{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,12]],"date-time":"2026-03-12T14:24:38Z","timestamp":1773325478652,"version":"3.50.1"},"publisher-location":"Cham","reference-count":37,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031270659","type":"print"},{"value":"9783031270666","type":"electronic"}],"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\/978-3-031-27066-6_10","type":"book-chapter","created":{"date-parts":[[2023,3,8]],"date-time":"2023-03-08T06:02:54Z","timestamp":1678255374000},"page":"134-150","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Improving Segmentation of\u00a0Breast Arterial Calcifications from\u00a0Digital Mammography: Good Annotation is All You Need"],"prefix":"10.1007","author":[{"given":"Kaier","family":"Wang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Melissa","family":"Hill","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Seymour","family":"Knowles-Barley","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Aristarkh","family":"Tikhonov","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lester","family":"Litchfield","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"James Christopher","family":"Bare","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,3,9]]},"reference":[{"issue":"2","key":"10_CR1","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s12170-018-0568-7","volume":"12","author":"C Abouzeid","year":"2018","unstructured":"Abouzeid, C., Bhatt, D., Amin, N.: The top five women\u2019s health issues in preventive cardiology. Curr. Cardiovasc. Risk Rep. 12(2), 1\u20139 (2018). https:\/\/doi.org\/10.1007\/s12170-018-0568-7","journal-title":"Curr. Cardiovasc. Risk Rep."},{"issue":"5","key":"10_CR2","doi-asserted-by":"publisher","first-page":"e225","DOI":"10.1016\/j.crad.2013.01.007","volume":"68","author":"M Alakhras","year":"2013","unstructured":"Alakhras, M., Bourne, R., Rickard, M., Ng, K., Pietrzyk, M., Brennan, P.: Digital tomosynthesis: a new future for breast imaging? Clin. Radiol. 68(5), e225\u2013e236 (2013). https:\/\/doi.org\/10.1016\/j.crad.2013.01.007","journal-title":"Clin. Radiol."},{"issue":"10","key":"10_CR3","doi-asserted-by":"publisher","first-page":"3240","DOI":"10.1109\/TMI.2020.2989737","volume":"39","author":"M AlGhamdi","year":"2020","unstructured":"AlGhamdi, M., Abdel-Mottaleb, M., Collado-Mesa, F.: DU-Net: convolutional network for the detection of arterial calcifications in mammograms. IEEE Trans. Med. Imaging 39(10), 3240\u20133249 (2020). https:\/\/doi.org\/10.1109\/TMI.2020.2989737","journal-title":"IEEE Trans. Med. Imaging"},{"issue":"8","key":"10_CR4","doi-asserted-by":"publisher","first-page":"4040","DOI":"10.1002\/mp.12392","volume":"44","author":"W Branderhorst","year":"2017","unstructured":"Branderhorst, W., Groot, J.E., Lier, M.G., Highnam, R.P., Heeten, G.J., Grimbergen, C.A.: Technical note: validation of two methods to determine contact area between breast and compression paddle in mammography. Med. Phys. 44(8), 4040\u20134044 (2017). https:\/\/doi.org\/10.1002\/mp.12392","journal-title":"Med. Phys."},{"issue":"8","key":"10_CR5","doi-asserted-by":"publisher","first-page":"1094","DOI":"10.1161\/CIRCULATIONAHA.118.038092","volume":"139","author":"QM Bui","year":"2019","unstructured":"Bui, Q.M., Daniels, L.B.: A review of the role of breast arterial calcification for cardiovascular risk stratification in women. Circulation 139(8), 1094\u20131101 (2019). https:\/\/doi.org\/10.1161\/CIRCULATIONAHA.118.038092","journal-title":"Circulation"},{"issue":"2","key":"10_CR6","doi-asserted-by":"publisher","first-page":"125","DOI":"10.3390\/info11020125","volume":"11","author":"A Buslaev","year":"2020","unstructured":"Buslaev, A., Iglovikov, V.I., Khvedchenya, E., Parinov, A., Druzhinin, M., Kalinin, A.A.: Albumentations: fast and flexible image augmentations. Information 11(2), 125 (2020). https:\/\/doi.org\/10.3390\/info11020125","journal-title":"Information"},{"issue":"11","key":"10_CR7","doi-asserted-by":"publisher","first-page":"2143","DOI":"10.1109\/TMI.2012.2215880","volume":"31","author":"JZ Cheng","year":"2012","unstructured":"Cheng, J.Z., Chen, C.M., Cole, E.B., Pisano, E.D., Shen, D.: Automated delineation of calcified vessels in mammography by tracking with uncertainty and graphical linking techniques. IEEE Trans. Med. Imaging 31(11), 2143\u20132155 (2012). https:\/\/doi.org\/10.1109\/TMI.2012.2215880","journal-title":"IEEE Trans. Med. Imaging"},{"key":"10_CR8","doi-asserted-by":"publisher","unstructured":"Cheng, J.Z., Chen, C.M., Shen, D.: Identification of breast vascular calcium deposition in digital mammography by linear structure analysis. In: 2012 9th IEEE International Symposium on Biomedical Imaging (ISBI), Barcelona, Spain, pp. 126\u2013129. IEEE (2012). https:\/\/doi.org\/10.1109\/ISBI.2012.6235500","DOI":"10.1109\/ISBI.2012.6235500"},{"issue":"1","key":"10_CR9","doi-asserted-by":"publisher","DOI":"10.1117\/1.JMI.2.1.015501","volume":"2","author":"CN Damases","year":"2015","unstructured":"Damases, C.N., Brennan, P.C., McEntee, M.F.: Mammographic density measurements are not affected by mammography system. J. Med. Imaging 2(1), 015501 (2015). https:\/\/doi.org\/10.1117\/1.JMI.2.1.015501","journal-title":"J. Med. Imaging"},{"key":"10_CR10","doi-asserted-by":"publisher","unstructured":"Ge, J., et al.: Automated detection of breast vascular calcification on full-field digital mammograms. In: Giger, M.L., Karssemeijer, N. (eds.) Medical Imaging, San Diego, CA, p. 691517 (2008). https:\/\/doi.org\/10.1117\/12.773096","DOI":"10.1117\/12.773096"},{"issue":"10","key":"10_CR11","doi-asserted-by":"publisher","first-page":"5851","DOI":"10.1002\/mp.15017","volume":"48","author":"X Guo","year":"2021","unstructured":"Guo, X., et al.: SCU-Net: a deep learning method for segmentation and quantification of breast arterial calcifications on mammograms. Med. Phys. 48(10), 5851\u20135861 (2021). https:\/\/doi.org\/10.1002\/mp.15017","journal-title":"Med. Phys."},{"issue":"1","key":"10_CR12","doi-asserted-by":"publisher","first-page":"11","DOI":"10.1016\/j.atherosclerosis.2014.12.035","volume":"239","author":"EJE Hendriks","year":"2015","unstructured":"Hendriks, E.J.E., de Jong, P.A., van der Graaf, Y., Mali, W.P.T.M., van der Schouw, Y.T., Beulens, J.W.J.: Breast arterial calcifications: a systematic review and meta-analysis of their determinants and their association with cardiovascular events. Atherosclerosis 239(1), 11\u201320 (2015). https:\/\/doi.org\/10.1016\/j.atherosclerosis.2014.12.035","journal-title":"Atherosclerosis"},{"key":"10_CR13","series-title":"Computational Imaging and Vision","doi-asserted-by":"publisher","DOI":"10.1007\/978-94-011-4613-5","volume-title":"Mammographic Image Analysis","author":"R Highnam","year":"1999","unstructured":"Highnam, R., Brady, J.M.: Mammographic Image Analysis. Computational Imaging and Vision, Springer, Dordrecht (1999). https:\/\/doi.org\/10.1007\/978-94-011-4613-5"},{"key":"10_CR14","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"342","DOI":"10.1007\/978-3-642-13666-5_46","volume-title":"Digital Mammography","author":"R Highnam","year":"2010","unstructured":"Highnam, R., Brady, S.M., Yaffe, M.J., Karssemeijer, N., Harvey, J.: Robust breast composition measurement - VolparaTM. In: Mart\u00ed, J., Oliver, A., Freixenet, J., Mart\u00ed, R. (eds.) IWDM 2010. LNCS, vol. 6136, pp. 342\u2013349. Springer, Heidelberg (2010). https:\/\/doi.org\/10.1007\/978-3-642-13666-5_46"},{"key":"10_CR15","unstructured":"Iakubovskii, P.: Segmentation Models Pytorch (2020). https:\/\/github.com\/qubvel\/segmentation_models.pytorch"},{"key":"10_CR16","doi-asserted-by":"publisher","unstructured":"Iribarren, C., et al.: Breast arterial calcification: a novel cardiovascular risk enhancer among postmenopausal women. Circ. Cardiovasc. Imaging 15(3), e013526 (2022). https:\/\/doi.org\/10.1161\/CIRCIMAGING.121.013526","DOI":"10.1161\/CIRCIMAGING.121.013526"},{"issue":"5","key":"10_CR17","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s00138-020-01078-1","volume":"31","author":"S Kaur","year":"2020","unstructured":"Kaur, S., Aggarwal, H., Rani, R.: Hyper-parameter optimization of deep learning model for prediction of Parkinson\u2019s disease. Mach. Vis. Appl. 31(5), 1\u201315 (2020). https:\/\/doi.org\/10.1007\/s00138-020-01078-1","journal-title":"Mach. Vis. Appl."},{"key":"10_CR18","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"475","DOI":"10.1007\/978-3-319-29451-3_38","volume-title":"Image and Video Technology","author":"N Khan","year":"2016","unstructured":"Khan, N., Wang, K., Chan, A., Highnam, R.: Automatic BI-RADS classification of mammograms. In: Br\u00e4unl, T., McCane, B., Rivera, M., Yu, X. (eds.) PSIVT 2015. LNCS, vol. 9431, pp. 475\u2013487. Springer, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-29451-3_38"},{"key":"10_CR19","unstructured":"Knowles-Barley, S.F., Highnam, R.: Auto Gamma Correction. WIPO Patent WO2022079569 (2022)"},{"issue":"7","key":"10_CR20","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0236598","volume":"15","author":"SC Lee","year":"2020","unstructured":"Lee, S.C., Phillips, M., Bellinge, J., Stone, J., Wylie, E., Schultz, C.: Is breast arterial calcification associated with coronary artery disease?\u2014a systematic review and meta-analysis. PLoS ONE 15(7), e0236598 (2020). https:\/\/doi.org\/10.1371\/journal.pone.0236598","journal-title":"PLoS ONE"},{"key":"10_CR21","doi-asserted-by":"publisher","DOI":"10.3389\/fonc.2022.848271","volume":"12","author":"Y Li","year":"2022","unstructured":"Li, Y., Gu, H., Wang, H., Qin, P., Wang, J.: BUSnet: a deep learning model of breast tumor lesion detection for ultrasound images. Front. Oncol. 12, 848271 (2022). https:\/\/doi.org\/10.3389\/fonc.2022.848271","journal-title":"Front. Oncol."},{"key":"10_CR22","doi-asserted-by":"publisher","unstructured":"Liu, X., et al.: Advances in deep learning-based medical image analysis. Health Data Sci. 2021, 1\u201314 (2021). https:\/\/doi.org\/10.34133\/2021\/8786793","DOI":"10.34133\/2021\/8786793"},{"issue":"2","key":"10_CR23","doi-asserted-by":"publisher","first-page":"102","DOI":"10.1016\/j.zemedi.2018.11.002","volume":"29","author":"AS Lundervold","year":"2019","unstructured":"Lundervold, A.S., Lundervold, A.: An overview of deep learning in medical imaging focusing on MRI. Z. Med. Phys. 29(2), 102\u2013127 (2019). https:\/\/doi.org\/10.1016\/j.zemedi.2018.11.002","journal-title":"Z. Med. Phys."},{"key":"10_CR24","doi-asserted-by":"publisher","unstructured":"Marchesi, A., et al.: The effect of mammogram preprocessing on microcalcification detection with convolutional neural networks. In: 2017 IEEE 30th International Symposium on Computer-Based Medical Systems (CBMS), Thessaloniki, pp. 207\u2013212. IEEE (2017). https:\/\/doi.org\/10.1109\/CBMS.2017.29","DOI":"10.1109\/CBMS.2017.29"},{"issue":"3","key":"10_CR25","doi-asserted-by":"publisher","first-page":"275","DOI":"10.1016\/j.acra.2008.08.011","volume":"16","author":"S Molloi","year":"2009","unstructured":"Molloi, S., Mehraien, T., Iribarren, C., Smith, C., Ducote, J.L., Feig, S.A.: Reproducibility of breast arterial calcium mass quantification using digital mammography. Acad. Radiol. 16(3), 275\u2013282 (2009). https:\/\/doi.org\/10.1016\/j.acra.2008.08.011","journal-title":"Acad. Radiol."},{"issue":"4","key":"10_CR26","doi-asserted-by":"publisher","first-page":"1428","DOI":"10.1118\/1.2868756","volume":"35","author":"S Molloi","year":"2008","unstructured":"Molloi, S., Xu, T., Ducote, J., Iribarren, C.: Quantification of breast arterial calcification using full field digital mammography. Med. Phys. 35(4), 1428\u20131439 (2008). https:\/\/doi.org\/10.1118\/1.2868756","journal-title":"Med. Phys."},{"issue":"1","key":"10_CR27","doi-asserted-by":"publisher","first-page":"10930","DOI":"10.1038\/s41598-021-90428-8","volume":"11","author":"R Ranjbarzadeh","year":"2021","unstructured":"Ranjbarzadeh, R., Bagherian Kasgari, A., Jafarzadeh Ghoushchi, S., Anari, S., Naseri, M., Bendechache, M.: Brain tumor segmentation based on deep learning and an attention mechanism using MRI multi-modalities brain images. Sci. Rep. 11(1), 10930 (2021). https:\/\/doi.org\/10.1038\/s41598-021-90428-8","journal-title":"Sci. Rep."},{"key":"10_CR28","unstructured":"Riva, F.: Breast arterial calcifications: detection, visualization and quantification through a convolutional neural network. Thesis for Master of Sciences in Biomedical Engineering, Polytechnic University of Milan, Italy (2021)"},{"key":"10_CR29","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"273","DOI":"10.1007\/978-3-030-12029-0_30","volume-title":"Statistical Atlases and Computational Models of the Heart. Atrial Segmentation and LV Quantification Challenges","author":"N Savioli","year":"2019","unstructured":"Savioli, N., Montana, G., Lamata, P.: V-FCNN: volumetric fully convolution neural network for automatic atrial segmentation. In: Pop, M., Sermesant, M., Zhao, J., Li, S., McLeod, K., Young, A., Rhode, K., Mansi, T. (eds.) STACOM 2018. LNCS, vol. 11395, pp. 273\u2013281. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-12029-0_30"},{"issue":"8","key":"10_CR30","doi-asserted-by":"publisher","first-page":"2852","DOI":"10.3390\/s21082852","volume":"21","author":"PN Srinivasu","year":"2021","unstructured":"Srinivasu, P.N., SivaSai, J.G., Ijaz, M.F., Bhoi, A.K., Kim, W., Kang, J.J.: Classification of skin disease using deep learning neural networks with MobileNet V2 and LSTM. Sensors 21(8), 2852 (2021). https:\/\/doi.org\/10.3390\/s21082852","journal-title":"Sensors"},{"issue":"12","key":"10_CR31","doi-asserted-by":"publisher","first-page":"2183","DOI":"10.3390\/diagnostics11122183","volume":"11","author":"V Thambawita","year":"2021","unstructured":"Thambawita, V., Str\u00fcmke, I., Hicks, S.A., Halvorsen, P., Parasa, S., Riegler, M.A.: Impact of image resolution on deep learning performance in endoscopy image classification: an experimental study using a large dataset of endoscopic images. Diagnostics 11(12), 2183 (2021). https:\/\/doi.org\/10.3390\/diagnostics11122183","journal-title":"Diagnostics"},{"issue":"2","key":"10_CR32","doi-asserted-by":"publisher","first-page":"295","DOI":"10.1093\/ckj\/sfab178","volume":"15","author":"B Van Berkel","year":"2022","unstructured":"Van Berkel, B., Van Ongeval, C., Van Craenenbroeck, A.H., Pottel, H., De Vusser, K., Evenepoel, P.: Prevalence, progression and implications of breast artery calcification in patients with chronic kidney disease. Clin. Kidney J. 15(2), 295\u2013302 (2022). https:\/\/doi.org\/10.1093\/ckj\/sfab178","journal-title":"Clin. Kidney J."},{"issue":"12","key":"10_CR33","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0081653","volume":"8","author":"J Wang","year":"2013","unstructured":"Wang, J., Azziz, A., Fan, B., Malkov, S., Klifa, C., Newitt, D., Yitta, S., Hylton, N., Kerlikowske, K., Shepherd, J.A.: Agreement of mammographic measures of volumetric breast density to MRI. PLoS ONE 8(12), e81653 (2013). https:\/\/doi.org\/10.1371\/journal.pone.0081653","journal-title":"PLoS ONE"},{"issue":"5","key":"10_CR34","doi-asserted-by":"publisher","first-page":"1172","DOI":"10.1109\/TMI.2017.2655486","volume":"36","author":"J Wang","year":"2017","unstructured":"Wang, J., Ding, H., Bidgoli, F.A., Zhou, B., Iribarren, C., Molloi, S., Baldi, P.: Detecting cardiovascular disease from mammograms with deep learning. IEEE Trans. Med. Imaging 36(5), 1172\u20131181 (2017). https:\/\/doi.org\/10.1109\/TMI.2017.2655486","journal-title":"IEEE Trans. Med. Imaging"},{"key":"10_CR35","doi-asserted-by":"publisher","unstructured":"Wang, K., Khan, N., Highnam, R.: Automated segmentation of breast arterial calcifications from digital mammography. In: 2019 International Conference on Image and Vision Computing New Zealand (IVCNZ), Dunedin, New Zealand, pp. 1\u20136. IEEE (2019). https:\/\/doi.org\/10.1109\/IVCNZ48456.2019.8960956","DOI":"10.1109\/IVCNZ48456.2019.8960956"},{"issue":"6","key":"10_CR36","doi-asserted-by":"publisher","first-page":"796","DOI":"10.1016\/j.jacr.2020.01.006","volume":"17","author":"X Wang","year":"2020","unstructured":"Wang, X., Liang, G., Zhang, Y., Blanton, H., Bessinger, Z., Jacobs, N.: Inconsistent performance of deep learning models on mammogram classification. J. Am. Coll. Radiol. 17(6), 796\u2013803 (2020). https:\/\/doi.org\/10.1016\/j.jacr.2020.01.006","journal-title":"J. Am. Coll. Radiol."},{"issue":"17","key":"10_CR37","doi-asserted-by":"publisher","DOI":"10.1088\/1361-6560\/ab99e5","volume":"65","author":"S Yu","year":"2020","unstructured":"Yu, S., Chen, M., Zhang, E., Wu, J., Yu, H., Yang, Z., Ma, L., Gu, X., Lu, W.: Robustness study of noisy annotation in deep learning based medical image segmentation. Phys. Med. Biol. 65(17), 175007 (2020). https:\/\/doi.org\/10.1088\/1361-6560\/ab99e5","journal-title":"Phys. Med. Biol."}],"container-title":["Lecture Notes in Computer Science","Computer Vision \u2013 ACCV 2022 Workshops"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-27066-6_10","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,3,8]],"date-time":"2023-03-08T06:17:21Z","timestamp":1678256241000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-27066-6_10"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031270659","9783031270666"],"references-count":37,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-27066-6_10","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"9 March 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ACCV","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Asian Conference on Computer Vision","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Macao","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China","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":"4 December 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"8 December 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"16","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"accv2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/www.accv2022.org","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 Microsoft","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"836","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":"277","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":"33% - 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":"3.3","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":"2.6","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)"}},{"value":"For the ACCV 2022 workshops 25 papers have been accepted from 40 submissions","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)"}}]}}