{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,24]],"date-time":"2026-09-24T20:03:23Z","timestamp":1790280203396,"version":"4.1.0"},"reference-count":42,"publisher":"Elsevier BV","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.elsevier.com\/tdm\/userlicense\/1.0\/"},{"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.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"content-domain":{"domain":["clinicalkey.com","clinicalkey.com.au","clinicalkey.es","clinicalkey.fr","clinicalkey.jp","elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Artificial Intelligence in Medicine"],"published-print":{"date-parts":[[2023,1]]},"DOI":"10.1016\/j.artmed.2022.102462","type":"journal-article","created":{"date-parts":[[2022,11,25]],"date-time":"2022-11-25T15:43:54Z","timestamp":1669391034000},"page":"102462","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":24,"special_numbering":"C","title":["ReCasNet: Improving consistency within the two-stage mitosis detection framework"],"prefix":"10.1016","volume":"135","author":[{"given":"Chawan","family":"Piansaddhayanaon","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sakun","family":"Santisukwongchote","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shanop","family":"Shuangshoti","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qingyi","family":"Tao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4117-3632","authenticated-orcid":false,"given":"Sira","family":"Sriswasdi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6104-4857","authenticated-orcid":false,"given":"Ekapol","family":"Chuangsuwanich","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.artmed.2022.102462_b1","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pone.0161286","article-title":"Mitosis counting in breast cancer: Object-level interobserver agreement and comparison to an automatic method","volume":"11","author":"Veta","year":"2016","journal-title":"PLOS ONE"},{"key":"10.1016\/j.artmed.2022.102462_b2","doi-asserted-by":"crossref","DOI":"10.1016\/j.compeleceng.2021.107038","article-title":"Mitosis detection techniques in h&e stained breast cancer pathological images: A comprehensive review","volume":"91","author":"Pan","year":"2021","journal-title":"Comput Electr Eng"},{"key":"10.1016\/j.artmed.2022.102462_b3","doi-asserted-by":"crossref","DOI":"10.1016\/j.media.2020.101813","article-title":"Deep neural network models for computational histopathology: A survey","volume":"67","author":"Srinidhi","year":"2021","journal-title":"Med Image Anal"},{"key":"10.1016\/j.artmed.2022.102462_b4","doi-asserted-by":"crossref","first-page":"214","DOI":"10.1177\/0300985819890686","article-title":"Computerized calculation of mitotic count distribution in canine cutaneous mast cell tumor sections: Mitotic Count Is Area dependent","volume":"57","author":"Bertram","year":"2019","journal-title":"Vet Pathol"},{"key":"10.1016\/j.artmed.2022.102462_b5","doi-asserted-by":"crossref","first-page":"8","DOI":"10.4103\/2153-3539.112693","article-title":"Mitosis detection in breast cancer histological images an ICPR 2012 contest","volume":"4","author":"Roux","year":"2013","journal-title":"J Pathol Inform"},{"key":"10.1016\/j.artmed.2022.102462_b6","article-title":"Assessment of algorithms for mitosis detection in breast cancer histopathology images","author":"Veta","year":"2014","journal-title":"Med Image Anal"},{"key":"10.1016\/j.artmed.2022.102462_b7","series-title":"MITOS-ATYPIA-14-dataset","year":"2021"},{"key":"10.1016\/j.artmed.2022.102462_b8","doi-asserted-by":"crossref","first-page":"111","DOI":"10.1016\/j.media.2019.02.012","article-title":"Predicting breast tumor proliferation from whole-slide images: The TUPAC16 challenge","volume":"54","author":"Veta","year":"2019","journal-title":"Med Image Anal"},{"issue":"1","key":"10.1016\/j.artmed.2022.102462_b9","doi-asserted-by":"crossref","first-page":"274","DOI":"10.1038\/s41597-019-0290-4","article-title":"A large-scale dataset for mitotic figure assessment on whole slide images of canine cutaneous mast cell tumor","volume":"6","author":"Bertram","year":"2019","journal-title":"Sci Data"},{"issue":"1","key":"10.1016\/j.artmed.2022.102462_b10","doi-asserted-by":"crossref","first-page":"417","DOI":"10.1038\/s41597-020-00756-z","article-title":"A completely annotated whole slide image dataset of canine breast cancer to aid human breast cancer research","volume":"7","author":"Aubreville","year":"2020","journal-title":"Sci Data"},{"key":"10.1016\/j.artmed.2022.102462_b11","doi-asserted-by":"crossref","unstructured":"Chen\u00a0H, Dou\u00a0Q, Wang\u00a0X, Qin\u00a0J, Heng\u00a0P. Mitosis Detection in Breast Cancer Histology Images via Deep Cascaded Networks. In: Proceedings of the AAAI conference on artificial intelligence, Vol. 30. 2016, (1). URL.","DOI":"10.1609\/aaai.v30i1.10140"},{"key":"10.1016\/j.artmed.2022.102462_b12","doi-asserted-by":"crossref","first-page":"121","DOI":"10.1016\/j.media.2017.12.002","article-title":"DeepMitosis: Mitosis detection via deep detection, verification and segmentation networks","volume":"45","author":"Li","year":"2018","journal-title":"Med Image Anal"},{"key":"10.1016\/j.artmed.2022.102462_b13","doi-asserted-by":"crossref","first-page":"68695","DOI":"10.1109\/ACCESS.2020.2983995","article-title":"MitosisNet: End-to-end mitotic cell detection by multi-task learning","volume":"8","author":"Alom","year":"2020","journal-title":"IEEE Access"},{"key":"10.1016\/j.artmed.2022.102462_b14","series-title":"A rotation and a translation suffice: Fooling CNNs with simple transformations","author":"Engstrom","year":"2019"},{"key":"10.1016\/j.artmed.2022.102462_b15","series-title":"Medical imaging 2013: digital pathology, Vol. 8676","first-page":"70","article-title":"Detecting mitotic figures in breast cancer histopathology images","author":"Veta","year":"2013"},{"key":"10.1016\/j.artmed.2022.102462_b16","unstructured":"Khan\u00a0AM, El-Daly\u00a0H, Rajpoot\u00a0NM. A Gamma-Gaussian mixture model for detection of mitotic cells in breast cancer histopathology images. In: Proceedings of the 21st international conference on pattern recognition (ICPR2012). 2012, p. 149\u201352."},{"key":"10.1016\/j.artmed.2022.102462_b17","unstructured":"Sommer\u00a0C, Fiaschi\u00a0L, Hamprecht\u00a0FA, Gerlich\u00a0DW. Learning-based mitotic cell detection in histopathological images. In: Proceedings of the 21st international conference on pattern recognition (ICPR2012). 2012, p. 2306\u20139."},{"issue":"11","key":"10.1016\/j.artmed.2022.102462_b18","doi-asserted-by":"crossref","first-page":"4041","DOI":"10.1109\/TIP.2015.2460455","article-title":"Mitosis detection for invasive breast cancer grading in histopathological images","volume":"24","author":"Paul","year":"2015","journal-title":"IEEE Trans Image Process"},{"key":"10.1016\/j.artmed.2022.102462_b19","doi-asserted-by":"crossref","first-page":"12","DOI":"10.4103\/2153-3539.112697","article-title":"Mitosis detection using generic features and an ensemble of cascade adaboosts","volume":"4","author":"Tek","year":"2013","journal-title":"J Pathol Inform"},{"key":"10.1016\/j.artmed.2022.102462_b20","unstructured":"Huang\u00a0C-H, Lee\u00a0H-K. Automated mitosis detection based on eXclusive Independent Component Analysis. In: Proceedings of the 21st international conference on pattern recognition (ICPR2012). 2012, p. 1856\u20139."},{"key":"10.1016\/j.artmed.2022.102462_b21","doi-asserted-by":"crossref","DOI":"10.1007\/s10916-017-0773-9","article-title":"Maximized inter-class weighted mean for fast and accurate mitosis cells detection in breast cancer histopathology images","volume":"41","author":"Nateghi","year":"2017","journal-title":"J Med Syst"},{"key":"10.1016\/j.artmed.2022.102462_b22","series-title":"Medical image computing and computer-assisted intervention \u2013 MICCAI 2015","first-page":"94","article-title":"Regenerative random forest with automatic feature selection to detect mitosis in histopathological breast cancer images","author":"Paul","year":"2015"},{"key":"10.1016\/j.artmed.2022.102462_b23","doi-asserted-by":"crossref","first-page":"60","DOI":"10.1016\/j.media.2017.07.005","article-title":"A survey on deep learning in medical image analysis","volume":"42","author":"Litjens","year":"2017","journal-title":"Med Image Anal"},{"key":"10.1016\/j.artmed.2022.102462_b24","doi-asserted-by":"crossref","first-page":"9","DOI":"10.4103\/2153-3539.112694","article-title":"Classification of mitotic figures with convolutional neural networks and seeded blob features","volume":"4","author":"Malon","year":"2013","journal-title":"J Pathol Inform"},{"key":"10.1016\/j.artmed.2022.102462_b25","series-title":"Medical image computing and computer-assisted intervention \u2013 MICCAI 2013","first-page":"411","article-title":"Mitosis detection in breast cancer histology images with deep neural networks","author":"Cire\u015fan","year":"2013"},{"key":"10.1016\/j.artmed.2022.102462_b26","doi-asserted-by":"crossref","DOI":"10.1038\/s41598-020-73246-2","article-title":"Deep learning algorithms out-perform veterinary pathologists in detecting the mitotically most active tumor region","volume":"10","author":"Aubreville","year":"2020","journal-title":"Sci Rep"},{"issue":"2","key":"10.1016\/j.artmed.2022.102462_b27","doi-asserted-by":"crossref","first-page":"211","DOI":"10.1177\/03009858211067478","article-title":"Computer-assisted mitotic count using a deep learning-based algorithm improves interobserver reproducibility and accuracy","volume":"59","author":"Bertram","year":"2021","journal-title":"Vet Pathol"},{"key":"10.1016\/j.artmed.2022.102462_b28","series-title":"OncoPetNet: A deep learning based AI system for mitotic figure counting on H&E stained whole slide digital images in a large veterinary diagnostic lab setting","author":"Fitzke","year":"2021"},{"key":"10.1016\/j.artmed.2022.102462_b29","series-title":"Advances in neural information processing systems, Vol. 28","article-title":"Faster R-CNN: Towards real-time object detection with region proposal networks","author":"Ren","year":"2015"},{"key":"10.1016\/j.artmed.2022.102462_b30","doi-asserted-by":"crossref","unstructured":"He\u00a0K, Zhang\u00a0X, Ren\u00a0S, Sun\u00a0J. Deep Residual Learning for Image Recognition. In: 2016 IEEE conference on computer vision and pattern recognition (CVPR). 2016, p. 770\u20138. http:\/\/dx.doi.org\/10.1109\/CVPR.2016.90.","DOI":"10.1109\/CVPR.2016.90"},{"key":"10.1016\/j.artmed.2022.102462_b31","doi-asserted-by":"crossref","unstructured":"Deng\u00a0J, Dong\u00a0W, Socher\u00a0R, Li\u00a0L, Kai Li, Li Fei-Fei. ImageNet: A large-scale hierarchical image database. In: 2009 IEEE conference on computer vision and pattern recognition. 2009, p. 248\u201355. http:\/\/dx.doi.org\/10.1109\/CVPR.2009.5206848.","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"10.1016\/j.artmed.2022.102462_b32","series-title":"MMDetection: Open mmlab detection toolbox and benchmark","author":"Chen","year":"2019"},{"key":"10.1016\/j.artmed.2022.102462_b33","series-title":"Proceedings of the 36th international conference on machine learning","first-page":"6105","article-title":"EfficientNet: Rethinking model scaling for convolutional neural networks","volume":"vol. 97","author":"Tan","year":"2019"},{"key":"10.1016\/j.artmed.2022.102462_b34","doi-asserted-by":"crossref","unstructured":"Liu\u00a0Z, Mao\u00a0H, Wu\u00a0C-Y, Feichtenhofer\u00a0C, Darrell\u00a0T, Xie\u00a0S. A convnet for the 2020s. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition. 2022, p. 11976\u201386.","DOI":"10.1109\/CVPR52688.2022.01167"},{"key":"10.1016\/j.artmed.2022.102462_b35","doi-asserted-by":"crossref","unstructured":"Lin\u00a0T-Y, Goyal\u00a0P, Girshick\u00a0R, He\u00a0K, Doll\u00e1r\u00a0P. Focal Loss for Dense Object Detection. In: 2017 IEEE international conference on computer vision (ICCV). 2017, p. 2999\u20133007. http:\/\/dx.doi.org\/10.1109\/ICCV.2017.324.","DOI":"10.1109\/ICCV.2017.324"},{"key":"10.1016\/j.artmed.2022.102462_b36","doi-asserted-by":"crossref","unstructured":"Cai\u00a0Z, Vasconcelos\u00a0N. Cascade r-cnn: Delving into high quality object detection. In: Proceedings of the IEEE conference on computer vision and pattern recognition. 2018, p. 6154\u201362.","DOI":"10.1109\/CVPR.2018.00644"},{"key":"10.1016\/j.artmed.2022.102462_b37","doi-asserted-by":"crossref","unstructured":"Chen\u00a0Q, Wang\u00a0Y, Yang\u00a0T, Zhang\u00a0X, Cheng\u00a0J, Sun\u00a0J. You only look one-level feature. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition. 2021, p. 13039\u201348.","DOI":"10.1109\/CVPR46437.2021.01284"},{"key":"10.1016\/j.artmed.2022.102462_b38","series-title":"Computer vision \u2013 ECCV 2014","first-page":"740","article-title":"Microsoft COCO: Common objects in context","author":"Lin","year":"2014"},{"key":"10.1016\/j.artmed.2022.102462_b39","doi-asserted-by":"crossref","first-page":"7","DOI":"10.1177\/0300985815593349","article-title":"Mitotic count and the field of view area: Time to standardize","volume":"53","author":"Meuten","year":"2016","journal-title":"Vet Pathol"},{"key":"10.1016\/j.artmed.2022.102462_b40","series-title":"TensorFlow: Large-scale machine learning on heterogeneous systems","author":"Abadi","year":"2015"},{"key":"10.1016\/j.artmed.2022.102462_b41","series-title":"Active learning literature survey","author":"Settles","year":"2009"},{"key":"10.1016\/j.artmed.2022.102462_b42","series-title":"6th international conference on learning representations, ICLR 2018, Vancouver, BC, Canada, april 30 - may 3, 2018, conference track proceedings","article-title":"Active learning for convolutional neural networks: A core-set approach","author":"Sener","year":"2018"}],"container-title":["Artificial Intelligence in Medicine"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0933365722002147?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0933365722002147?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2025,10,15]],"date-time":"2025-10-15T15:16:51Z","timestamp":1760541411000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0933365722002147"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,1]]},"references-count":42,"alternative-id":["S0933365722002147"],"URL":"https:\/\/doi.org\/10.1016\/j.artmed.2022.102462","relation":{},"ISSN":["0933-3657"],"issn-type":[{"value":"0933-3657","type":"print"}],"subject":[],"published":{"date-parts":[[2023,1]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"ReCasNet: Improving consistency within the two-stage mitosis detection framework","name":"articletitle","label":"Article Title"},{"value":"Artificial Intelligence in Medicine","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.artmed.2022.102462","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2022 Elsevier B.V. All rights reserved.","name":"copyright","label":"Copyright"}],"article-number":"102462"}}