{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,23]],"date-time":"2026-02-23T00:50:52Z","timestamp":1771807852494,"version":"3.50.1"},"publisher-location":"Cham","reference-count":27,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783030882099","type":"print"},{"value":"9783030882105","type":"electronic"}],"license":[{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"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":[[2021]]},"DOI":"10.1007\/978-3-030-88210-5_16","type":"book-chapter","created":{"date-parts":[[2021,9,29]],"date-time":"2021-09-29T06:14:35Z","timestamp":1632896075000},"page":"173-183","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":27,"title":["Compound Figure Separation of Biomedical Images with Side Loss"],"prefix":"10.1007","author":[{"given":"Tianyuan","family":"Yao","sequence":"first","affiliation":[]},{"given":"Chang","family":"Qu","sequence":"additional","affiliation":[]},{"given":"Quan","family":"Liu","sequence":"additional","affiliation":[]},{"given":"Ruining","family":"Deng","sequence":"additional","affiliation":[]},{"given":"Yuanhan","family":"Tian","sequence":"additional","affiliation":[]},{"given":"Jiachen","family":"Xu","sequence":"additional","affiliation":[]},{"given":"Aadarsh","family":"Jha","sequence":"additional","affiliation":[]},{"given":"Shunxing","family":"Bao","sequence":"additional","affiliation":[]},{"given":"Mengyang","family":"Zhao","sequence":"additional","affiliation":[]},{"given":"Agnes B.","family":"Fogo","sequence":"additional","affiliation":[]},{"given":"Bennett A.","family":"Landman","sequence":"additional","affiliation":[]},{"given":"Catie","family":"Chang","sequence":"additional","affiliation":[]},{"given":"Haichun","family":"Yang","sequence":"additional","affiliation":[]},{"given":"Yuankai","family":"Huo","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2021,9,25]]},"reference":[{"issue":"5","key":"16_CR1","doi-asserted-by":"publisher","first-page":"893","DOI":"10.1002\/asi.22810","volume":"64","author":"E Apostolova","year":"2013","unstructured":"Apostolova, E., You, D., Xue, Z., Antani, S., Demner-Fushman, D., Thoma, G.R.: Image retrieval from scientific publications: text and image content processing to separate multipanel figures. J. Am. Soc. Inform. Sci. Technol. 64(5), 893\u2013908 (2013)","journal-title":"J. Am. Soc. Inform. Sci. Technol."},{"key":"16_CR2","unstructured":"Bochkovskiy, A., Wang, C.Y., Liao, H.Y.M.: YOLOv4: optimal speed and accuracy of object detection. arXiv preprint arXiv:2004.10934 (2020)"},{"key":"16_CR3","doi-asserted-by":"publisher","first-page":"105273","DOI":"10.1016\/j.cmpb.2019.105273","volume":"184","author":"G Bueno","year":"2020","unstructured":"Bueno, G., Fernandez-Carrobles, M.M., Gonzalez-Lopez, L., Deniz, O.: Glomerulosclerosis identification in whole slide images using semantic segmentation. Comput. Methods Programs Biomed. 184, 105273 (2020)","journal-title":"Comput. Methods Programs Biomed."},{"key":"16_CR4","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-24211-8","volume-title":"Unsupervised Learning Algorithms","author":"ME Celebi","year":"2016","unstructured":"Celebi, M.E., Aydin, K.: Unsupervised Learning Algorithms. Springer, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-24211-8"},{"key":"16_CR5","unstructured":"Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597\u20131607. PMLR (2020)"},{"key":"16_CR6","doi-asserted-by":"crossref","unstructured":"Davila, K., Setlur, S., Doermann, D., Bhargava, U.K., Govindaraju, V.: Chart mining: a survey of methods for automated chart analysis. IEEE Trans. Pattern Anal. Mach. Intell. (2020)","DOI":"10.1109\/TPAMI.2020.2992028"},{"issue":"2","key":"16_CR7","doi-asserted-by":"publisher","first-page":"168","DOI":"10.5626\/JCSE.2012.6.2.168","volume":"6","author":"D Demner-Fushman","year":"2012","unstructured":"Demner-Fushman, D., Antani, S., Simpson, M., Thoma, G.R.: Design and development of a multimodal biomedical information retrieval system. J. Comput. Sci. Eng. 6(2), 168\u2013177 (2012)","journal-title":"J. Comput. Sci. Eng."},{"key":"16_CR8","unstructured":"Gadermayr, M., Dombrowski, A.K., Klinkhammer, B.M., Boor, P., Merhof, D.: CNN cascades for segmenting whole slide images of the kidney. arXiv preprint arXiv:1708.00251 (2017)"},{"issue":"10","key":"16_CR9","doi-asserted-by":"publisher","first-page":"1953","DOI":"10.1681\/ASN.2018121259","volume":"30","author":"B Ginley","year":"2019","unstructured":"Ginley, B., et al.: Computational segmentation and classification of diabetic glomerulosclerosis. J. Am. Soc. Nephrol. 30(10), 1953\u20131967 (2019)","journal-title":"J. Am. Soc. Nephrol."},{"key":"16_CR10","doi-asserted-by":"crossref","unstructured":"Govind, D., Ginley, B., Lutnick, B., Tomaszewski, J.E., Sarder, P.: Glomerular detection and segmentation from multimodal microscopy images using a butterworth band-pass filter. In: Medical Imaging 2018: Digital Pathology, vol. 10581, p. 1058114. International Society for Optics and Photonics (2018)","DOI":"10.1117\/12.2295446"},{"key":"16_CR11","unstructured":"Garc\u00eda Seco de Herrera, A., Schaer, R., Bromuri, S., M\u00fcller, H.: Overview of the ImageCLEF 2016 medical task. In: Working Notes of CLEF 2016 (Cross Language Evaluation Forum), September 2016"},{"key":"16_CR12","doi-asserted-by":"crossref","unstructured":"Huang, W., Tan, C.L., Leow, W.K.: Associating text and graphics for scientific chart understanding. In: Eighth International Conference on Document Analysis and Recognition (ICDAR 2005), pp. 580\u2013584. IEEE (2005)","DOI":"10.1109\/ICDAR.2005.54"},{"key":"16_CR13","doi-asserted-by":"publisher","first-page":"1309","DOI":"10.1016\/j.kint.2021.01.015","volume":"99","author":"Y Huo","year":"2021","unstructured":"Huo, Y., Deng, R., Liu, Q., Fogo, A.B., Yang, H.: AI applications in renal pathology. Kidney Int. 99, 1309\u20131320 (2021)","journal-title":"Kidney Int."},{"key":"16_CR14","doi-asserted-by":"crossref","unstructured":"Jiang, W., Schwenker, E., Spreadbury, T., Ferrier, N., Chan, M.K., Cossairt, O.: A two-stage framework for compound figure separation. arXiv preprint arXiv:2101.09903 (2021)","DOI":"10.1109\/ICIP42928.2021.9506171"},{"key":"16_CR15","doi-asserted-by":"publisher","first-page":"55","DOI":"10.1016\/j.compmedimag.2014.03.004","volume":"39","author":"J Kalpathy-Cramer","year":"2015","unstructured":"Kalpathy-Cramer, J., de Herrera, A.G.S., Demner-Fushman, D., Antani, S., Bedrick, S., M\u00fcller, H.: Evaluating performance of biomedical image retrieval systems\u2013an overview of the medical image retrieval task at ImageCLEF 2004\u20132013. Comput. Med. Imaging Graph. 39, 55\u201361 (2015)","journal-title":"Comput. Med. Imaging Graph."},{"issue":"7","key":"16_CR16","doi-asserted-by":"publisher","first-page":"955","DOI":"10.1016\/j.ekir.2019.04.008","volume":"4","author":"S Kannan","year":"2019","unstructured":"Kannan, S., et al.: Segmentation of glomeruli within trichrome images using deep learning. Kidney Int. Rep. 4(7), 955\u2013962 (2019)","journal-title":"Kidney Int. Rep."},{"issue":"4","key":"16_CR17","doi-asserted-by":"publisher","first-page":"379","DOI":"10.1093\/hmg\/11.4.379","volume":"11","author":"A Koziell","year":"2002","unstructured":"Koziell, A., et al.: Genotype\/phenotype correlations of NPHS1 and NPHS2 mutations in nephrotic syndrome advocate a functional inter-relationship in glomerular filtration. Hum. Mol. Genet. 11(4), 379\u2013388 (2002)","journal-title":"Hum. Mol. Genet."},{"key":"16_CR18","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"247","DOI":"10.1007\/978-3-319-27677-9_16","volume-title":"Pattern Recognition: Applications and Methods","author":"P-S Lee","year":"2015","unstructured":"Lee, P.-S., Howe, B.: Detecting and dismantling composite visualizations in the scientific literature. In: Fred, A., De Marsico, M., Figueiredo, M. (eds.) ICPRAM 2015. LNCS, vol. 9493, pp. 247\u2013266. Springer, Cham (2015). https:\/\/doi.org\/10.1007\/978-3-319-27677-9_16"},{"key":"16_CR19","doi-asserted-by":"crossref","unstructured":"Lee, P.S., Howe, B.: Dismantling composite visualizations in the scientific literature. In: ICPRAM (2), pp. 79\u201391. Citeseer (2015)","DOI":"10.5220\/0005213100790091"},{"issue":"7","key":"16_CR20","doi-asserted-by":"publisher","first-page":"1192","DOI":"10.1093\/bioinformatics\/btx611","volume":"34","author":"P Li","year":"2017","unstructured":"Li, P., Jiang, X., Kambhamettu, C., Shatkay, H.: Compound image segmentation of published biomedical figures. Bioinformatics 34(7), 1192\u20131199 (2017). https:\/\/doi.org\/10.1093\/bioinformatics\/btx611","journal-title":"Bioinformatics"},{"key":"16_CR21","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"199","DOI":"10.1007\/978-3-319-65813-1_20","volume-title":"Experimental IR Meets Multilinguality, Multimodality, and Interaction","author":"P Li","year":"2017","unstructured":"Li, P., Jiang, X., Kambhamettu, C., Shatkay, H.: Segmenting compound biomedical figures into their constituent panels. In: Jones, G.J.F., et al. (eds.) CLEF 2017. LNCS, vol. 10456, pp. 199\u2013210. Springer, Cham (2017). https:\/\/doi.org\/10.1007\/978-3-319-65813-1_20"},{"key":"16_CR22","doi-asserted-by":"crossref","unstructured":"Redmon, J., Divvala, S., Girshick, R., Farhadi, A.: You only look once: unified, real-time object detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 779\u2013788 (2016)","DOI":"10.1109\/CVPR.2016.91"},{"issue":"2","key":"16_CR23","doi-asserted-by":"publisher","first-page":"34","DOI":"10.14569\/IJARAI.2013.020206","volume":"2","author":"R Sathya","year":"2013","unstructured":"Sathya, R., Abraham, A.: Comparison of supervised and unsupervised learning algorithms for pattern classification. Int. J. Adv. Res. Artif. Intell. 2(2), 34\u201338 (2013)","journal-title":"Int. J. Adv. Res. Artif. Intell."},{"key":"16_CR24","doi-asserted-by":"crossref","unstructured":"Shi, X., Wu, Y., Cao, H., Burns, G., Natarajan, P.: Layout-aware subfigure decomposition for complex figures in the biomedical literature. In: 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), ICASSP 2019, pp. 1343\u20131347. IEEE (2019)","DOI":"10.1109\/ICASSP.2019.8683824"},{"key":"16_CR25","doi-asserted-by":"crossref","unstructured":"Tsutsui, S., Crandall, D.J.: A data driven approach for compound figure separation using convolutional neural networks. In: 2017 14th IAPR International Conference on Document Analysis and Recognition (ICDAR), vol. 1, pp. 533\u2013540. IEEE (2017)","DOI":"10.1109\/ICDAR.2017.93"},{"key":"16_CR26","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"408","DOI":"10.1007\/978-3-319-66179-7_47","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2017","author":"Y Zhang","year":"2017","unstructured":"Zhang, Y., Yang, L., Chen, J., Fredericksen, M., Hughes, D.P., Chen, D.Z.: Deep adversarial networks for biomedical image segmentation utilizing unannotated images. In: Descoteaux, M., Maier-Hein, L., Franz, A., Jannin, P., Collins, D.L., Duchesne, S. (eds.) MICCAI 2017. LNCS, vol. 10435, pp. 408\u2013416. Springer, Cham (2017). https:\/\/doi.org\/10.1007\/978-3-319-66179-7_47"},{"issue":"11","key":"16_CR27","first-page":"1327","volume":"71","author":"J Zou","year":"2020","unstructured":"Zou, J., Thoma, G., Antani, S.: Unified deep neural network for segmentation and labeling of multipanel biomedical figures. J. Am. Soc. Inf. Sci. 71(11), 1327\u20131340 (2020)","journal-title":"J. Am. Soc. Inf. Sci."}],"container-title":["Lecture Notes in Computer Science","Deep Generative Models, and Data Augmentation, Labelling, and Imperfections"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-88210-5_16","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,9,9]],"date-time":"2024-09-09T00:07:01Z","timestamp":1725840421000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-88210-5_16"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"ISBN":["9783030882099","9783030882105"],"references-count":27,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-88210-5_16","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021]]},"assertion":[{"value":"25 September 2021","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"DALI","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"MICCAI Workshop on Data Augmentation, Labelling, and Imperfections","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Strasbourg","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"France","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":"1 October 2021","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"1 October 2021","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"1","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"dali22021","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/dali-miccai.github.io\/","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":"Microsoft CMT","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"32","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":"15","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":"47% - 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 conference was held virtually due to the COVID-19 pandemic .","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)"}}]}}