{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,25]],"date-time":"2025-03-25T19:15:06Z","timestamp":1742930106537,"version":"3.40.3"},"publisher-location":"Singapore","reference-count":50,"publisher":"Springer Singapore","isbn-type":[{"type":"print","value":"9789811540172"},{"type":"electronic","value":"9789811540189"}],"license":[{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2020]]},"DOI":"10.1007\/978-981-15-4018-9_4","type":"book-chapter","created":{"date-parts":[[2020,3,28]],"date-time":"2020-03-28T11:02:43Z","timestamp":1585393363000},"page":"36-48","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["A Study on Deep Learning for Breast Cancer Detection in Histopathological Images"],"prefix":"10.1007","author":[{"given":"Oinam Vivek","family":"Singh","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Prakash","family":"Choudhary","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Khelchandra","family":"Thongam","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2020,3,29]]},"reference":[{"unstructured":"Breast cancer: prevention and control. http:\/\/www.who.int\/cancer\/detection\/breastcancer\/en\/ . Accessed 13 Feb 2018","key":"4_CR1"},{"doi-asserted-by":"crossref","unstructured":"Neal, L., Tortorelli, C.L., Nassar, A.: Clinician\u2019s guide to imaging and pathologic findings in benign breast disease. In: Mayo Clinic Proceedings, vol. 85, pp. 274\u2013279 (2010)","key":"4_CR2","DOI":"10.4065\/mcp.2009.0656"},{"issue":"3","key":"4_CR3","doi-asserted-by":"publisher","first-page":"521","DOI":"10.2214\/ajr.158.3.1310825","volume":"158","author":"DB Kopans","year":"1992","unstructured":"Kopans, D.B.: The positive predictive value of mammography. Am. J. Roentgenol. 158(3), 521\u2013526 (1992)","journal-title":"Am. J. Roentgenol."},{"issue":"3","key":"4_CR4","doi-asserted-by":"publisher","first-page":"641","DOI":"10.1148\/radiol.2533082308","volume":"253","author":"JG Elmore","year":"2009","unstructured":"Elmore, J.G., et al.: Variability in interpretive performance at screening mammography and radiologists characteristics associated with accuracy. Radiology 253(3), 641\u2013651 (2009)","journal-title":"Radiology"},{"issue":"5","key":"4_CR5","doi-asserted-by":"publisher","first-page":"1400","DOI":"10.1109\/TBME.2014.2303852","volume":"61","author":"M Veta","year":"2014","unstructured":"Veta, M., Pluim, J.P., vanDiest, P.J., Viergever, M.A.: Breast cancer histopathology image analysis: a review. IEEE Trans. Biomed. Eng. 61(5), 1400\u20131411 (2014)","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"4_CR6","doi-asserted-by":"publisher","first-page":"147","DOI":"10.1109\/RBME.2009.2034865","volume":"2","author":"MN Gurcan","year":"2009","unstructured":"Gurcan, M.N., Boucheron, L.E., Can, A., Madabhushi, A., Rajpoot, N.M., Yener, B.: Histopathological image analysis: a review. IEEE Rev. Biomed. Eng. 2, 147\u2013171 (2009)","journal-title":"IEEE Rev. Biomed. Eng."},{"unstructured":"Ng, A.: Sparse autoencoder. In: CS294A LectureNotes, vol. 72, pp. 1\u201319. Stanford University (2011)","key":"4_CR7"},{"unstructured":"Salakhutdinov, R., Hinton, G.E.: Deep Boltzmann machines. In: Proceedings of The Twelfth International Conference on Artificial Intelligence and Statistics (AIS-TATS), vol. 5, pp. 448\u2013455 (2009)","key":"4_CR8"},{"issue":"4","key":"4_CR9","doi-asserted-by":"publisher","first-page":"193","DOI":"10.1007\/BF00344251","volume":"36","author":"K Fukushima","year":"1980","unstructured":"Fukushima, K.: Neocognitron: a self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position. Biol. Cybern. 36(4), 193\u2013202 (1980). https:\/\/doi.org\/10.1007\/BF00344251","journal-title":"Biol. Cybern."},{"key":"4_CR10","doi-asserted-by":"publisher","first-page":"711","DOI":"10.1109\/42.476112","volume":"14","author":"S-C Lo","year":"1995","unstructured":"Lo, S.-C., Lou, S.-L., Lin, J.-S., Freedman, M.T., Chien, M.V., Mun, S.K.: Artificial convolution neural network techniques and applications for lung nodule detection. IEEE Trans. Med. Imaging 14, 711\u2013718 (1995)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"4_CR11","doi-asserted-by":"publisher","first-page":"2278","DOI":"10.1109\/5.726791","volume":"86","author":"Y LeCun","year":"1998","unstructured":"LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proc. IEEE 86, 2278\u20132324 (1998)","journal-title":"Proc. IEEE"},{"unstructured":"Krizhevsky, A., Sutskever, I., Hinton, G.: Imagenet classification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems, pp. 1097\u20131105 (2012)","key":"4_CR12"},{"issue":"3","key":"4_CR13","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s11263-015-0816-y","volume":"115","author":"O Russakovsky","year":"2014","unstructured":"Russakovsky, O., et al.: ImageNet large scale visual recognition challenge. Int. J. Comput. Vis. 115(3), 1\u201342 (2014). https:\/\/doi.org\/10.1007\/s11263-015-0816-y","journal-title":"Int. J. Comput. Vis."},{"issue":"5","key":"4_CR14","doi-asserted-by":"publisher","first-page":"403","DOI":"10.1111\/j.1365-2559.1991.tb00229.x","volume":"19","author":"CW Elston","year":"1991","unstructured":"Elston, C.W., Ellis, I.: Pathological prognostic factors in breast cancer. I. The value of histological grade in breast cancer: experience from a large study with long-term follow-up. Histopathology 19(5), 403\u2013410 (1991)","journal-title":"Histopathology"},{"doi-asserted-by":"crossref","unstructured":"LeCun, Y., Kavukcuoglu, K., Farabet, C.: Convolutional networks and applications in vision. In: Proceedings of IEEE International Symposium on Circuits and Systems (ISCAS), pp. 253\u2013256 (2010)","key":"4_CR15","DOI":"10.1109\/ISCAS.2010.5537907"},{"issue":"1","key":"4_CR16","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1561\/2200000006","volume":"2","author":"Y Bengio","year":"2009","unstructured":"Bengio, Y.: Learning deep architectures for AI. Found. Trends\u00ae Mach. Learn. 2(1), 1\u2013127 (2009)","journal-title":"Found. Trends\u00ae Mach. Learn."},{"key":"4_CR17","series-title":"Communications in Computer and Information Science","doi-asserted-by":"publisher","first-page":"398","DOI":"10.1007\/978-3-319-60964-5_35","volume-title":"Medical Image Understanding and Analysis","author":"A Hamidinekoo","year":"2017","unstructured":"Hamidinekoo, A., Suhail, Z., Qaiser, T., Zwiggelaar, R.: Investigating the effect of various augmentations on the input data fed to a convolutional neural network for the task of mammographic mass classification. In: Vald\u00e9s Hern\u00e1ndez, M., Gonz\u00e1lez-Castro, V. (eds.) MIUA 2017. CCIS, vol. 723, pp. 398\u2013409. Springer, Cham (2017). https:\/\/doi.org\/10.1007\/978-3-319-60964-5_35"},{"key":"4_CR18","doi-asserted-by":"publisher","first-page":"85","DOI":"10.1016\/j.neunet.2014.09.003","volume":"61","author":"J Schmidhuber","year":"2015","unstructured":"Schmidhuber, J.: Deeplearning in neural networks: an overview. Neural Netw. 61, 85\u2013117 (2015)","journal-title":"Neural Netw."},{"unstructured":"Krizhevsky, A., Hinton, G.: Learning multiple layers of features from tiny images (2009)","key":"4_CR19"},{"unstructured":"Glorot, X., Bordes, A., Bengio, Y.: Deep sparse rectifier neural networks. In: 14th International Conference on Artificial Intelligence and Statistics, vol. 15, pp. 315\u2013323 (2011)","key":"4_CR20"},{"key":"4_CR21","volume-title":"Deep Learning","author":"I Goodfellow","year":"2016","unstructured":"Goodfellow, I., Bengio, Y., Courville, A.: Deep Learning. MIT Press, Cambridge (2016)"},{"doi-asserted-by":"crossref","unstructured":"Dahl, G.E., Sainath, T.N., Hinton, G.E.: Improving deep neural networks for LVCSR using rectified linear units and drop out. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 8609\u20138613 (2013)","key":"4_CR22","DOI":"10.1109\/ICASSP.2013.6639346"},{"issue":"1","key":"4_CR23","first-page":"1929","volume":"15","author":"N Srivastava","year":"2014","unstructured":"Srivastava, N., Hinton, G.E., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Drop out: a simple way to prevent neural networks from over fitting. J. Mach. Learn. Res. 15(1), 1929\u20131958 (2014)","journal-title":"J. Mach. Learn. Res."},{"unstructured":"Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems, pp. 1097\u20131105 (2012)","key":"4_CR24"},{"doi-asserted-by":"crossref","unstructured":"Xu, J., Xiang, L., Hang, R., Wu, J.: Stacked sparse autoencoder (SSAE) based framework for nuclei patch classification on breast cancer histopathology. In: IEEE 11th International Symposium on Biomedical Imaging (ISBI), pp. 999\u20131002 (2014)","key":"4_CR25","DOI":"10.1109\/ISBI.2014.6868041"},{"issue":"1","key":"4_CR26","doi-asserted-by":"publisher","first-page":"119","DOI":"10.1109\/TMI.2015.2458702","volume":"35","author":"J Xu","year":"2016","unstructured":"Xu, J., et al.: Stacked sparse auto encoder (SSAE) for nuclei detection of breast cancer histopathology images. IEEE Trans. Med. Imaging 35(1), 119\u2013130 (2016)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"4_CR27","doi-asserted-by":"publisher","first-page":"50","DOI":"10.1016\/j.compmedimag.2016.05.003","volume":"57","author":"A Janowczyk","year":"2017","unstructured":"Janowczyk, A., Basavanhally, A., Madabhushi, A.: Stain normalization using sparse auto encoders (StaNoSA): application to digital pathology. Comput. Med. Imaging Graph. 57, 50\u201361 (2017)","journal-title":"Comput. Med. Imaging Graph."},{"doi-asserted-by":"publisher","unstructured":"Janowczyk, A., Madabhushi, A.: Deep learning for digital pathology image analysis: a comprehensive tutorial with selected use cases. J. Pathol. Inform. (2016). https:\/\/doi.org\/10.4103\/2153-3539.186902","key":"4_CR28","DOI":"10.4103\/2153-3539.186902"},{"issue":"2","key":"4_CR29","doi-asserted-by":"publisher","first-page":"550","DOI":"10.1109\/TMI.2015.2481436","volume":"35","author":"F Xing","year":"2016","unstructured":"Xing, F., Xie, Y., Yang, L.: Anautomatic learning-based framework for robust nucleus segmentation. IEEE Trans. Med. Imaging 35(2), 550\u2013566 (2016)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"4_CR30","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"632","DOI":"10.1007\/978-3-319-46723-8_73","volume-title":"Medical Image Computing and Computer-Assisted Intervention \u2013 MICCAI 2016","author":"M Veta","year":"2016","unstructured":"Veta, M., van Diest, P.J., Pluim, J.P.W.: Cutting out the middleman: measuring nuclear area in histopathology slides without segmentation. In: Ourselin, S., Joskowicz, L., Sabuncu, Mert R., Unal, G., Wells, W. (eds.) MICCAI 2016. LNCS, vol. 9901, pp. 632\u2013639. Springer, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-46723-8_73"},{"key":"4_CR31","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"358","DOI":"10.1007\/978-3-319-24574-4_43","volume-title":"Medical Image Computing and Computer-Assisted Intervention \u2013 MICCAI 2015","author":"Y Xie","year":"2015","unstructured":"Xie, Y., Xing, F., Kong, X., Su, H., Yang, L.: Beyond classification: structured regression for robust cell detection using convolutional neural network. In: Navab, N., Hornegger, J., Wells, William M., Frangi, Alejandro F. (eds.) MICCAI 2015. LNCS, vol. 9351, pp. 358\u2013365. Springer, Cham (2015). https:\/\/doi.org\/10.1007\/978-3-319-24574-4_43"},{"doi-asserted-by":"crossref","unstructured":"Romo-Bucheli, D., Janowczyk, A., Romero, E., Gilmore, H., Madabhushi, A.:. Automated tubule nuclei quantification and correlation with oncotype DX risk categories in ER+breast cancer whole slide images. In: SPIE Medical Imaging, p. 979106. International Society for Optics and Photonics (2016)","key":"4_CR32","DOI":"10.1117\/12.2211368"},{"key":"4_CR33","doi-asserted-by":"publisher","first-page":"214","DOI":"10.1016\/j.neucom.2016.01.034","volume":"191","author":"J Xu","year":"2016","unstructured":"Xu, J., Luo, X., Wang, G., Gilmore, H., Madabhushi, A.: A deep convolutional neural network for segmenting and classifying epithelial and stromal regions in histopathological images. Neurocomputing 191, 214\u2013223 (2016)","journal-title":"Neurocomputing"},{"unstructured":"Bejnordi, B.E., et al.: Deep learning-based assessment of tumor associated stroma for diagnosing breast cancer in histopathology images. arXiv preprint arXiv:1702.05803 (2017)","key":"4_CR34"},{"key":"4_CR35","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"411","DOI":"10.1007\/978-3-642-40763-5_51","volume-title":"Medical Image Computing and Computer-Assisted Intervention \u2013 MICCAI 2013","author":"DC Cire\u015fan","year":"2013","unstructured":"Cire\u015fan, D.C., Giusti, A., Gambardella, L.M., Schmidhuber, J.: Mitosis detection in breast cancer histology images with deep neural networks. In: Mori, K., Sakuma, I., Sato, Y., Barillot, C., Navab, N. (eds.) MICCAI 2013. LNCS, vol. 8150, pp. 411\u2013418. Springer, Heidelberg (2013). https:\/\/doi.org\/10.1007\/978-3-642-40763-5_51"},{"issue":"1","key":"4_CR36","doi-asserted-by":"publisher","first-page":"237","DOI":"10.1016\/j.media.2014.11.010","volume":"20","author":"M Veta","year":"2015","unstructured":"Veta, M., et al.: Assessment of algorithms for mitosis detection in breast cancer histopathology images. Med. Image Anal. 20(1), 237\u2013248 (2015)","journal-title":"Med. Image Anal."},{"doi-asserted-by":"publisher","unstructured":"Wang, H., et al.: Cascaded ensemble of convolutional neural networks and handcrafted features for mitosis detection. In: SPIE Medical Imaging, vol. 9041, p. 90410B. International Society for Optics and Photonics (2014). https:\/\/doi.org\/10.1117\/12.2043902","key":"4_CR37","DOI":"10.1117\/12.2043902"},{"issue":"1","key":"4_CR38","doi-asserted-by":"publisher","first-page":"9","DOI":"10.4103\/2153-3539.112694","volume":"4","author":"CD Malon","year":"2013","unstructured":"Malon, C.D., Cosatto, E.: Classification of mitotic figures with convolutional neural networks and seeded blob features. Pathol. Inform. 4(1), 9 (2013). https:\/\/doi.org\/10.4103\/2153-3539.112694","journal-title":"Pathol. Inform."},{"doi-asserted-by":"crossref","unstructured":"Chen, H., Dou, Q., Wang, X., Qin, J., Heng, P.-A.: Mitosis detection in breast cancer histology images via deep cascaded networks. In: Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence, pp. 1160\u20131166. AAAI Press (2016)","key":"4_CR39","DOI":"10.1609\/aaai.v30i1.10140"},{"doi-asserted-by":"crossref","unstructured":"Jia, Y., et al.: Caffe: convolutional architecture for fast feature embedding. In: Proceedings of the 22nd ACM International Conference on Multimedia, pp. 675\u2013678. ACM (2014)","key":"4_CR40","DOI":"10.1145\/2647868.2654889"},{"doi-asserted-by":"crossref","unstructured":"Chen, H., Wang, X., Heng, P.A.: Automated mitosis detection with deep regression networks. In: 13th IEEE International Symposium on Biomedical Imaging (ISBI), pp. 1204\u20131207. IEEE (2016)","key":"4_CR41","DOI":"10.1109\/ISBI.2016.7493482"},{"issue":"5","key":"4_CR42","doi-asserted-by":"publisher","first-page":"1313","DOI":"10.1109\/TMI.2016.2528120","volume":"35","author":"S Albarqouni","year":"2016","unstructured":"Albarqouni, S., Baur, C., Achilles, F., Belagiannis, V., Demirci, S., Navab, N.: Aggnet: deep learning from crowds for mitosis detection in breast cancer histology images. IEEE Trans. Med. Imaging 35(5), 1313\u20131321 (2016)","journal-title":"IEEE Trans. Med. Imaging"},{"doi-asserted-by":"publisher","unstructured":"Cruz-Roa, A., et al.: Automatic detection of invasive ductal carcinoma in whole slide images with convolutional neural networks. In: SPIE Medical Imaging, vol. 9041. International Society for Optics and Photonics (2014). https:\/\/doi.org\/10.1117\/12.2043872","key":"4_CR43","DOI":"10.1117\/12.2043872"},{"unstructured":"Wang, D., Khosla, A., Gargeya, R., Irshad, H., Beck, A.H.: Deep learning for identifying metastatic breast cancer. arXiv preprint arXiv:1606.05718 (2016)","key":"4_CR44"},{"key":"4_CR45","doi-asserted-by":"publisher","first-page":"26286","DOI":"10.1038\/srep26286","volume":"6","author":"G Litjens","year":"2016","unstructured":"Litjens, G., et al.: Deep learning as a tool for increased accuracy and efficiency of histopathological diagnosis. Sci. Rep. 6, 26286 (2016). https:\/\/doi.org\/10.1038\/srep26286","journal-title":"Sci. Rep."},{"doi-asserted-by":"crossref","unstructured":"Giusti, A., Caccia, C., Cire\u015fari, D.C., Schmidhuber, J., Gambardella, L.M.: A comparison of algorithms and humans for mitosis detection. In: IEEE 11th International Symposium on Biomedical Imaging (ISBI), pp. 1360\u20131363. IEEE (2014)","key":"4_CR46","DOI":"10.1109\/ISBI.2014.6868130"},{"key":"4_CR47","doi-asserted-by":"publisher","first-page":"1170","DOI":"10.1093\/jnci\/84.15.1170","volume":"84","author":"N Boyd","year":"1992","unstructured":"Boyd, N., Jensen, H.M., Cooke, G., Han, H.L.: Relationship between mammographic and histological risk factors for breast cancer. J. Natl. Cancer Inst. 84, 1170\u20131179 (1992)","journal-title":"J. Natl. Cancer Inst."},{"unstructured":"Rao, S.: Mitos-rcnn: a novel approach to mitotic figure detection in breast cancer histopathology images using region based convolutional neural networks. arXiv preprint arXiv:1807.01788 (2018)","key":"4_CR48"},{"key":"4_CR49","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"827","DOI":"10.1007\/978-3-319-93000-8_94","volume-title":"Image Analysis and Recognition","author":"Y Guo","year":"2018","unstructured":"Guo, Y., Dong, H., Song, F., Zhu, C., Liu, J.: Breast cancer histology image classification based on deep neural networks. In: Campilho, A., Karray, F., ter Haar Romeny, B. (eds.) ICIAR 2018. LNCS, vol. 10882, pp. 827\u2013836. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-319-93000-8_94"},{"key":"4_CR50","first-page":"1","volume":"7","author":"S Akbar","year":"2018","unstructured":"Akbar, S., Peikari, M., Salama, S., Nofech-Mozes, S., Martel, A.: The transition module: a method for preventing over fitting in convolutional neural networks. Comput. Methods BioMech. Biomed. Eng. Imaging Vis. 7, 1\u20136 (2018)","journal-title":"Comput. Methods BioMech. Biomed. Eng. Imaging Vis."}],"container-title":["Communications in Computer and Information Science","Computer Vision and Image Processing"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-15-4018-9_4","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,10,19]],"date-time":"2022-10-19T22:19:52Z","timestamp":1666217992000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/978-981-15-4018-9_4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020]]},"ISBN":["9789811540172","9789811540189"],"references-count":50,"URL":"https:\/\/doi.org\/10.1007\/978-981-15-4018-9_4","relation":{},"ISSN":["1865-0929","1865-0937"],"issn-type":[{"type":"print","value":"1865-0929"},{"type":"electronic","value":"1865-0937"}],"subject":[],"published":{"date-parts":[[2020]]},"assertion":[{"value":"29 March 2020","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"CVIP","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Computer Vision and Image Processing","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Jaipur","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"India","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2019","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"27 September 2019","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"29 September 2019","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"4","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"cvip2019","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/cvip2019.mnit.ac.in\/","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":"EasyChair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"202","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":"73","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":"10","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":"36% - 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)"}}]}}