{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,7]],"date-time":"2026-02-07T19:50:19Z","timestamp":1770493819777,"version":"3.49.0"},"publisher-location":"Cham","reference-count":33,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783030804312","type":"print"},{"value":"9783030804329","type":"electronic"}],"license":[{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"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":[[2021]]},"DOI":"10.1007\/978-3-030-80432-9_33","type":"book-chapter","created":{"date-parts":[[2021,7,5]],"date-time":"2021-07-05T23:08:25Z","timestamp":1625526505000},"page":"438-453","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Improving Generalization of ENAS-Based CNN Models for Breast Lesion Classification from Ultrasound Images"],"prefix":"10.1007","author":[{"given":"Mohammed","family":"Ahmed","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Alaa","family":"AlZoubi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hongbo","family":"Du","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2021,7,6]]},"reference":[{"key":"33_CR1","unstructured":"The International Agency for Research on Cancer (IARC) report. World Cancer Day 2021: Spotlight on IARC research related to breast cancer. International Agency for Research on Cancer. https:\/\/www.iarc.who.int\/featured-news\/world-cancer-day-2021\/. Accessed 12 May 2021"},{"key":"33_CR2","doi-asserted-by":"publisher","first-page":"123","DOI":"10.1148\/radiology.196.1.7784555","volume":"196","author":"TA Stavros","year":"1995","unstructured":"Stavros, T.A., Thickman, D., Rapp, L., Dennis, M.A., Parker, S.H., Sisney, G.: Solid breast nodules\u202f: use of sonography to distinguish lesions. Radiology 196, 123\u2013134 (1995)","journal-title":"Radiology"},{"key":"33_CR3","doi-asserted-by":"publisher","first-page":"101985","DOI":"10.1016\/j.media.2021.101985","volume":"69","author":"X Xie","year":"2021","unstructured":"Xie, X., Niu, J., Liu, X., Chen, Z., Tang, S., Yu, S.: A Survey on incorporating domain knowledge into deep learning for medical image analysis. Med. Image Anal. 69, 101985 (2021)","journal-title":"Med. Image Anal."},{"key":"33_CR4","doi-asserted-by":"publisher","first-page":"106300","DOI":"10.1016\/j.ultras.2020.106300","volume":"110","author":"YC Zhu","year":"2021","unstructured":"Zhu, Y.C., et al.: A generic deep learning framework to classify thyroid and breast lesions in ultrasound images. Ultrasonics 110, 106300 (2021)","journal-title":"Ultrasonics"},{"key":"33_CR5","volume-title":"Deep Learning","author":"Y Goodfellow","year":"2016","unstructured":"Goodfellow, Y., Bengio, Y., Courville, A.: Deep Learning. MIT Press, USA (2016)"},{"key":"33_CR6","unstructured":"Wistuba, M., Rawat, A., Pedapati, T.: A survey on neural architecture search, vol. 20, pp. 1\u201321 (2019). [Online]: http:\/\/arxiv.org\/abs\/1905.01392. Accessed 4 Mar 2021"},{"issue":"11","key":"33_CR7","doi-asserted-by":"publisher","first-page":"2278","DOI":"10.1109\/5.726791","volume":"86","author":"Y LeCun","year":"1998","unstructured":"LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proc. IEEE 86(11), 2278\u20132324 (1998)","journal-title":"Proc. IEEE"},{"key":"33_CR8","first-page":"1097","volume":"25","author":"A Krizhevsky","year":"2012","unstructured":"Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. Adv. Neural. Inf. Process. Syst. 25, 1097\u20131105 (2012)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"33_CR9","doi-asserted-by":"publisher","first-page":"60","DOI":"10.1016\/j.media.2017.07.005","volume":"42","author":"G Litjens","year":"2017","unstructured":"Litjens, G., et al.: A survey on deep learning in medical image analysis. Med. Image Anal. 42, 60\u201388 (2017)","journal-title":"Med. Image Anal."},{"issue":"5","key":"33_CR10","doi-asserted-by":"publisher","first-page":"1299","DOI":"10.1109\/TMI.2016.2535302","volume":"35","author":"N Tajbakhsh","year":"2016","unstructured":"Tajbakhsh, N., et al.: Convolutional neural networks for medical image analysis: full training or fine tuning? IEEE Trans. Med. Imaging 35(5), 1299\u20131312 (2016)","journal-title":"IEEE Trans. Med. Imaging"},{"issue":"19","key":"33_CR11","doi-asserted-by":"publisher","first-page":"7714","DOI":"10.1088\/1361-6560\/aa82ec","volume":"62","author":"S Han","year":"2017","unstructured":"Han, S., et al.: A deep learning framework for supporting the classification of breast lesions in ultrasound images. Phys. Med. Biol. 62(19), 7714\u20137728 (2017)","journal-title":"Phys. Med. Biol."},{"key":"33_CR12","first-page":"1","volume":"2018","author":"T Xiao","year":"2018","unstructured":"Xiao, T., Liu, L., Li, K., Qin, W., Yu, S., Li, Z.: Comparison of transferred deep neural networks in ultrasonic breast masses discrimination. Biomed. Res. Int. 2018, 1\u20139 (2018)","journal-title":"Biomed. Res. Int."},{"key":"33_CR13","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s00521-020-05394-5","volume":"5","author":"M Masud","year":"2020","unstructured":"Masud, M., Eldin Rashed, A.E., Hossain, M.S.: Convolutional neural network-based models for diagnosis of breast cancer. Neural Comput. Appl. 5, 1\u201312 (2020). https:\/\/doi.org\/10.1007\/s00521-020-05394-5","journal-title":"Neural Comput. Appl."},{"key":"33_CR14","doi-asserted-by":"crossref","unstructured":"Hijab, A., Rushdi, M.A., Gomaa, M.M.: Breast cancer classification in ultrasound images using transfer learning. In: Proceedings of Fifth International Conference on Advances in Biomedical Engineering (2019)","DOI":"10.1109\/ICABME47164.2019.8940291"},{"issue":"5","key":"33_CR15","doi-asserted-by":"publisher","first-page":"1218","DOI":"10.1007\/s10278-020-00357-7","volume":"33","author":"H Zhang","year":"2020","unstructured":"Zhang, H., Han, L., Chen, K., Peng, Y., Lin, J.: Diagnostic efficiency of the breast ultrasound computer-aided prediction model based on convolutional neural network in breast cancer. J. Digit. Imaging 33(5), 1218\u20131223 (2020). https:\/\/doi.org\/10.1007\/s10278-020-00357-7","journal-title":"J. Digit. Imaging"},{"key":"33_CR16","unstructured":"Zoph, B., Le, Q.V.: Neural architecture search with reinforcement learning, pp.1\u201316 (2017). [Online]: http:\/\/arxiv.org\/abs\/1611.01578. Accessed 12 May 2021"},{"key":"33_CR17","doi-asserted-by":"crossref","unstructured":"Zoph, B., Le, Q.: Learning transferable architectures for scalable image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 8697\u20138710 (2018)","DOI":"10.1109\/CVPR.2018.00907"},{"key":"33_CR18","unstructured":"Pham, H., Guan, M.Y., Zoph, B., Le, Q.V., Dean, J.: Efficient neural architecture search via parameters sharing. In: Proceedings of International Conference on M.L, pp. 4095\u20134104 (2018)"},{"key":"33_CR19","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"19","DOI":"10.1007\/978-3-030-01246-5_2","volume-title":"Computer Vision \u2013 ECCV 2018","author":"C Liu","year":"2018","unstructured":"Liu, C., et al.: Progressive neural architecture search. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11205, pp. 19\u201335. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-01246-5_2"},{"key":"33_CR20","unstructured":"Gessert, N., Schlaefer, A.: Efficient neural architecture search on low-dimensional data for OCT image segmentation. arXiv preprint arXiv:1905.02590 (2019)"},{"key":"33_CR21","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"828","DOI":"10.1007\/978-3-030-32226-7_92","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2019","author":"N Dong","year":"2019","unstructured":"Dong, N., Xu, M., Liang, X., Jiang, Y., Dai, W., Xing, E.: Neural architecture search for adversarial medical image segmentation. In: Shen, D., et al. (eds.) MICCAI 2019. LNCS, vol. 11769, pp. 828\u2013836. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-32226-7_92"},{"key":"33_CR22","unstructured":"Mohammed, A., Du, H., AlZoubi, A.: An ENAS based approach for constructing deep learning models for breast cancer recognition from ultrasound images. In: Proceedings of MIDL Conference. arXiv preprint arXiv:2005.13695 (2020)"},{"key":"33_CR23","unstructured":"Recht, B., Roelofs, R., Schmidt, L., Shankar, V.: Do ImageNet classifiers generalize to ImageNet? In: Proceedings of International Conference on Machine Learning, pp. 5389\u20135400 (2019)"},{"key":"33_CR24","unstructured":"Rice, L., Wong, E., Kolter, Z.: Overfitting an adversarially robust deep learning. In: Proceedings of International Conference on Machine Learning, pp. 8093\u20138104. PMLR (2020)"},{"issue":"10","key":"33_CR25","doi-asserted-by":"publisher","first-page":"105002","DOI":"10.1088\/1361-6560\/ab82e8","volume":"65","author":"RK Samala","year":"2020","unstructured":"Samala, R.K., Chan, H.P., Hadjiiski, L.M., Helvie, M.A., Richter, C.D.: Generalization error analysis for deep convolutional neural network with transfer learning in breast cancer diagnosis. Phys. Med. Biol. 65(10), 105002 (2020)","journal-title":"Phys. Med. Biol."},{"key":"33_CR26","doi-asserted-by":"crossref","unstructured":"Zeimarani, B., Costa, M.G.F., Nurani, N.Z., Bianco, S.R., De Albuquerque Pereira, W.C., Filho, C.F.F.C.: Breast lesion classification in ultrasound images using deep convolutional neural network. IEEE Access. 8, 133349\u2013133359 (2020)","DOI":"10.1109\/ACCESS.2020.3010863"},{"key":"33_CR27","unstructured":"Jiang, Y., Zhao, C., Dou, Z., Pang, L.: Neural architecture refinement: a practical way for avoiding overfitting in NAS. arXiv preprint arXiv:1905.02341 (2019)"},{"key":"33_CR28","doi-asserted-by":"publisher","first-page":"104863","DOI":"10.1016\/j.dib.2019.104863","volume":"28","author":"W Al-Dhabyani","year":"2020","unstructured":"Al-Dhabyani, W., Gomaa, M., Khaled, H., Fahmy, A.: Dataset of breast ultrasound images. Data Brief 28, 104863 (2020)","journal-title":"Data Brief"},{"issue":"1","key":"33_CR29","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s40537-019-0192-5","volume":"6","author":"JM Johnson","year":"2019","unstructured":"Johnson, J.M., Khoshgoftaar, T.M.: Survey on deep learning with class imbalance. J. Big Data 6(1), 1\u201354 (2019). https:\/\/doi.org\/10.1186\/s40537-019-0192-5","journal-title":"J. Big Data"},{"key":"33_CR30","doi-asserted-by":"publisher","first-page":"249","DOI":"10.1016\/j.neunet.2018.07.011","volume":"106","author":"M Buda","year":"2018","unstructured":"Buda, M., Maki, A., Mazurowski, M.A.: A systematic study of the class imbalance problem in convolutional neural networks. Neural Netw. 106, 249\u2013259 (2018)","journal-title":"Neural Netw."},{"key":"33_CR31","unstructured":"Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014)"},{"key":"33_CR32","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, vol. 2016, pp. 770\u2013778 (2015)","DOI":"10.1109\/CVPR.2016.90"},{"key":"33_CR33","doi-asserted-by":"crossref","unstructured":"Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z.: Rethinking the inception architecture for computer vision. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, vol. 2016, pp. 2818\u20132826 (2016)","DOI":"10.1109\/CVPR.2016.308"}],"container-title":["Lecture Notes in Computer Science","Medical Image Understanding and Analysis"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-80432-9_33","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,12]],"date-time":"2024-03-12T17:34:43Z","timestamp":1710264883000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-80432-9_33"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"ISBN":["9783030804312","9783030804329"],"references-count":33,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-80432-9_33","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021]]},"assertion":[{"value":"6 July 2021","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"MIUA","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Annual Conference on Medical Image Understanding and Analysis","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Oxford","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":"12 July 2021","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"14 July 2021","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"25","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"miua2021","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/miua2021.com\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"CMT","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"77","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"32","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"8","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"42% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3,8","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3,3","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}