{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,30]],"date-time":"2025-09-30T10:41:49Z","timestamp":1759228909059},"reference-count":33,"publisher":"SPIE","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022,4,4]]},"DOI":"10.1117\/12.2622862","type":"proceedings-article","created":{"date-parts":[[2022,3,18]],"date-time":"2022-03-18T22:04:07Z","timestamp":1647641047000},"page":"56","source":"Crossref","is-referenced-by-count":1,"title":["CrowdDeep: deep-learning from the crowd for nuclei segmentation"],"prefix":"10.1117","author":[{"given":"Parmida","family":"Ghahremani","sequence":"first","affiliation":[]},{"given":"Arie","family":"Kaufman","sequence":"additional","affiliation":[]}],"member":"189","reference":[{"key":"c1","doi-asserted-by":"publisher","DOI":"10.2105\/AJPH.2006.090902"},{"key":"c2","doi-asserted-by":"publisher","DOI":"10.1080\/02564602.2014.906971"},{"key":"c3","doi-asserted-by":"crossref","unstructured":"Smith, J. R., \u201cA virtual opinion,\u201d IEEE MultiMedia 19, 2\u20133 (Feb 2012).","DOI":"10.1109\/MMUL.2012.18"},{"key":"c4","first-page":"2961","article-title":"Mask R-CNN","volume-title":"Proceedings of the IEEE International Conference on Computer Vision","author":"He","year":"2017"},{"key":"c5","first-page":"3431","article-title":"Fully convolutional networks for semantic segmentation","volume-title":"Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition","author":"Long","year":"2015"},{"key":"c6","first-page":"2223","article-title":"Unpaired image-to-image translation using cycle-consistent adversarial networks","volume-title":"Proceedings of the IEEE International Conference on Computer Vision","author":"Zhu","year":"2017"},{"key":"c7","first-page":"933","article-title":"Nuclei segmentation in histopathology images using deep neural networks","volume-title":"IEEE 14th International Symposium on Biomedical Imaging (ISBI)","author":"Naylor","year":"2017"},{"key":"c8","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.42"},{"key":"c9","first-page":"228","article-title":"Nuclei segmentation with recurrent residual convolutional neural networks based U-Net (R2U-Net)","volume-title":"IEEE National Aerospace and Electronics Conference (NAECON)","author":"Alom","year":"2018"},{"key":"c10","doi-asserted-by":"publisher","DOI":"10.1007\/s11517-019-02008-8"},{"key":"c11","first-page":"208","article-title":"Mask-RCNN and U-Net ensembled for nuclei segmentation","volume-title":"IEEE 16th International Symposium on Biomedical Imaging (ISBI)","author":"Vuola","year":"2019"},{"key":"c12","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-32239-7"},{"key":"c13","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.42"},{"key":"c14","doi-asserted-by":"publisher","DOI":"10.1177\/0165551512437638"},{"key":"c15","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/2788993.2789841","article-title":"Measuring the crowd: A preliminary taxonomy of crowdsourcing metrics","volume-title":"Proceedings of the 11th International Symposium on Open Collaboration (OpenSys)","author":"Cullina","year":"2015"},{"key":"c16","article-title":"Crowdsourcing for identification of polyp-free segments in virtual colonoscopy videos","volume-title":"Proc. of SPIE Medical Imaging","author":"Park","year":"2017"},{"key":"c17","doi-asserted-by":"publisher","DOI":"10.1117\/12.2292563"},{"key":"c18","doi-asserted-by":"publisher","DOI":"10.1142\/9455"},{"key":"c19","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2016.2528120"},{"key":"c20","article-title":"More than fun and money. Worker motivation in crowdsourcing - a study on mechanical turk","author":"Kaufmann","year":"2011","journal-title":"AMCIS"},{"key":"c21","article-title":"Can we get rid of TREC assessors? Using mechanical turk for relevance assessment","author":"Alonso","year":"2009","journal-title":"SIGIR Workshop on The Future of IR Evaluation"},{"key":"c22","doi-asserted-by":"crossref","first-page":"557","DOI":"10.1145\/1743384.1743478","article-title":"How reliable are annotations via crowdsourcing: A study about inter-annotator agreement for multi-label image annotation","volume-title":"Proceedings of the International Conference on Multimedia Information Retrieval","author":"Nowak","year":"2010"},{"key":"c23","doi-asserted-by":"publisher","DOI":"10.1145\/1480506.1480508"},{"key":"c24","doi-asserted-by":"publisher","DOI":"10.1148\/radiol.11110938"},{"key":"c25","first-page":"57","article-title":"Turkit: Human computation algorithms on mechanical turk","volume-title":"Proceedings of the 23rd Annual ACM Symposium on User Interface Software and Technology (UIST)","author":"Little","year":"2010"},{"key":"c26","doi-asserted-by":"crossref","first-page":"43","DOI":"10.1145\/2047196.2047202","article-title":"Crowdforge: Crowdsourcing complex work","volume-title":"Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology","author":"Kittur","year":"2011"},{"key":"c27","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2020.107404"},{"key":"c28","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2017.2677499"},{"key":"c29","doi-asserted-by":"publisher","DOI":"10.1109\/ISBI.2009.5193250"},{"key":"c30","doi-asserted-by":"publisher","DOI":"10.1109\/38.946629"},{"key":"c31","article-title":"Deep convolutional gaussian mixture model for stain-color normalization of histopathological images","volume-title":"International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI)","author":"Zanjani","year":"2018"},{"key":"c32","first-page":"234","article-title":"U-Net: convolutional networks for biomedical image segmentation","author":"Ronneberger","year":"2015","journal-title":"Medical Image Computing and Computer-Assisted Intervention (MICCAI)"},{"key":"c33","article-title":"Retina blood vessel segmentation using a u-net based convolutional neural network","volume-title":"Proc. of International Conference on Data Science (ICDS)","author":"Xiancheng","year":"2018"}],"event":{"name":"Digital and Computational Pathology","start":{"date-parts":[[2022,2,20]]},"location":"San Diego, United States","end":{"date-parts":[[2022,3,28]]}},"container-title":["Medical Imaging 2022: Digital and Computational Pathology"],"original-title":[],"deposited":{"date-parts":[[2022,7,3]],"date-time":"2022-07-03T01:38:30Z","timestamp":1656812310000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.spiedigitallibrary.org\/conference-proceedings-of-spie\/12039\/2622862\/CrowdDeep-deep-learning-from-the-crowd-for-nuclei-segmentation\/10.1117\/12.2622862.full"}},"subtitle":[],"editor":[{"given":"Richard M.","family":"Levenson","sequence":"additional","affiliation":[]},{"given":"John E.","family":"Tomaszewski","sequence":"additional","affiliation":[]},{"given":"Aaron D.","family":"Ward","sequence":"additional","affiliation":[]}],"short-title":[],"issued":{"date-parts":[[2022,4,4]]},"references-count":33,"URL":"https:\/\/doi.org\/10.1117\/12.2622862","relation":{},"subject":[],"published":{"date-parts":[[2022,4,4]]}}}