{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,6]],"date-time":"2025-11-06T16:08:51Z","timestamp":1762445331496,"version":"3.40.3"},"publisher-location":"Cham","reference-count":22,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031164392"},{"type":"electronic","value":"9783031164408"}],"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.springernature.com\/gp\/researchers\/text-and-data-mining"},{"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.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022]]},"DOI":"10.1007\/978-3-031-16440-8_12","type":"book-chapter","created":{"date-parts":[[2022,9,15]],"date-time":"2022-09-15T09:30:11Z","timestamp":1663234211000},"page":"119-128","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["ChrSNet: Chromosome Straightening Using Self-attention Guided Networks"],"prefix":"10.1007","author":[{"given":"Sunyi","family":"Zheng","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jingxiong","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhongyi","family":"Shui","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chenglu","family":"Zhu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yunlong","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Pingyi","family":"Chen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lin","family":"Yang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2022,9,16]]},"reference":[{"key":"12_CR1","doi-asserted-by":"crossref","unstructured":"Armanious, K., Jiang, C., Abdulatif, S., K\u00fcstner, T., Gatidis, S., Yang, B.: Unsupervised medical image translation using cycle-medgan. In: 2019 27th European Signal Processing Conference (EUSIPCO), pp. 1\u20135. IEEE (2019)","DOI":"10.23919\/EUSIPCO.2019.8902799"},{"key":"12_CR2","doi-asserted-by":"publisher","DOI":"10.1016\/j.compmedimag.2019.101684","volume":"79","author":"K Armanious","year":"2020","unstructured":"Armanious, K., et al.: MEDGAN: medical image translation using GANs. Comput. Med. Imaging Graph. 79, 101684 (2020)","journal-title":"Comput. Med. Imaging Graph."},{"key":"12_CR3","doi-asserted-by":"crossref","unstructured":"Arora, T., Dhir, R., Mahajan, M.: An algorithm to straighten the bent human chromosomes. In: 2017 Fourth International Conference on Image Information Processing (ICIIP), pp. 1\u20136. IEEE (2017)","DOI":"10.1109\/ICIIP.2017.8313772"},{"key":"12_CR4","unstructured":"Chen, J., et al.: Transunet: transformers make strong encoders for medical image segmentation. arXiv preprint arXiv:2102.04306 (2021)"},{"issue":"7","key":"12_CR5","doi-asserted-by":"publisher","first-page":"2553","DOI":"10.1109\/TMI.2020.2974159","volume":"39","author":"M Eslami","year":"2020","unstructured":"Eslami, M., Tabarestani, S., Albarqouni, S., Adeli, E., Navab, N., Adjouadi, M.: Image-to-images translation for multi-task organ segmentation and bone suppression in chest x-ray radiography. IEEE Trans. Med. Imaging 39(7), 2553\u20132565 (2020)","journal-title":"IEEE Trans. Med. Imaging"},{"issue":"11","key":"12_CR6","doi-asserted-by":"publisher","first-page":"2485","DOI":"10.1038\/s41436-019-0519-9","volume":"21","author":"IF J\u00f8rgensen","year":"2019","unstructured":"J\u00f8rgensen, I.F., et al.: Comorbidity landscape of the Danish patient population affected by chromosome abnormalities. Genet. Med. 21(11), 2485\u20132495 (2019)","journal-title":"Genet. Med."},{"key":"12_CR7","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"480","DOI":"10.1007\/978-3-030-87240-3_46","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2021","author":"Y Pan","year":"2021","unstructured":"Pan, Y., Chen, Y., Shen, D., Xia, Y.: Collaborative image synthesis and disease diagnosis for classification of neurodegenerative disorders with incomplete multi-modal neuroimages. In: de Bruijne, M., et al. (eds.) MICCAI 2021. LNCS, vol. 12905, pp. 480\u2013489. Springer, Cham (2021). https:\/\/doi.org\/10.1007\/978-3-030-87240-3_46"},{"key":"12_CR8","doi-asserted-by":"crossref","unstructured":"Poletti, E., Grisan, E., Ruggeri, A.: Automatic classification of chromosomes in q-band images. In: 2008 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 1911\u20131914. IEEE (2008)","DOI":"10.1109\/IEMBS.2008.4649560"},{"issue":"11","key":"12_CR9","doi-asserted-by":"publisher","first-page":"2569","DOI":"10.1109\/TMI.2019.2905841","volume":"38","author":"Y Qin","year":"2019","unstructured":"Qin, Y., et al.: Varifocal-net: a chromosome classification approach using deep convolutional networks. IEEE Trans. Med. Imaging 38(11), 2569\u20132581 (2019)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"12_CR10","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"234","DOI":"10.1007\/978-3-319-24574-4_28","volume-title":"Medical Image Computing and Computer-Assisted Intervention \u2013 MICCAI 2015","author":"O Ronneberger","year":"2015","unstructured":"Ronneberger, O., Fischer, P., Brox, T.: U-Net: convolutional networks for biomedical image segmentation. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9351, pp. 234\u2013241. Springer, Cham (2015). https:\/\/doi.org\/10.1007\/978-3-319-24574-4_28"},{"issue":"9","key":"12_CR11","doi-asserted-by":"publisher","first-page":"1208","DOI":"10.1016\/j.patrec.2008.01.029","volume":"29","author":"MJ Roshtkhari","year":"2008","unstructured":"Roshtkhari, M.J., Setarehdan, S.K.: A novel algorithm for straightening highly curved images of human chromosome. Pattern Recogn. Lett. 29(9), 1208\u20131217 (2008)","journal-title":"Pattern Recogn. Lett."},{"key":"12_CR12","doi-asserted-by":"crossref","unstructured":"Sharma, M., Saha, O., Sriraman, A., Hebbalaguppe, R., Vig, L., Karande, S.: Crowdsourcing for chromosome segmentation and deep classification. In: Proceedings of the IEEE conference on Computer Vision and Pattern Recognition Workshops, pp. 34\u201341 (2017)","DOI":"10.1109\/CVPRW.2017.109"},{"key":"12_CR13","doi-asserted-by":"publisher","first-page":"880","DOI":"10.1016\/j.measurement.2013.10.014","volume":"47","author":"D Somasundaram","year":"2014","unstructured":"Somasundaram, D., Kumar, V.V.: Straightening of highly curved human chromosome for cytogenetic analysis. Measurement 47, 880\u2013892 (2014)","journal-title":"Measurement"},{"key":"12_CR14","doi-asserted-by":"crossref","unstructured":"Song, S., et al.: A novel application of image-to-image translation: chromosome straightening framework by learning from a single image. In: 2021 14th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI), pp. 1\u20139. IEEE (2021)","DOI":"10.1109\/CISP-BMEI53629.2021.9624383"},{"key":"12_CR15","doi-asserted-by":"crossref","unstructured":"Tan, J., Zhao, S., Xiong, P., Liu, J., Fan, H., Liu, S.: Practical wide-angle portraits correction with deep structured models. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 3498\u20133506 (2021)","DOI":"10.1109\/CVPR46437.2021.00350"},{"key":"12_CR16","first-page":"159","volume":"3","author":"A Theisen","year":"2010","unstructured":"Theisen, A., Shaffer, L.G.: Disorders caused by chromosome abnormalities. Appl. Clin. Genet. 3, 159 (2010)","journal-title":"Appl. Clin. Genet."},{"issue":"4","key":"12_CR17","doi-asserted-by":"publisher","first-page":"600","DOI":"10.1109\/TIP.2003.819861","volume":"13","author":"Z Wang","year":"2004","unstructured":"Wang, Z., Bovik, A.C., Sheikh, H.R., Simoncelli, E.P.: Image quality assessment: from error visibility to structural similarity. IEEE Trans. Image Process. 13(4), 600\u2013612 (2004)","journal-title":"IEEE Trans. Image Process."},{"key":"12_CR18","doi-asserted-by":"crossref","unstructured":"Xiao, L., Luo, C.: Deepacc: automate chromosome classification based on metaphase images using deep learning framework fused with priori knowledge. In: 2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI), pp. 607\u2013610. IEEE (2021)","DOI":"10.1109\/ISBI48211.2021.9433943"},{"key":"12_CR19","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"613","DOI":"10.1007\/978-3-030-87237-3_59","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2021","author":"J Ye","year":"2021","unstructured":"Ye, J., Xue, Y., Liu, P., Zaino, R., Cheng, K.C., Huang, X.: A multi-attribute controllable generative model for histopathology image synthesis. In: de Bruijne, M., et al. (eds.) MICCAI 2021. LNCS, vol. 12908, pp. 613\u2013623. Springer, Cham (2021). https:\/\/doi.org\/10.1007\/978-3-030-87237-3_59"},{"key":"12_CR20","doi-asserted-by":"crossref","unstructured":"Zhang, R., Isola, P., Efros, A.A., Shechtman, E., Wang, O.: The unreasonable effectiveness of deep features as a perceptual metric. In: CVPR (2018)","DOI":"10.1109\/CVPR.2018.00068"},{"key":"12_CR21","doi-asserted-by":"crossref","unstructured":"Zhang, W., et al.: Chromosome classification with convolutional neural network based deep learning. In: 2018 11th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI). pp. 1\u20135. IEEE (2018)","DOI":"10.1109\/CISP-BMEI.2018.8633228"},{"key":"12_CR22","doi-asserted-by":"crossref","unstructured":"Zhu, F., Zhao, S., Wang, P., Wang, H., Yan, H., Liu, S.: Semi-supervised wide-angle portraits correction by multi-scale transformer. arXiv preprint arXiv:2109.08024 (2021)","DOI":"10.1109\/CVPR52688.2022.01907"}],"container-title":["Lecture Notes in Computer Science","Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2022"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-16440-8_12","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,27]],"date-time":"2024-03-27T18:07:51Z","timestamp":1711562871000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-16440-8_12"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783031164392","9783031164408"],"references-count":22,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-16440-8_12","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":"16 September 2022","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"MICCAI","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Medical Image Computing and Computer-Assisted Intervention","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Singapore","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Singapore","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2022","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"18 September 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"22 September 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"25","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"miccai2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/conferences.miccai.org\/2022\/en\/","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 Conference","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"1831","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":"574","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":"31% - 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":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}