{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,15]],"date-time":"2026-06-15T13:46:55Z","timestamp":1781531215021,"version":"3.54.5"},"publisher-location":"Cham","reference-count":42,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031469138","type":"print"},{"value":"9783031469145","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\/978-3-031-46914-5_3","type":"book-chapter","created":{"date-parts":[[2023,10,30]],"date-time":"2023-10-30T07:02:43Z","timestamp":1698649363000},"page":"35-46","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Anatomy-Aware Masking for\u00a0Inpainting in\u00a0Medical Imaging"],"prefix":"10.1007","author":[{"given":"Yousef","family":"Yeganeh","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Azade","family":"Farshad","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nassir","family":"Navab","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2023,10,31]]},"reference":[{"issue":"11","key":"3_CR1","doi-asserted-by":"publisher","first-page":"2274","DOI":"10.1109\/TPAMI.2012.120","volume":"34","author":"R Achanta","year":"2012","unstructured":"Achanta, R., Shaji, A., Smith, K., Lucchi, A., Fua, P., S\u00fcsstrunk, S.: SLIC superpixels compared to state-of-the-art superpixel methods. IEEE Trans. Pattern Anal. Mach. Intell. 34(11), 2274\u20132282 (2012). https:\/\/doi.org\/10.1109\/TPAMI.2012.120","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"3_CR2","doi-asserted-by":"crossref","unstructured":"Armanious, K., Kumar, V., Abdulatif, S., Hepp, T., Gatidis, S., Yang, B.: ipA-MedGAN: inpainting of arbitrary regions in medical imaging. In: 2020 IEEE International Conference on Image Processing (ICIP). IEEE (2020)","DOI":"10.1109\/ICIP40778.2020.9191207"},{"key":"3_CR3","doi-asserted-by":"crossref","unstructured":"Armanious, K., Mecky, Y., Gatidis, S., Yang, B.: Adversarial inpainting of medical image modalities. In: ICASSP 2019\u20132019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE (2019)","DOI":"10.1109\/ICASSP.2019.8682677"},{"key":"3_CR4","unstructured":"Astaraki, M., et al.: Autopaint: A self-inpainting method for unsupervised anomaly detection. arXiv preprint arXiv:2305.12358 (2023)"},{"key":"3_CR5","doi-asserted-by":"crossref","unstructured":"Bertalmio, M., Sapiro, G., Caselles, V., Ballester, C.: Image inpainting. In: Proceedings of the 27th annual conference on Computer graphics and interactive techniques (2000)","DOI":"10.1145\/344779.344972"},{"key":"3_CR6","doi-asserted-by":"publisher","first-page":"529","DOI":"10.1007\/978-3-030-87202-1_51","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2021: 24th International Conference, Strasbourg, France, September 27\u2013October 1, 2021, Proceedings, Part IV","author":"C Bukas","year":"2021","unstructured":"Bukas, C., et al.: Patient-specific virtual spine straightening and vertebra inpainting: an automatic framework for osteoplasty planning. In: de Bruijne, M., et al. (eds.) Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2021: 24th International Conference, Strasbourg, France, September 27\u2013October 1, 2021, Proceedings, Part IV, pp. 529\u2013539. Springer, Cham (2021). https:\/\/doi.org\/10.1007\/978-3-030-87202-1_51"},{"key":"3_CR7","doi-asserted-by":"crossref","unstructured":"Dhamo, H., et al.: Semantic image manipulation using scene graphs. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 5213\u20135222 (2020)","DOI":"10.1109\/CVPR42600.2020.00526"},{"issue":"2","key":"3_CR8","doi-asserted-by":"publisher","first-page":"2007","DOI":"10.1007\/s11063-019-10163-0","volume":"51","author":"O Elharrouss","year":"2020","unstructured":"Elharrouss, O., Almaadeed, N., Al-Maadeed, S., Akbari, Y.: Image inpainting: a Review. Neural Process. Lett. 51(2), 2007\u20132028 (2020). https:\/\/doi.org\/10.1007\/s11063-019-10163-0","journal-title":"Neural Process. Lett."},{"key":"3_CR9","doi-asserted-by":"publisher","first-page":"45","DOI":"10.1007\/978-3-031-16852-9_5","volume-title":"Domain Adaptation and Representation Transfer: 4th MICCAI Workshop, DART 2022, Held in Conjunction with MICCAI 2022, Singapore, September 22, 2022, Proceedings","author":"A Farshad","year":"2022","unstructured":"Farshad, A., Makarevich, A., Belagiannis, V., Navab, N.: MetaMedSeg: volumetric meta-learning for\u00a0few-shot organ segmentation. In: Kamnitsa, K., et al. (eds.) Domain Adaptation and Representation Transfer: 4th MICCAI Workshop, DART 2022, Held in Conjunction with MICCAI 2022, Singapore, September 22, 2022, Proceedings, pp. 45\u201355. Springer, Cham (2022). https:\/\/doi.org\/10.1007\/978-3-031-16852-9_5"},{"key":"3_CR10","doi-asserted-by":"crossref","unstructured":"Farshad, A., Yeganeh, Y., Chi, Y., Shen, C., Ommer, B., Navab, N.: SceneGenie: scene graph guided diffusion models for image synthesis. arXiv preprint arXiv:2304.14573 (2023)","DOI":"10.1109\/ICCVW60793.2023.00016"},{"key":"3_CR11","unstructured":"Farshad, A., Yeganeh, Y., Dhamo, H., Tombari, F., Navab, N.: DisPositioNet: disentangled pose and identity in semantic image manipulation. In: 33rd British Machine Vision Conference 2022, BMVC 2022, London, UK, November 21\u201324, 2022. BMVA Press (2022)"},{"key":"3_CR12","doi-asserted-by":"publisher","first-page":"582","DOI":"10.1007\/978-3-031-16434-7_56","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2022: 25th International Conference, Singapore, September 18\u201322, 2022, Proceedings, Part II","author":"A Farshad","year":"2022","unstructured":"Farshad, A., Yeganeh, Y., Gehlbach, P., Navab, N.: Y-Net: a Spatiospectral dual-encoder network for\u00a0medical image segmentation. In: Wang, L., Dou, Q., Fletcher, P.T., Speidel, S., Li, S. (eds.) Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2022: 25th International Conference, Singapore, September 18\u201322, 2022, Proceedings, Part II, pp. 582\u2013592. Springer, Cham (2022). https:\/\/doi.org\/10.1007\/978-3-031-16434-7_56"},{"issue":"2","key":"3_CR13","doi-asserted-by":"publisher","first-page":"167","DOI":"10.1023\/B:VISI.0000022288.19776.77","volume":"59","author":"PF Felzenszwalb","year":"2004","unstructured":"Felzenszwalb, P.F., Huttenlocher, D.P.: Efficient graph-based image segmentation. Int. J. Comput. Vision 59(2), 167\u2013181 (2004). https:\/\/doi.org\/10.1023\/B:VISI.0000022288.19776.77","journal-title":"Int. J. Comput. Vision"},{"key":"3_CR14","doi-asserted-by":"crossref","unstructured":"Feng, Z., Chi, S., Yin, J., Zhao, D., Liu, X.: A variational approach to medical image inpainting based on mumford-shah model. In: 2007 International Conference on Service Systems and Service Management (2007)","DOI":"10.1109\/ICSSSM.2007.4280177"},{"issue":"1","key":"3_CR15","doi-asserted-by":"publisher","first-page":"012040","DOI":"10.1088\/1757-899X\/680\/1\/012040","volume":"680","author":"NV Gapon","year":"2019","unstructured":"Gapon, N.V., Voronin, V.V., Sizyakin, R.A., Bakaev, D., Skorikova, A.: Medical image inpainting using multi-scale patches and neural networks concepts. IOP Confer. Ser.: Mater. Sci. Eng. 680(1), 012040 (2019). https:\/\/doi.org\/10.1088\/1757-899X\/680\/1\/012040","journal-title":"IOP Confer. Ser.: Mater. Sci. Eng."},{"key":"3_CR16","doi-asserted-by":"publisher","unstructured":"Guizard, N., Nakamura, K., Coup\u00e9, P., Fonov, V.S., Arnold, D.L., Collins, D.L.: Non-local means inpainting of MS lesions in longitudinal image processing. Front. Neurosci. 9 (2015). https:\/\/doi.org\/10.3389\/fnins.2015.00456","DOI":"10.3389\/fnins.2015.00456"},{"key":"3_CR17","doi-asserted-by":"crossref","unstructured":"Guo, X., Yang, H., Huang, D.: Image inpainting via conditional texture and structure dual generation. In: ICCV (2021)","DOI":"10.1109\/ICCV48922.2021.01387"},{"key":"3_CR18","doi-asserted-by":"publisher","first-page":"69728","DOI":"10.1109\/ACCESS.2018.2877401","volume":"6","author":"M Isogawa","year":"2018","unstructured":"Isogawa, M., Mikami, D., Iwai, D., Kimata, H., Sato, K.: Mask optimization for image inpainting. IEEE Access 6, 69728\u201369741 (2018). https:\/\/doi.org\/10.1109\/ACCESS.2018.2877401","journal-title":"IEEE Access"},{"key":"3_CR19","doi-asserted-by":"publisher","first-page":"363","DOI":"10.1007\/978-3-030-01234-2_22","volume-title":"Computer Vision \u2013 ECCV 2018: 15th European Conference, Munich, Germany, September 8\u201314, 2018, Proceedings, Part VII","author":"V Jampani","year":"2018","unstructured":"Jampani, V., Sun, D., Liu, M.-Y., Yang, M.-H., Kautz, J.: Superpixel Sampling Networks. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) Computer Vision \u2013 ECCV 2018: 15th European Conference, Munich, Germany, September 8\u201314, 2018, Proceedings, Part VII, pp. 363\u2013380. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-01234-2_22"},{"key":"3_CR20","doi-asserted-by":"publisher","unstructured":"Kang, S.K., et al.: Deep learning-Based 3D inpainting of brain MR images. Sci. Rep. 11(1), (2021). https:\/\/doi.org\/10.1038\/s41598-020-80930-w","DOI":"10.1038\/s41598-020-80930-w"},{"key":"3_CR21","doi-asserted-by":"publisher","first-page":"101950","DOI":"10.1016\/j.media.2020.101950","volume":"69","author":"AE Kavur","year":"2021","unstructured":"Kavur, A.E., et al.: CHAOS Challenge - combined (CT-MR) healthy abdominal organ segmentation. Med. Image Anal. 69, 101950 (2021). https:\/\/doi.org\/10.1016\/j.media.2020.101950","journal-title":"Med. Image Anal."},{"issue":"5","key":"3_CR22","doi-asserted-by":"publisher","first-page":"2185","DOI":"10.1002\/mp.14701","volume":"48","author":"S Kim","year":"2021","unstructured":"Kim, S., Kim, B., Park, H.W.: Synthesis of brain tumor multicontrast MR images for improved data augmentation. Med. Phys. 48(5), 2185\u20132198 (2021). https:\/\/doi.org\/10.1002\/mp.14701","journal-title":"Med. Phys."},{"key":"3_CR23","unstructured":"Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. arxiv.org:1412.6980 (2014)"},{"key":"3_CR24","doi-asserted-by":"crossref","unstructured":"Li, W., Lin, Z., Zhou, K., Qi, L., Wang, Y., Jia, J.: Mat: mask-aware transformer for large hole image inpainting. In: CVPR (2022)","DOI":"10.1109\/CVPR52688.2022.01049"},{"key":"3_CR25","unstructured":"Li, Z., et al.: Superpixel masking and inpainting for self-supervised anomaly detection. In: BMVC (2020)"},{"key":"3_CR26","doi-asserted-by":"publisher","first-page":"89","DOI":"10.1007\/978-3-030-01252-6_6","volume-title":"Computer Vision \u2013 ECCV 2018: 15th European Conference, Munich, Germany, September 8-14, 2018, Proceedings, Part XI","author":"G Liu","year":"2018","unstructured":"Liu, G., Reda, F.A., Shih, K.J., Wang, T.-C., Tao, A., Catanzaro, B.: Image inpainting for irregular holes using partial convolutions. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) Computer Vision \u2013 ECCV 2018: 15th European Conference, Munich, Germany, September 8-14, 2018, Proceedings, Part XI, pp. 89\u2013105. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-01252-6_6"},{"key":"3_CR27","doi-asserted-by":"publisher","first-page":"41","DOI":"10.1007\/978-3-030-59520-3_5","volume-title":"Simulation and Synthesis in Medical Imaging: 5th International Workshop, SASHIMI 2020, Held in Conjunction with MICCAI 2020, Lima, Peru, October 4, 2020, Proceedings","author":"JV Manj\u00f3n","year":"2020","unstructured":"Manj\u00f3n, J.V., et al.: Blind MRI brain lesion inpainting using deep learning. In: Burgos, N., Svoboda, D., Wolterink, J.M., Zhao, C. (eds.) Simulation and Synthesis in Medical Imaging: 5th International Workshop, SASHIMI 2020, Held in Conjunction with MICCAI 2020, Lima, Peru, October 4, 2020, Proceedings, pp. 41\u201349. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-59520-3_5"},{"key":"3_CR28","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1155\/2010\/814319","volume":"2010","author":"M Arnold","year":"2010","unstructured":"Arnold, M., Ghosh, A., Ameling, S., Lacey, G.: Automatic segmentation and inpainting of specular highlights for endoscopic imaging. EURASIP J. Image Video Process. 2010, 1\u201312 (2010). https:\/\/doi.org\/10.1155\/2010\/814319","journal-title":"EURASIP J. Image Video Process."},{"issue":"5","key":"3_CR29","doi-asserted-by":"publisher","first-page":"577","DOI":"10.1002\/cpa.3160420503","volume":"42","author":"D Mumford","year":"1989","unstructured":"Mumford, D., Shah, J.: Optimal approximations by piecewise smooth functions and associated variational problems. Commun. Pure Appl. Math. 42(5), 577\u2013685 (1989). https:\/\/doi.org\/10.1002\/cpa.3160420503","journal-title":"Commun. Pure Appl. Math."},{"key":"3_CR30","doi-asserted-by":"publisher","first-page":"762","DOI":"10.1007\/978-3-030-58526-6_45","volume-title":"Computer Vision \u2013 ECCV 2020: 16th European Conference, Glasgow, UK, August 23\u201328, 2020, Proceedings, Part XXIX","author":"C Ouyang","year":"2020","unstructured":"Ouyang, C., Biffi, C., Chen, C., Kart, T., Qiu, H., Rueckert, D.: Self-supervision with Superpixels: training few-shot medical image segmentation without annotation. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) Computer Vision \u2013 ECCV 2020: 16th European Conference, Glasgow, UK, August 23\u201328, 2020, Proceedings, Part XXIX, pp. 762\u2013780. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-58526-6_45"},{"key":"3_CR31","doi-asserted-by":"crossref","unstructured":"Rojas, D.J.B., Fernandes, B.J.T., Fernandes, S.M.M.: A review on image inpainting techniques and datasets. In: 2020 33rd SIBGRAPI Conference on Graphics, Patterns and Images (SIBGRAPI). IEEE (2020)","DOI":"10.1109\/SIBGRAPI51738.2020.00040"},{"key":"3_CR32","unstructured":"Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv:1409.1556 (2014)"},{"key":"3_CR33","doi-asserted-by":"crossref","unstructured":"Suvorov, R., et al.: Resolution-robust large mask inpainting with Fourier convolutions. In: WACV (2022)","DOI":"10.1109\/WACV51458.2022.00323"},{"key":"3_CR34","doi-asserted-by":"publisher","DOI":"10.1145\/3426020.3426088","volume-title":"Deep Learning-Based Inpainting for Chest X-Ray Image","author":"MT Tran","year":"2020","unstructured":"Tran, M.T., Kim, S.H., Yang, H.J., Lee, G.S.: Deep Learning-Based Inpainting for Chest X-Ray Image. Association for Computing Machinery, New York, NY, USA (2020)"},{"issue":"9","key":"3_CR35","doi-asserted-by":"publisher","first-page":"4247","DOI":"10.3390\/app11094247","volume":"11","author":"M-T Tran","year":"2021","unstructured":"Tran, M.-T., Kim, S.-H., Yang, H.-J., Lee, G.-S.: Multi-task learning for medical image inpainting based on organ boundary awareness. Appl. Sci. 11(9), 4247 (2021). https:\/\/doi.org\/10.3390\/app11094247","journal-title":"Appl. Sci."},{"key":"3_CR36","doi-asserted-by":"publisher","first-page":"e453","DOI":"10.7717\/peerj.453","volume":"2","author":"S van der Walt","year":"2014","unstructured":"van der Walt, S., et al.: scikit-image: image processing in Python. PeerJ 2, e453 (2014). https:\/\/doi.org\/10.7717\/peerj.453","journal-title":"PeerJ"},{"key":"3_CR37","doi-asserted-by":"publisher","first-page":"110027","DOI":"10.1016\/j.measurement.2021.110027","volume":"185","author":"Q Wang","year":"2021","unstructured":"Wang, Q., Chen, Y., Zhang, N., Gu, Y.: Medical image inpainting with edge and structure priors. Measurement 185, 110027 (2021). https:\/\/doi.org\/10.1016\/j.measurement.2021.110027","journal-title":"Measurement"},{"issue":"4","key":"3_CR38","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). https:\/\/doi.org\/10.1109\/TIP.2003.819861","journal-title":"IEEE Trans. Image Process."},{"key":"3_CR39","doi-asserted-by":"crossref","unstructured":"Yang, F., Sun, Q., Jin, H., Zhou, Z.: Superpixel segmentation with fully convolutional networks. In: CVPR (2020)","DOI":"10.1109\/CVPR42600.2020.01398"},{"key":"3_CR40","unstructured":"Yeganeh, Y., et al.: Scope: structural continuity preservation for medical image segmentation. arXiv preprint arXiv:2304.14572 (2023)"},{"key":"3_CR41","doi-asserted-by":"crossref","unstructured":"Yeganeh, Y., Farshad, A., Weinberger, P., Ahmadi, S.A., Adeli, E., Navab, N.: Transformers pay attention to convolutions leveraging emerging properties of vits by dual attention-image network. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision. pp. 2304\u20132315 (2023)","DOI":"10.1109\/ICCVW60793.2023.00244"},{"key":"3_CR42","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"}],"container-title":["Lecture Notes in Computer Science","Shape in Medical Imaging"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-46914-5_3","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,11,1]],"date-time":"2024-11-01T01:03:45Z","timestamp":1730423025000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-46914-5_3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031469138","9783031469145"],"references-count":42,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-46914-5_3","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":"31 October 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ShapeMI","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Workshop on Shape in Medical Imaging","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Vancouver, BC","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Canada","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2023","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"8 October 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"12 October 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"shapemi2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/shapemi.github.io\/","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":"CMT","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"27","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":"23","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":"85% - 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":"2","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)"}}]}}