{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,26]],"date-time":"2026-02-26T05:15:45Z","timestamp":1772082945500,"version":"3.50.1"},"reference-count":38,"publisher":"Springer Science and Business Media LLC","issue":"6","license":[{"start":{"date-parts":[[2023,11,27]],"date-time":"2023-11-27T00:00:00Z","timestamp":1701043200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,11,27]],"date-time":"2023-11-27T00:00:00Z","timestamp":1701043200000},"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":["Neural Comput &amp; Applic"],"published-print":{"date-parts":[[2024,2]]},"DOI":"10.1007\/s00521-023-09237-x","type":"journal-article","created":{"date-parts":[[2023,11,27]],"date-time":"2023-11-27T07:02:51Z","timestamp":1701068571000},"page":"3017-3035","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":16,"title":["Automatic detection of breast cancer for mastectomy based on MRI images using Mask R-CNN and Detectron2 models"],"prefix":"10.1007","volume":"36","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2931-0278","authenticated-orcid":false,"given":"Chiman Haydar","family":"Salh","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3037-4903","authenticated-orcid":false,"given":"Abbas M.","family":"Ali","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,11,27]]},"reference":[{"key":"9237_CR1","doi-asserted-by":"publisher","unstructured":"Benjelloun M, El Adoui M, Larhmam MA, Mahmoudi SA (2018) Automated breast tumor segmentation in DCE-MRI using deep learning. In: 2018 4th Int Conf Cloud Comput Technol Appl Cloudtech 2018, pp 1\u20136. https:\/\/doi.org\/10.1109\/CloudTech.2018.8713352.","DOI":"10.1109\/CloudTech.2018.8713352"},{"key":"9237_CR2","doi-asserted-by":"publisher","DOI":"10.3390\/s17071572","author":"L Wang","year":"2017","unstructured":"Wang L (2017) Early diagnosis of breast cancer. Sensors (Switzerland). https:\/\/doi.org\/10.3390\/s17071572","journal-title":"Sensors (Switzerland)"},{"issue":"07","key":"9237_CR3","doi-asserted-by":"publisher","first-page":"693","DOI":"10.1007\/978-981-16-1866-6_50","volume":"68","author":"A Sivasangari","year":"2022","unstructured":"Sivasangari A, Ajitha P, Bevishjenila, Vimali JS, Jose J, Gowri S (2022) Breast cancer detection using machine learning. Lect Notes Data Eng Commun Technol 68(07):693\u2013702. https:\/\/doi.org\/10.1007\/978-981-16-1866-6_50","journal-title":"Lect Notes Data Eng Commun Technol"},{"key":"9237_CR4","doi-asserted-by":"publisher","first-page":"197312","DOI":"10.1109\/ACCESS.2020.3034914","volume":"8","author":"CM Kim","year":"2020","unstructured":"Kim CM, Park RC, Hong EJ (2020) Breast mass classification using eLFA algorithm based on CRNN deep learning model. IEEE Access 8:197312\u2013197323. https:\/\/doi.org\/10.1109\/ACCESS.2020.3034914","journal-title":"IEEE Access"},{"issue":"6","key":"9237_CR5","doi-asserted-by":"publisher","first-page":"1","DOI":"10.3390\/cancers12061511","volume":"12","author":"EF Jones","year":"2020","unstructured":"Jones EF et al (2020) Current landscape of breast cancer imaging and potential quantitative imaging markers of response in er-positive breast cancers treated with neoadjuvant therapy. Cancers (Basel) 12(6):1\u201324. https:\/\/doi.org\/10.3390\/cancers12061511","journal-title":"Cancers (Basel)"},{"key":"9237_CR6","doi-asserted-by":"publisher","first-page":"103113","DOI":"10.1016\/j.bspc.2021.103113","volume":"71","author":"C Militello","year":"2022","unstructured":"Militello C et al (2022) Semi-automated and interactive segmentation of contrast-enhancing masses on breast DCE-MRI using spatial fuzzy clustering. Biomed Signal Process Control 71:103113. https:\/\/doi.org\/10.1016\/j.bspc.2021.103113","journal-title":"Biomed Signal Process Control"},{"issue":"8","key":"9237_CR7","doi-asserted-by":"publisher","first-page":"2751","DOI":"10.1007\/s00371-021-02153-y","volume":"38","author":"N Ahmad","year":"2022","unstructured":"Ahmad N, Asghar S, Gillani SA (2022) Transfer learning-assisted multi-resolution breast cancer histopathological images classification. Vis Comput 38(8):2751\u20132770. https:\/\/doi.org\/10.1007\/s00371-021-02153-y","journal-title":"Vis Comput"},{"key":"9237_CR8","doi-asserted-by":"publisher","unstructured":"Maicas G, Carneiro G, Bradley AP (2017) Globally optimal breast mass segmentation from DCE-MRI using deep semantic segmentation as shape prior. In: Proc\u2014Int Symp Biomed Imaging, pp 305\u2013309. https:\/\/doi.org\/10.1109\/ISBI.2017.7950525.","DOI":"10.1109\/ISBI.2017.7950525"},{"key":"9237_CR9","doi-asserted-by":"publisher","unstructured":"Amkrane Y, El Adoui M, Benjelloun M (2020) Towards breast cancer response prediction using artificial intelligence and radiomics. In: Proc 2020 5th Int Conf Cloud Comput Artif Intell Technol Appl CloudTech 2020. https:\/\/doi.org\/10.1109\/CloudTech49835.2020.9365890.","DOI":"10.1109\/CloudTech49835.2020.9365890"},{"key":"9237_CR10","doi-asserted-by":"publisher","first-page":"S135","DOI":"10.1016\/j.acra.2020.12.001","volume":"29","author":"Y Zhang","year":"2022","unstructured":"Zhang Y et al (2022) Automatic detection and segmentation of breast cancer on MRI using mask R-CNN trained on non\u2013fat-sat images and tested on fat-sat images. Acad Radiol 29:S135\u2013S144. https:\/\/doi.org\/10.1016\/j.acra.2020.12.001","journal-title":"Acad Radiol"},{"key":"9237_CR11","doi-asserted-by":"publisher","unstructured":"Maicas G, Carneiro G, Bradley AP, Nascimento JC, Reid I (2017) Deep reinforcement learning for active breast lesion detection from DCE-MRI. Lect Notes Comput Sci (including Subser Lect Notes Artif Intell Lect Notes Bioinformatics), vol 10435 LNCS, pp 665\u2013673. https:\/\/doi.org\/10.1007\/978-3-319-66179-7_76.","DOI":"10.1007\/978-3-319-66179-7_76"},{"key":"9237_CR12","doi-asserted-by":"publisher","DOI":"10.1155\/2022\/4477099","author":"G Ye","year":"2022","unstructured":"Ye G, He S, Pan R, Zhu L, Zhou D, Lu RL (2022) Research on DCE-MRI images based on deep transfer learning in breast cancer adjuvant curative effect prediction. J Healthc Eng. https:\/\/doi.org\/10.1155\/2022\/4477099","journal-title":"J Healthc Eng"},{"issue":"1","key":"9237_CR13","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s13058-020-01291-w","volume":"22","author":"EJ Sutton","year":"2020","unstructured":"Sutton EJ et al (2020) A machine learning model that classifies breast cancer pathologic complete response on MRI post-neoadjuvant chemotherapy. Breast Cancer Res 22(1):1\u201311. https:\/\/doi.org\/10.1186\/s13058-020-01291-w","journal-title":"Breast Cancer Res"},{"issue":"10","key":"9237_CR14","doi-asserted-by":"publisher","first-page":"5897","DOI":"10.1002\/mp.15156","volume":"48","author":"F Ayatollahi","year":"2021","unstructured":"Ayatollahi F, Shokouhi SB, Mann RM, Teuwen J (2021) Automatic breast lesion detection in ultrafast DCE-MRI using deep learning. Med Phys 48(10):5897\u20135907. https:\/\/doi.org\/10.1002\/mp.15156","journal-title":"Med Phys"},{"issue":"July","key":"9237_CR15","doi-asserted-by":"publisher","first-page":"1","DOI":"10.3389\/fonc.2022.943415","volume":"12","author":"Y Chen","year":"2022","unstructured":"Chen Y et al (2022) A deep learning model based on dynamic contrast-enhanced magnetic resonance imaging enables accurate prediction of benign and malignant breast lessons. Front Oncol 12(July):1\u201310. https:\/\/doi.org\/10.3389\/fonc.2022.943415","journal-title":"Front Oncol"},{"key":"9237_CR16","unstructured":"Hu Q, Whitney HM, Giger ML (2019) Transfer learning in 4D for breast cancer diagnosis using dynamic contrast-enhanced magnetic resonance imaging, no. NeurIPS, [Online]. http:\/\/arxiv.org\/abs\/1911.03022"},{"issue":"8","key":"9237_CR17","doi-asserted-by":"publisher","first-page":"70","DOI":"10.46338\/ijetae0822_09","volume":"12","author":"AE Minarno","year":"2022","unstructured":"Minarno AE, Wandani LR, Azhar Y (2022) Classification of breast cancer based on histopathological image using efficientNet-B0 on convolutional neural network. Int J Emerg Technol Adv Eng 12(8):70\u201377. https:\/\/doi.org\/10.46338\/ijetae0822_09","journal-title":"Int J Emerg Technol Adv Eng"},{"key":"9237_CR18","doi-asserted-by":"publisher","DOI":"10.3390\/jpm12091444","author":"P Amerikanos","year":"2022","unstructured":"Amerikanos P, Maglogiannis I (2022) Image analysis in digital pathology utilizing machine learning and deep neural networks. J Pers Med. https:\/\/doi.org\/10.3390\/jpm12091444","journal-title":"J Pers Med"},{"issue":"12","key":"9237_CR19","doi-asserted-by":"publisher","first-page":"1","DOI":"10.3390\/mi13122197","volume":"13","author":"Y Zhao","year":"2022","unstructured":"Zhao Y, Zhang J, Hu D, Qu H, Tian Y, Cui X (2022) Application of deep learning in histopathology images of breast cancer: a review. Micromachines 13(12):1\u201330. https:\/\/doi.org\/10.3390\/mi13122197","journal-title":"Micromachines"},{"issue":"3","key":"9237_CR20","doi-asserted-by":"publisher","first-page":"507","DOI":"10.21037\/tlcr.2020.04.11","volume":"9","author":"MS Kim","year":"2020","unstructured":"Kim MS et al (2020) Artificial intelligence and lung cancer treatment decision: agreement with recommendation of multidisciplinary tumor board. Transl Lung Cancer Res 9(3):507\u2013514. https:\/\/doi.org\/10.21037\/tlcr.2020.04.11","journal-title":"Transl Lung Cancer Res"},{"issue":"1","key":"9237_CR21","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s12885-022-09369-8","volume":"22","author":"GTF Brown","year":"2022","unstructured":"Brown GTF, Bekker HL, Young AL (2022) Quality and efficacy of Multidisciplinary Team (MDT) quality assessment tools and discussion checklists: a systematic review. BMC Cancer 22(1):1\u201310. https:\/\/doi.org\/10.1186\/s12885-022-09369-8","journal-title":"BMC Cancer"},{"issue":"4","key":"9237_CR22","doi-asserted-by":"publisher","first-page":"1","DOI":"10.3390\/cancers13040661","volume":"13","author":"J Wang","year":"2021","unstructured":"Wang J, Liu Q, Xie H, Yang Z, Zhou H (2021) Boosted efficientnet: detection of lymph node metastases in breast cancer using convolutional neural networks. Cancers (Basel) 13(4):1\u201314. https:\/\/doi.org\/10.3390\/cancers13040661","journal-title":"Cancers (Basel)"},{"key":"9237_CR23","doi-asserted-by":"publisher","DOI":"10.3390\/axioms11010034","author":"DR Nayak","year":"2022","unstructured":"Nayak DR, Padhy N, Mallick PK, Zymbler M, Kumar S (2022) Brain tumor classification using dense efficient-net. Axioms. https:\/\/doi.org\/10.3390\/axioms11010034","journal-title":"Axioms"},{"key":"9237_CR24","unstructured":"Tan M, Le QV (2019) EfficientNet: rethinking model scaling for convolutional neural networks. In: 36th Int Conf Mach Learn ICML 2019, vol 2019-June, pp 10691\u201310700"},{"key":"9237_CR25","doi-asserted-by":"publisher","unstructured":"Min H et al (2020) Fully automatic computer-aided mass detection and segmentation via pseudo-color mammograms and mask R-CNN. In: Proc\u2014Int Symp Biomed Imaging, vol 2020\u2013April, pp 1111\u20131115. https:\/\/doi.org\/10.1109\/ISBI45749.2020.9098732.","DOI":"10.1109\/ISBI45749.2020.9098732"},{"issue":"2","key":"9237_CR26","doi-asserted-by":"publisher","first-page":"318","DOI":"10.1109\/TPAMI.2018.2858826","volume":"42","author":"TY Lin","year":"2020","unstructured":"Lin TY, Goyal P, Girshick R, He K, Dollar P (2020) Focal loss for dense object detection. IEEE Trans Pattern Anal Mach Intell 42(2):318\u2013327. https:\/\/doi.org\/10.1109\/TPAMI.2018.2858826","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"9237_CR27","doi-asserted-by":"publisher","first-page":"44400","DOI":"10.1109\/ACCESS.2020.2976432","volume":"8","author":"L Cai","year":"2020","unstructured":"Cai L, Long T, Dai Y, Huang Y (2020) Mask R-CNN-based detection and segmentation for pulmonary nodule 3D visualization diagnosis. IEEE Access 8:44400\u201344409. https:\/\/doi.org\/10.1109\/ACCESS.2020.2976432","journal-title":"IEEE Access"},{"issue":"19","key":"9237_CR28","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1097\/MD.0000000000015200","volume":"98","author":"JY Chiao","year":"2019","unstructured":"Chiao JY, Chen KY, Liao KY-K, Hsieh PH, Zhang G, Huang TC (2019) Detection and classification the breast tumors using mask R-CNN on sonograms. Med (United States) 98(19):1\u20135. https:\/\/doi.org\/10.1097\/MD.0000000000015200","journal-title":"Med (United States)"},{"key":"9237_CR29","unstructured":"Ahmad S, Mouiad A (2021) Comparative study: 2D object detection & inferencing using Detectron2 2D object detection & inferencing using Detectron2: comparative study Abstract, August, pp 0\u20135"},{"key":"9237_CR30","doi-asserted-by":"publisher","unstructured":"Pham V, Pham C, Dang T (2020) Road damage detection and classification with Detectron2 and faster R-CNN. In: Proc\u20142020 IEEE Int Conf Big Data, Big Data 2020, pp 5592\u20135601. https:\/\/doi.org\/10.1109\/BigData50022.2020.9378027","DOI":"10.1109\/BigData50022.2020.9378027"},{"issue":"July","key":"9237_CR31","doi-asserted-by":"publisher","first-page":"112091","DOI":"10.1016\/j.matchar.2022.112091","volume":"191","author":"M Ackermann","year":"2022","unstructured":"Ackermann M, Iren D, Wesselmecking S, Shetty D, Krupp U (2022) Automated segmentation of martensite-austenite islands in bainitic steel. Mater Charact 191(July):112091. https:\/\/doi.org\/10.1016\/j.matchar.2022.112091","journal-title":"Mater Charact"},{"issue":"2","key":"9237_CR32","doi-asserted-by":"publisher","first-page":"673","DOI":"10.1109\/TMI.2020.3035292","volume":"40","author":"N Cai","year":"2021","unstructured":"Cai N, Chen H, Li Y, Peng Y, Li J (2021) Adaptive weighting landmark-based group-wise registration on lung DCE-MRI images. IEEE Trans Med Imaging 40(2):673\u2013687. https:\/\/doi.org\/10.1109\/TMI.2020.3035292","journal-title":"IEEE Trans Med Imaging"},{"issue":"3","key":"9237_CR33","doi-asserted-by":"publisher","first-page":"798","DOI":"10.1002\/jmri.26981","volume":"51","author":"J Zhou","year":"2020","unstructured":"Zhou J et al (2020) Diagnosis of benign and malignant breast lesions on DCE-MRI by using radiomics and deep learning with consideration of peritumor tissue. J Magn Reson Imaging 51(3):798\u2013809. https:\/\/doi.org\/10.1002\/jmri.26981","journal-title":"J Magn Reson Imaging"},{"issue":"July","key":"9237_CR34","doi-asserted-by":"publisher","first-page":"1","DOI":"10.3389\/fbioe.2021.662749","volume":"9","author":"Y Huang","year":"2021","unstructured":"Huang Y et al (2021) Prediction of tumor shrinkage pattern to neoadjuvant chemotherapy using a multiparametric MRI-based machine learning model in patients with breast cancer. Front Bioeng Biotechnol 9(July):1\u201315. https:\/\/doi.org\/10.3389\/fbioe.2021.662749","journal-title":"Front Bioeng Biotechnol"},{"key":"9237_CR35","doi-asserted-by":"publisher","DOI":"10.1148\/ryai.2021200159","author":"Q Hu","year":"2021","unstructured":"Hu Q, Whitney HM, Li H, Ji Y, Liu P, Giger ML (2021) Improved classification of benign and malignant breast lesions using deep feature maximum intensity projection MRI in breast cancer diagnosis using dynamic contrast-enhanced MRI. Radiol Artif Intell. https:\/\/doi.org\/10.1148\/ryai.2021200159","journal-title":"Radiol Artif Intell"},{"issue":"1","key":"9237_CR36","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41598-020-77875-5","volume":"10","author":"JH Choi","year":"2020","unstructured":"Choi JH et al (2020) Early prediction of neoadjuvant chemotherapy response for advanced breast cancer using PET\/MRI image deep learning. Sci Rep 10(1):1\u201311. https:\/\/doi.org\/10.1038\/s41598-020-77875-5","journal-title":"Sci Rep"},{"key":"9237_CR37","doi-asserted-by":"publisher","DOI":"10.3390\/app10176109","author":"L Conte","year":"2020","unstructured":"Conte L, Tafuri B, Portaluri M, Galiano A, Maggiulli E, De Nunzio G (2020) Breast cancer mass detection in dce-mri using deep-learning features followed by discrimination of infiltrative vs. in situ carcinoma through a machine-learning approach. Appl Sci. https:\/\/doi.org\/10.3390\/app10176109","journal-title":"Appl Sci"},{"key":"9237_CR38","doi-asserted-by":"publisher","DOI":"10.1155\/2022\/6126061","author":"L Li","year":"2022","unstructured":"Li L, Tian H, Zhang B, Wang W, Li B (2022) Prediction for distant metastasis of breast cancer using dynamic contrast-enhanced magnetic resonance imaging images under deep learning. Comput Intell Neurosci. https:\/\/doi.org\/10.1155\/2022\/6126061","journal-title":"Comput Intell Neurosci"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-023-09237-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00521-023-09237-x\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-023-09237-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,1,23]],"date-time":"2024-01-23T07:10:07Z","timestamp":1705993807000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00521-023-09237-x"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,11,27]]},"references-count":38,"journal-issue":{"issue":"6","published-print":{"date-parts":[[2024,2]]}},"alternative-id":["9237"],"URL":"https:\/\/doi.org\/10.1007\/s00521-023-09237-x","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"value":"0941-0643","type":"print"},{"value":"1433-3058","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,11,27]]},"assertion":[{"value":"1 March 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"3 November 2023","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"27 November 2023","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"We wish to confirm that there are no known conflicts of interest associated with this publication, and there has been no significant financial support for this work that could have influenced its outcome.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}