: TissueWand, a rapid histopathology annotation tool. J. Pathol. Inform. 11, 27 (2020)","journal-title":"J. Pathol. Inform."},{"key":"38_CR15","unstructured":"Musgrave, K., Belongie, S., Lim, S.-N.: PyTorch metric learning. arXiv: 2008.09164 [cs.CV] (2020)"},{"key":"38_CR16","doi-asserted-by":"publisher","first-page":"14588","DOI":"10.1038\/s41598-017-15092-3","volume":"7","author":"M Nalisnik","year":"2017","unstructured":"Nalisnik, M., et al.: Interactive phenotyping of large-scale histology imaging data with HistomicsML. Sci. Rep. 7, 14588 (2017)","journal-title":"Sci. Rep."},{"key":"38_CR17","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":"38_CR18","doi-asserted-by":"publisher","first-page":"122","DOI":"10.5281\/zenodo.3632035","volume":"56","author":"A Pol\u00f3nia","year":"2019","unstructured":"Pol\u00f3nia, A., Eloy, C., Aguiar, P.: BACH dataset: grand challenge on breast cancer histology images. Med. Image Anal. 56, 122\u2013139 (2019). https:\/\/doi.org\/10.5281\/zenodo.3632035","journal-title":"Med. Image Anal."},{"key":"38_CR19","doi-asserted-by":"publisher","first-page":"26995","DOI":"10.1007\/s11042-020-09292-9","volume":"79","author":"L Putzu","year":"2020","unstructured":"Putzu, L., Piras, L., Giacinto, G.: Convolutional neural networks for relevance feedback in content based image retrieval. Multimedia Tools Appl. 79, 26995\u201327021 (2020)","journal-title":"Multimedia Tools Appl."},{"key":"38_CR20","doi-asserted-by":"publisher","first-page":"14347","DOI":"10.1038\/s41598-019-50587-1","volume":"9","author":"\u0141 Raczkowski","year":"2019","unstructured":"Raczkowski, \u0141, et al.: ARA: accurate, reliable and active histopathological image classification framework with Bayesian deep learning. Sci. Rep. 9, 14347 (2019)","journal-title":"Sci. Rep."},{"issue":"4","key":"38_CR21","doi-asserted-by":"publisher","first-page":"1149","DOI":"10.2214\/ajr.183.4.1831149","volume":"183","author":"F Sardanelli","year":"2004","unstructured":"Sardanelli, F., et al.: Sensitivity of MRI versus mammography for detecting foci of multifocal, multicentric breast cancer in Fatty and dense breasts using the whole-breast pathologic examination as a gold standard. AJR Am J. Roentgenol. 183(4), 1149\u201357 (2004)","journal-title":"AJR Am J. Roentgenol."},{"key":"38_CR22","doi-asserted-by":"publisher","first-page":"2395","DOI":"10.1109\/TMI.2020.2971006","volume":"39","author":"M Shaban","year":"2020","unstructured":"Shaban, M., et al.: Context-aware convolutional neural network for grading of colorectal cancer histology images. IEEE Trans. Med. Imaging 39, 2395\u20132405 (2020)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"38_CR23","doi-asserted-by":"crossref","unstructured":"Shen, Y., Ke, J.: Representative region based active learning for histological classification of colorectal cancer. In: 2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI), pp. 1730\u20131733 (2021)","DOI":"10.1109\/ISBI48211.2021.9433931"},{"key":"38_CR24","doi-asserted-by":"publisher","first-page":"10509","DOI":"10.1038\/s41598-019-46718-3","volume":"9","author":"S Tabibu","year":"2019","unstructured":"Tabibu, S., Vinod, P.K., Jawahar, C.: Pan-Renal Cell Carcinoma classification and survival prediction from histopathology images using deep learning. Sci. Rep. 9, 10509 (2019)","journal-title":"Sci. Rep."},{"key":"38_CR25","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"399","DOI":"10.1007\/978-3-319-66179-7_46","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2017","author":"L Yang","year":"2017","unstructured":"Yang, L., Zhang, Y., Chen, J., Zhang, S., Chen, D.Z.: Suggestive annotation: a deep active learning framework for biomedical image segmentation. In: Descoteaux, M., Maier-Hein, L., Franz, A., Jannin, P., Collins, D.L., Duchesne, S. (eds.) MICCAI 2017. LNCS, vol. 10435, pp. 399\u2013407. Springer, Cham (2017). https:\/\/doi.org\/10.1007\/978-3-319-66179-7_46"}],"container-title":["Lecture Notes in Computer Science","Pattern Recognition"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-02444-3_38","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,5,9]],"date-time":"2022-05-09T12:09:47Z","timestamp":1652098187000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-02444-3_38"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783031024436","9783031024443"],"references-count":25,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-02444-3_38","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2022]]},"assertion":[{"value":"10 May 2022","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ACPR","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Asian Conference on Pattern Recognition","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Jeju Island","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Korea (Republic of)","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2021","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"9 November 2021","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"12 November 2021","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"6","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"acpr2021","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/www.acpr2021.org","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Single-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":"154","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":"85","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":"55% - 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":"4","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)"}}]}}