{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,8]],"date-time":"2026-01-08T21:17:37Z","timestamp":1767907057828,"version":"3.49.0"},"publisher-location":"Cham","reference-count":30,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031376597","type":"print"},{"value":"9783031376603","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\/978-3-031-37660-3_37","type":"book-chapter","created":{"date-parts":[[2023,7,29]],"date-time":"2023-07-29T06:02:20Z","timestamp":1690610540000},"page":"529-538","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Transfer Learning in\u00a0Breast Mass Detection on\u00a0the\u00a0OMI-DB Dataset: A\u00a0Preliminary Study"],"prefix":"10.1007","author":[{"given":"Marya","family":"Ryspayeva","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mario","family":"Molinara","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Alessandro","family":"Bria","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Claudio","family":"Marrocco","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Francesco","family":"Tortorella","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,7,30]]},"reference":[{"key":"37_CR1","doi-asserted-by":"publisher","first-page":"219","DOI":"10.3233\/fi-2019-1829","volume":"168","author":"X Yu","year":"2019","unstructured":"Yu, X., Wang, S.-H.: Abnormality diagnosis in mammograms by transfer learning based on ResNet18. Fundam. Inform. 168, 219\u2013230 (2019). https:\/\/doi.org\/10.3233\/fi-2019-1829","journal-title":"Fundam. Inform."},{"key":"37_CR2","doi-asserted-by":"publisher","first-page":"1280","DOI":"10.1016\/j.jacr.2021.04.021","volume":"18","author":"DL Monticciolo","year":"2021","unstructured":"Monticciolo, D.L., et al.: Breast cancer screening recommendations inclusive of all women at average risk: Update from the ACR and Society of Breast Imaging. J. Am. Coll. Radiol. 18, 1280\u20131288 (2021). https:\/\/doi.org\/10.1016\/j.jacr.2021.04.021","journal-title":"J. Am. Coll. Radiol."},{"key":"37_CR3","doi-asserted-by":"publisher","first-page":"1137","DOI":"10.1016\/j.jacr.2017.06.001","volume":"14","author":"DL Monticciolo","year":"2017","unstructured":"Monticciolo, D.L., et al.: Breast cancer screening for average-risk women: recommendations from the ACR commission on breast imaging. J. Am. Coll. Radiol. 14, 1137\u20131143 (2017). https:\/\/doi.org\/10.1016\/j.jacr.2017.06.001","journal-title":"J. Am. Coll. Radiol."},{"key":"37_CR4","doi-asserted-by":"crossref","unstructured":"D\u2019Elia, C., Marrocco, C., Molinara, M., Tortorella, F.: Detection of clusters of microcalcifications in mammograms: a multi classifier approach. In: 2008 21st IEEE International Symposium on Computer-Based Medical Systems. IEEE (2008)","DOI":"10.1109\/CBMS.2008.102"},{"key":"37_CR5","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"415","DOI":"10.1007\/978-3-319-41546-8_52","volume-title":"Breast Imaging","author":"A Bria","year":"2016","unstructured":"Bria, A., Marrocco, C., Karssemeijer, N., Molinara, M., Tortorella, F.: Deep cascade classifiers to detect clusters of microcalcifications. In: Tingberg, A., L\u00e5ng, K., Timberg, P. (eds.) IWDM 2016. LNCS, vol. 9699, pp. 415\u2013422. Springer, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-41546-8_52"},{"key":"37_CR6","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"884","DOI":"10.1007\/11553595_108","volume-title":"Image Analysis and Processing \u2013 ICIAP 2005","author":"C Marrocco","year":"2005","unstructured":"Marrocco, C., Molinara, M., Tortorella, F.: Algorithms for detecting clusters of microcalcifications in mammograms. In: Roli, F., Vitulano, S. (eds.) ICIAP 2005. LNCS, vol. 3617, pp. 884\u2013891. Springer, Heidelberg (2005). https:\/\/doi.org\/10.1007\/11553595_108"},{"key":"37_CR7","doi-asserted-by":"publisher","DOI":"10.1016\/j.artmed.2019.101749","volume":"103","author":"B Savelli","year":"2020","unstructured":"Savelli, B., Bria, A., Molinara, M., Marrocco, C., Tortorella, F.: A multi-context CNN ensemble for small lesion detection. Artif. Intell. Med. 103, 101749 (2020). https:\/\/doi.org\/10.1016\/j.artmed.2019.101749","journal-title":"Artif. Intell. Med."},{"key":"37_CR8","doi-asserted-by":"publisher","first-page":"1857","DOI":"10.1109\/tmi.2018.2814058","volume":"37","author":"A Bria","year":"2018","unstructured":"Bria, A., et al.: Improving the automated detection of calcifications using adaptive variance stabilization. IEEE Trans. Med. Imaging 37, 1857\u20131864 (2018). https:\/\/doi.org\/10.1109\/tmi.2018.2814058","journal-title":"IEEE Trans. Med. Imaging"},{"key":"37_CR9","doi-asserted-by":"crossref","unstructured":"Marchesi, A., et al.: The effect of mammogram preprocessing on microcalcification detection with convolutional neural networks. In: 2017 IEEE 30th International Symposium on Computer-Based Medical Systems (CBMS). IEEE (2017)","DOI":"10.1109\/CBMS.2017.29"},{"key":"37_CR10","doi-asserted-by":"publisher","unstructured":"Halling-Brown, M.D., et al.: OPTIMAM mammography image database: a large-scale resource of mammography images and clinical data. Radiol. Artif. Intell. 3, e200103 (2021). https:\/\/doi.org\/10.1148\/ryai.2020200103","DOI":"10.1148\/ryai.2020200103"},{"key":"37_CR11","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2020.103774","volume":"121","author":"R Agarwal","year":"2020","unstructured":"Agarwal, R., D\u00edaz, O., Yap, M.H., Llad\u00f3, X., Mart\u00ed, R.: Deep learning for mass detection in full field digital mammograms. Comput. Biol. Med. 121, 103774 (2020). https:\/\/doi.org\/10.1016\/j.compbiomed.2020.103774","journal-title":"Comput. Biol. Med."},{"key":"37_CR12","doi-asserted-by":"crossref","unstructured":"Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: ImageNet: a large-scale hierarchical image database. In: 2009 IEEE Conference on Computer Vision and Pattern Recognition. IEEE (2009)","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"37_CR13","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"740","DOI":"10.1007\/978-3-319-10602-1_48","volume-title":"Computer Vision \u2013 ECCV 2014","author":"T-Y Lin","year":"2014","unstructured":"Lin, T.-Y., et al.: Microsoft COCO: common objects in context. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8693, pp. 740\u2013755. Springer, Cham (2014). https:\/\/doi.org\/10.1007\/978-3-319-10602-1_48"},{"key":"37_CR14","doi-asserted-by":"crossref","unstructured":"Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollar, P.: Focal loss for dense object detection. In: 2017 IEEE International Conference on Computer Vision (ICCV). IEEE (2017)","DOI":"10.1109\/ICCV.2017.324"},{"key":"37_CR15","doi-asserted-by":"crossref","unstructured":"Girshick, R., Donahue, J., Darrell, T., Malik, J.: Rich feature hierarchies for accurate object detection and semantic segmentation. In: 2014 IEEE Conference on Computer Vision and Pattern Recognition. IEEE (2014)","DOI":"10.1109\/CVPR.2014.81"},{"key":"37_CR16","doi-asserted-by":"crossref","unstructured":"Girshick, R.: Fast R-CNN. In: 2015 IEEE International Conference on Computer Vision (ICCV). IEEE (2015)","DOI":"10.1109\/ICCV.2015.169"},{"key":"37_CR17","doi-asserted-by":"crossref","unstructured":"Lin, T.-Y., Dollar, P., Girshick, R., He, K., Hariharan, B., Belongie, S.: Feature pyramid networks for object detection. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE (2017)","DOI":"10.1109\/CVPR.2017.106"},{"key":"37_CR18","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"402","DOI":"10.1007\/978-3-030-32226-7_45","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2019","author":"M Zlocha","year":"2019","unstructured":"Zlocha, M., Dou, Q., Glocker, B.: Improving RetinaNet for CT lesion detection with dense masks from weak RECIST labels. In: Shen, D., et al. (eds.) MICCAI 2019. LNCS, vol. 11769, pp. 402\u2013410. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-32226-7_45"},{"key":"37_CR19","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2022.103589","volume":"75","author":"J Chen","year":"2022","unstructured":"Chen, J., et al.: Detection of cervical lesions in colposcopic images based on the RetinaNet method. Biomed. Sig. Process. Control 75, 103589 (2022). https:\/\/doi.org\/10.1016\/j.bspc.2022.103589","journal-title":"Biomed. Sig. Process. Control"},{"key":"37_CR20","doi-asserted-by":"publisher","first-page":"80","DOI":"10.1148\/radiol.210817","volume":"303","author":"NC Swinburne","year":"2022","unstructured":"Swinburne, N.C., et al.: for the MSK mind consortium: semisupervised training of a brain MRI tumor detection model using mined annotations. Radiology 303, 80\u201389 (2022). https:\/\/doi.org\/10.1148\/radiol.210817","journal-title":"Radiology"},{"key":"37_CR21","doi-asserted-by":"publisher","unstructured":"Adachi, M., et al.: Detection and diagnosis of breast cancer using artificial intelligence based assessment of maximum intensity projection dynamic contrast-enhanced magnetic resonance images. Diagnostics (Basel) 10, 330 (2020). https:\/\/doi.org\/10.3390\/diagnostics10050330","DOI":"10.3390\/diagnostics10050330"},{"key":"37_CR22","doi-asserted-by":"publisher","first-page":"592","DOI":"10.4103\/0973-1482.126453","volume":"9","author":"E Kozegar","year":"2013","unstructured":"Kozegar, E., Soryani, M., Minaei, B., Domingues, I.: Assessment of a novel mass detection algorithm in mammograms. J. Cancer Res. Ther. 9, 592\u2013600 (2013). https:\/\/doi.org\/10.4103\/0973-1482.126453","journal-title":"J. Cancer Res. Ther."},{"key":"37_CR23","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"321","DOI":"10.1007\/978-3-319-67558-9_37","volume-title":"Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support","author":"A Akselrod-Ballin","year":"2017","unstructured":"Akselrod-Ballin, A., et al.: Deep learning for automatic detection of abnormal findings in breast mammography. In: Cardoso, M.J., et al. (eds.) DLMIA\/ML-CDS -2017. LNCS, vol. 10553, pp. 321\u2013329. Springer, Cham (2017). https:\/\/doi.org\/10.1007\/978-3-319-67558-9_37"},{"key":"37_CR24","doi-asserted-by":"crossref","unstructured":"Shen, R., Yao, J., Yan, K., Tian, K., Jiang, C., Zhou, K.: Unsupervised domain adaptation with adversarial learning for mass detection in mammogram. Neurocomputing (2020)","DOI":"10.1016\/j.neucom.2020.01.099"},{"key":"37_CR25","doi-asserted-by":"publisher","first-page":"93","DOI":"10.1016\/j.cmpb.2016.10.026","volume":"138","author":"J Anitha","year":"2017","unstructured":"Anitha, J., Peter, J.D., Pandian, S.I.A.: A dual stage adaptive thresholding (DuSAT) for automatic mass detection in mammograms. Comput. Comput. Methods Programs Biomed. 138, 93\u2013104 (2017)","journal-title":"Comput. Comput. Methods Programs Biomed."},{"key":"37_CR26","doi-asserted-by":"publisher","first-page":"2843","DOI":"10.1088\/0031-9155\/45\/10\/308","volume":"45","author":"GM te Brake","year":"2000","unstructured":"te Brake, G.M., Karssemeijer, N., Hendriks, J.H.C.L.: An automatic method to discriminate malignant masses from normal tissue in digital mammograms1. Phys. Med. Biol. 45, 2843\u20132857 (2000). https:\/\/doi.org\/10.1088\/0031-9155\/45\/10\/308","journal-title":"Phys. Med. Biol."},{"key":"37_CR27","doi-asserted-by":"publisher","first-page":"114","DOI":"10.1016\/j.media.2017.01.009","volume":"37","author":"N Dhungel","year":"2017","unstructured":"Dhungel, N., Carneiro, G., Bradley, A.P.: A deep learning approach for the analysis of masses in mammograms with minimal user intervention. Med. Med. Image Anal. 37, 114\u2013128 (2017)","journal-title":"Med. Med. Image Anal."},{"key":"37_CR28","doi-asserted-by":"crossref","unstructured":"Ribli, D., Horv\u00e1th, A., Unger, Z., Pollner, P., Csabai, I.: Detecting and classifying lesions in mammograms with Deep Learning. Sci. Sci. Rep. 8 (2018)","DOI":"10.1038\/s41598-018-22437-z"},{"key":"37_CR29","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0203355","volume":"13","author":"H Jung","year":"2018","unstructured":"Jung, H., et al.: Detection of masses in mammograms using a one-stage object detector based on a deep convolutional neural network. PLoS ONE 13, e0203355 (2018). https:\/\/doi.org\/10.1371\/journal.pone.0203355","journal-title":"PLoS ONE"},{"key":"37_CR30","doi-asserted-by":"publisher","unstructured":"Agarwal, R., Diaz, O., Llad\u00f3, X., Yap, M.H., Mart\u00ed, R.: Automatic mass detection in mammograms using deep convolutional neural networks. J. Med. Imaging (Bellingham). 6, 1 (2019). https:\/\/doi.org\/10.1117\/1.jmi.6.3.031409","DOI":"10.1117\/1.jmi.6.3.031409"}],"container-title":["Lecture Notes in Computer Science","Pattern Recognition, Computer Vision, and Image Processing. ICPR 2022 International Workshops and Challenges"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-37660-3_37","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,7,29]],"date-time":"2023-07-29T06:08:23Z","timestamp":1690610903000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-37660-3_37"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031376597","9783031376603"],"references-count":30,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-37660-3_37","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":"30 July 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICPR","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Pattern Recognition","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Montr\u00e9al, QC","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":"2022","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"21 August 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"25 August 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"icpr2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/iapr.org\/icpr2022","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}