{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,12]],"date-time":"2025-11-12T14:11:38Z","timestamp":1762956698271,"version":"3.40.3"},"publisher-location":"Singapore","reference-count":27,"publisher":"Springer Singapore","isbn-type":[{"type":"print","value":"9789811638794"},{"type":"electronic","value":"9789811638800"}],"license":[{"start":{"date-parts":[[2021,8,15]],"date-time":"2021-08-15T00:00:00Z","timestamp":1628985600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,8,15]],"date-time":"2021-08-15T00:00:00Z","timestamp":1628985600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022]]},"DOI":"10.1007\/978-981-16-3880-0_35","type":"book-chapter","created":{"date-parts":[[2021,8,14]],"date-time":"2021-08-14T11:02:58Z","timestamp":1628938978000},"page":"335-342","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Deep YOLO-Based Detection of Breast Cancer Mitotic-Cells in Histopathological Images"],"prefix":"10.1007","author":[{"given":"Maisun Mohamed","family":"Al Zorgani","sequence":"first","affiliation":[]},{"given":"Irfan","family":"Mehmood","sequence":"additional","affiliation":[]},{"given":"Hassan","family":"Ugail","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2021,8,15]]},"reference":[{"issue":"5","key":"35_CR1","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.O.: 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"},{"key":"35_CR2","doi-asserted-by":"publisher","first-page":"121","DOI":"10.1016\/j.media.2017.12.002","volume":"45","author":"C Li","year":"2018","unstructured":"Li, C., Wang, X., Liu, W., Latecki, L.J.: DeepMitosis: mitosis detection via deep detection, verification and segmentation networks. Med. Image Anal. 45, 121\u2013133 (2018)","journal-title":"Med. Image Anal."},{"key":"35_CR3","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, 237\u2013248 (2015)","journal-title":"Med. Image Anal."},{"key":"35_CR4","first-page":"1","volume":"4","author":"H Irshad","year":"2013","unstructured":"Irshad, H.: Automated mitosis detection in histopathology using morphological and multi-channel statistics features. J. Pathol. Inform. 4, 1\u20136 (2013)","journal-title":"J. Pathol. Inform."},{"key":"35_CR5","doi-asserted-by":"crossref","unstructured":"Tashk, A., Helfroush, M.S., Danyali, H., Akbarzadeh, M.: An automatic mitosis detection method for breast cancer histopathology slide images based on objective and pixel-wise textural features classification. In: KIT 2013, pp. 406\u2013410 (2013)","DOI":"10.1109\/IKT.2013.6620101"},{"key":"35_CR6","unstructured":"Sommer, C., Fiaschi, L., Hamprecht, F.A., Gerlich, D.W.: Learning-based mitotic cell detection in histopathological images. In: ICPR 2012, pp. 2306\u20132309 (2012)"},{"key":"35_CR7","doi-asserted-by":"crossref","unstructured":"Paul, A., Dey, A., Mukherjee, D.P., Sivaswamy, J., Tourani, V.: Regenerative random forest with automatic feature selection to detect mitosis in histopathological breast cancer images. In: MICCAI, pp. 94\u2013102. Springer (2015)","DOI":"10.1007\/978-3-319-24571-3_12"},{"key":"35_CR8","doi-asserted-by":"crossref","unstructured":"Cire\u015fan, D., Giusti, A., Gambardella, L., Schmidhuber, J.: Mitosis detection in breast cancer histology images with deep neural networks. In: MICCAI-2013, pp. 411\u2013418. Springer (2013)","DOI":"10.1007\/978-3-642-40763-5_51"},{"key":"35_CR9","unstructured":"Wang, H., et al.: Cascaded ensemble of convolutional neural networks and handcrafted features for mitosis detection. In: SPIE Medical Imaging, pp. 1\u201310"},{"key":"35_CR10","doi-asserted-by":"crossref","unstructured":"Chen, H., Dou, Q., Wang, X., Qin, J., Heng, P.: Mitosis detection in breast cancer histology images via deep cascaded networks. In: 13th AAAI Conference on Artificial Intelligence, pp. 1160\u20131166 (2016)","DOI":"10.1609\/aaai.v30i1.10140"},{"issue":"5","key":"35_CR11","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"},{"key":"35_CR12","doi-asserted-by":"publisher","first-page":"1","DOI":"10.4103\/2153-3539.112694","volume":"4","author":"C Malon","year":"2013","unstructured":"Malon, C., Cosatto, E.: Classification of mitotic figures with convolutional neural networks and seeded blob features. J. Pathol. Inform. 4, 1\u20135 (2013)","journal-title":"J. Pathol. Inform."},{"key":"35_CR13","doi-asserted-by":"publisher","first-page":"97","DOI":"10.1155\/2012\/385271","volume":"35","author":"C Malon","year":"2012","unstructured":"Malon, C., Brachtel, E., Cosatto, E., Graf, H.P., Kurata, A., et al.: Mitotic figure recognition: agreement among pathologists and computerized detector. Anal. Cell. Pathol. 35, 97\u2013100 (2012)","journal-title":"Anal. Cell. Pathol."},{"key":"35_CR14","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.W., Diest, V., Paul, J., Viergever, M.A.: Breast cancer histopathology image analysis: a review. IEEE Trans. Biomed. Eng. 61, 1400\u20131411 (2014)","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"35_CR15","doi-asserted-by":"crossref","unstructured":"Cai, D., Sun, X., Zhou, N., Han, X., Yao, J.: Efficient mitosis detection in breast cancer histology images by RCNN. In: IEEE 16th International Symposium on Biomedical Imaging, pp. 919\u2013922 (2019)","DOI":"10.1109\/ISBI.2019.8759461"},{"key":"35_CR16","doi-asserted-by":"crossref","unstructured":"Dodballapur, V., Song, Y., Huang, H., Chen, M., Chrzanowski, W., Cai, W.: Mask-driven mitosis detection in histopathology images. In: IEEE 16th International Symposium on Biomedical Imaging, pp. 1855\u20131859 (2019)","DOI":"10.1109\/ISBI.2019.8759164"},{"key":"35_CR17","doi-asserted-by":"crossref","unstructured":"Li, Y., Mercan, E., Knezevitch, S., Elmore, J.G., Shapiro, L.G.: Efficient and accurate mitosis detection\u2014a lightweight RCNN approach. In: 7th International Conference on Pattern Recognition Applications and Methods, pp. 69\u201377 (2018)","DOI":"10.5220\/0006550700690077"},{"key":"35_CR18","doi-asserted-by":"publisher","first-page":"1137","DOI":"10.1109\/TPAMI.2016.2577031","volume":"39","author":"S Ren","year":"2016","unstructured":"Ren, S., He, K., Girshick, R., Sun, J.: Faster R-CNN: towards real-time object detection with region proposal networks. IEEE Trans. Pattern Anal. Mach. Intell. 39, 1137\u20131149 (2016)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"35_CR19","doi-asserted-by":"crossref","unstructured":"Redmon, J., Divvala, S., Girshick, R., Farhadi, A.: You only look once: unified, real-time object detection. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 779\u2013788 (2016)","DOI":"10.1109\/CVPR.2016.91"},{"key":"35_CR20","doi-asserted-by":"crossref","unstructured":"Redmon, J., Farhadi, A.: YOLO9000: better, faster, stronger. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 7263\u20137271 (2017)","DOI":"10.1109\/CVPR.2017.690"},{"key":"35_CR21","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 770\u2013778 (2016)","DOI":"10.1109\/CVPR.2016.90"},{"key":"35_CR22","unstructured":"Mitosis Detection in Breast Cancer Histological Images. http:\/\/ludo17.free.fr\/mitos_2012\/download.html. Accessed 23 Nov 2020"},{"key":"35_CR23","first-page":"1","volume":"4","author":"L Roux","year":"2013","unstructured":"Roux, L., Racoceanu, D., Lom\u00e9nie, N., Kulikova, M., Irshad, H., Klossa, J., et al.: Mitosis detection in breast cancer histological images an ICPR 2012 contest. J. Pathol. Inf. 4, 1\u20137 (2013)","journal-title":"J. Pathol. Inf."},{"key":"35_CR24","doi-asserted-by":"publisher","first-page":"1729","DOI":"10.1109\/TBME.2014.2303294","volume":"61","author":"AM Khan","year":"2014","unstructured":"Khan, A.M., Rajpoot, N., Treanor, D., Magee, D.A.: Nonlinear mapping approach to stain normalization in digital histopathology images using image-specific color deconvolution. IEEE Trans. Bio. Eng. 61, 1729\u20131738 (2014)","journal-title":"IEEE Trans. Bio. Eng."},{"key":"35_CR25","unstructured":"Stain Normalisation Toolbox. https:\/\/warwick.ac.uk\/fac\/sci\/dcs\/research\/tia\/software\/sntoolbox\/. Accessed 12 Dec 2020"},{"key":"35_CR26","doi-asserted-by":"crossref","unstructured":"Miko\u0142ajczyk, A., Grochowski, M.: Data augmentation for improving deep learning in image classification problem. In: 2018 International Interdisciplinary Ph.D. Workshop (IIPhDW), pp. 117\u2013122 (2018)","DOI":"10.1109\/IIPHDW.2018.8388338"},{"key":"35_CR27","doi-asserted-by":"publisher","first-page":"60","DOI":"10.1186\/s40537-019-0197-0","volume":"6","author":"C Shorten","year":"2019","unstructured":"Shorten, C., Khoshgoftaar, T.M.: A survey on image data augmentation for deep learning. J. Big Data 6, 60 (2019)","journal-title":"J. Big Data"}],"container-title":["Lecture Notes in Electrical Engineering","Proceedings of 2021 International Conference on Medical Imaging and Computer-Aided Diagnosis (MICAD 2021)"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-16-3880-0_35","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,1,7]],"date-time":"2023-01-07T11:08:48Z","timestamp":1673089728000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-16-3880-0_35"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,8,15]]},"ISBN":["9789811638794","9789811638800"],"references-count":27,"URL":"https:\/\/doi.org\/10.1007\/978-981-16-3880-0_35","relation":{},"ISSN":["1876-1100","1876-1119"],"issn-type":[{"type":"print","value":"1876-1100"},{"type":"electronic","value":"1876-1119"}],"subject":[],"published":{"date-parts":[[2021,8,15]]},"assertion":[{"value":"15 August 2021","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"MICAD","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Medical Imaging and Computer-Aided Diagnosis","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Birmingham","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"United Kingdom","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2021","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"25 March 2021","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"26 March 2021","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"micad2021","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}