{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,26]],"date-time":"2025-03-26T19:06:03Z","timestamp":1743015963684,"version":"3.40.3"},"publisher-location":"Cham","reference-count":30,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031781032"},{"type":"electronic","value":"9783031781049"}],"license":[{"start":{"date-parts":[[2024,12,2]],"date-time":"2024-12-02T00:00:00Z","timestamp":1733097600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,12,2]],"date-time":"2024-12-02T00:00:00Z","timestamp":1733097600000},"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":[[2025]]},"DOI":"10.1007\/978-3-031-78104-9_2","type":"book-chapter","created":{"date-parts":[[2024,12,1]],"date-time":"2024-12-01T21:43:42Z","timestamp":1733089422000},"page":"17-30","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Deep BI-RADS Network for\u00a0Improved Cancer Detection from\u00a0Mammograms"],"prefix":"10.1007","author":[{"given":"Gil","family":"Ben-Artzi","sequence":"first","affiliation":[]},{"given":"Feras","family":"Daragma","sequence":"additional","affiliation":[]},{"given":"Shahar","family":"Mahpod","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,12,2]]},"reference":[{"key":"2_CR1","volume-title":"ACR BI-RADS$$\\text{\\textregistered} $$ Atlas - Mammography","author":"American College of Radiology","year":"2013","unstructured":"American College of Radiology: ACR BI-RADS$$\\text{\\textregistered} $$ Atlas - Mammography. American College of Radiology, Reston, VA (2013)"},{"key":"2_CR2","unstructured":"American College of Radiology: ACR TI-RADS$$\\text{\\textregistered} $$ Atlas. American College of Radiology, Reston, VA (2073)"},{"key":"2_CR3","unstructured":"Ba, L.J., Kiros, J.R., Hinton, G.E.: Layer normalization. CoRR abs\/1607.06450 (2016). http:\/\/arxiv.org\/abs\/1607.06450"},{"key":"2_CR4","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1007\/978-3-031-16437-8_1","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2022: 25th International Conference, Singapore, September 18\u201322, 2022, Proceedings, Part III","author":"Y Chen","year":"2022","unstructured":"Chen, Y., et al.: Multi-view local co-occurrence and\u00a0global consistency learning improve mammogram classification generalisation. In: Wang, L., Dou, Q., Fletcher, P.T., Speidel, S., Li, S. (eds.) Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2022: 25th International Conference, Singapore, September 18\u201322, 2022, Proceedings, Part III, pp. 3\u201313. Springer Nature Switzerland, Cham (2022). https:\/\/doi.org\/10.1007\/978-3-031-16437-8_1"},{"issue":"1","key":"2_CR5","doi-asserted-by":"publisher","first-page":"19","DOI":"10.1007\/s10479-005-5724-z","volume":"134","author":"PT De Boer","year":"2005","unstructured":"De Boer, P.T., Kroese, D.P., Mannor, S., Rubinstein, R.Y.: A tutorial on the cross-entropy method. Ann. Oper. Res. 134(1), 19\u201367 (2005)","journal-title":"Ann. Oper. Res."},{"key":"2_CR6","doi-asserted-by":"crossref","unstructured":"Falconi, L.G., Maria\u00a0Perez, W.G.A., Conci, A.: Transfer learning and fine tuning in breast mammogram abnormalities classification on CBIS-DDSM database 5(2), 154\u2013165 (2020)","DOI":"10.25046\/aj050220"},{"key":"2_CR7","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, pp. 770\u2013778 (2016)","DOI":"10.1109\/CVPR.2016.90"},{"key":"2_CR8","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Identity mappings in deep residual networks (2016). http:\/\/arxiv.org\/abs\/1603.05027, ECCV 2016 camera-ready","DOI":"10.1007\/978-3-319-46493-0_38"},{"key":"2_CR9","doi-asserted-by":"publisher","unstructured":"Heath, M., et al.: Current status of the digital database for screening mammography. In: Digital Mammography, pp. 457\u2013460. Springer, Dordrecht (1998). https:\/\/doi.org\/10.1007\/978-94-011-5318-8_75","DOI":"10.1007\/978-94-011-5318-8_75"},{"key":"2_CR10","unstructured":"Jaegle, A., Gimeno, F., Brock, A., Vinyals, O., Zisserman, A., Carreira, J.: Perceiver: general perception with iterative attention. In: International Conference on Machine Learning, pp. 4651\u20134664. PMLR (2021)"},{"key":"2_CR11","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"491","DOI":"10.1007\/978-3-030-58558-7_29","volume-title":"Computer Vision \u2013 ECCV 2020","author":"A Kolesnikov","year":"2020","unstructured":"Kolesnikov, A., Beyer, L., Zhai, X., Puigcerver, J., Yung, J., Gelly, S., Houlsby, N.: Big Transfer (BiT): General Visual Representation Learning. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12350, pp. 491\u2013507. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-58558-7_29"},{"issue":"1","key":"2_CR12","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/sdata.2017.177","volume":"4","author":"RS Lee","year":"2017","unstructured":"Lee, R.S., Gimenez, F., Hoogi, A., Miyake, K.K., Gorovoy, M., Rubin, D.L.: A curated mammography data set for use in computer-aided detection and diagnosis research. Sci. Data 4(1), 1\u20139 (2017)","journal-title":"Sci. Data"},{"key":"2_CR13","doi-asserted-by":"publisher","first-page":"5902","DOI":"10.1007\/s00330-020-07659-y","volume":"31","author":"H Liu","year":"2021","unstructured":"Liu, H., et al.: A deep learning model integrating mammography and clinical factors facilitates the malignancy prediction of BI-RADS 4 microcalcifications in breast cancer screening. Eur. Radiol. 31, 5902\u20135912 (2021)","journal-title":"Eur. Radiol."},{"key":"2_CR14","doi-asserted-by":"crossref","unstructured":"Liu, Y., Zhang, F., Zhang, Q., Wang, S., Wang, Y., Yu, Y.: Cross-view correspondence reasoning based on bipartite graph convolutional network for mammogram mass detection. In: 2020 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 3811\u20133821 (2020)","DOI":"10.1109\/CVPR42600.2020.00387"},{"key":"2_CR15","doi-asserted-by":"crossref","unstructured":"Mo, Y., et\u00a0al.: HoVer-Trans: anatomy-aware hover-transformer for ROI-free breast cancer diagnosis in ultrasound images. IEEE Trans. Med. Imaging (2023)","DOI":"10.1109\/TMI.2023.3236011"},{"key":"2_CR16","doi-asserted-by":"publisher","unstructured":"Nguyen, H.T.X., Tran, S.B., Nguyen, D.B., Pham, H.H., Nguyen, H.Q.: A novel multi-view deep learning approach for BI-RADS and density assessment of mammograms. In: 2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), pp. 2144\u20132148 (2022). https:\/\/doi.org\/10.1109\/EMBC48229.2022.9871564","DOI":"10.1109\/EMBC48229.2022.9871564"},{"key":"2_CR17","doi-asserted-by":"crossref","unstructured":"Nguyen, H.T., Tran, S.B., Nguyen, D.B., Pham, H.H., Nguyen, H.Q.: A novel multi-view deep learning approach for BI-RADS and density assessment of mammograms. In: 2022 44th Annual International Conference of the IEEE Engineering in Medicine and Biology Society(EMBC), pp. 2144\u20132148. IEEE (2022)","DOI":"10.1109\/EMBC48229.2022.9871564"},{"key":"2_CR18","unstructured":"Paszke, A., et al.: PyTorch: an imperative style, high-performance deep learning library. In: Wallach, H., Larochelle, H., Beygelzimer, A., d\u2019Alch\u00e9-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 32, pp. 8024\u20138035. Curran Associates, Inc. (2019)"},{"key":"2_CR19","unstructured":"Qiao, S., Wang, H., Liu, C., Shen, W., Yuille, A.L.: Weight standardization. CoRR abs\/1903.10520 (2019). http:\/\/dblp.uni-trier.de\/db\/journals\/corr\/corr1903.html#abs-1903-10520"},{"key":"2_CR20","doi-asserted-by":"crossref","unstructured":"Robbins, H., Monro, S.: A stochastic approximation method. Ann. Math. Stat., 400\u2013407 (1951)","DOI":"10.1214\/aoms\/1177729586"},{"key":"2_CR21","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2020.101908","volume":"68","author":"Y Shen","year":"2021","unstructured":"Shen, Y., Wu, N., Phang, J., Park, J.C., Liu, K., Tyagi, S., et al.: An interpretable classifier for high-resolution breast cancer screening images utilizing weakly supervised localization. Med. Image Anal. 68, 101908 (2021)","journal-title":"Med. Image Anal."},{"key":"2_CR22","first-page":"7537","volume":"33","author":"M Tancik","year":"2020","unstructured":"Tancik, M., et al.: Fourier features let networks learn high frequency functions in low dimensional domains. Adv. Neural. Inf. Process. Syst. 33, 7537\u20137547 (2020)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"issue":"3","key":"2_CR23","doi-asserted-by":"publisher","first-page":"1160","DOI":"10.3390\/s22031160","volume":"22","author":"KJ Tsai","year":"2022","unstructured":"Tsai, K.J., et al.: A high-performance deep neural network model for BI-RADS classification of screening mammography. Sensors 22(3), 1160 (2022)","journal-title":"Sensors"},{"key":"2_CR24","doi-asserted-by":"publisher","first-page":"104","DOI":"10.1007\/978-3-030-87199-4_10","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2021: 24th International Conference, Strasbourg, France, September 27\u2013October 1, 2021, Proceedings, Part III","author":"G van Tulder","year":"2021","unstructured":"van Tulder, G., Tong, Y., Marchiori, E.: Multi-view analysis of unregistered medical images using cross-view transformers. In: de Bruijne, M., et al. (eds.) Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2021: 24th International Conference, Strasbourg, France, September 27\u2013October 1, 2021, Proceedings, Part III, pp. 104\u2013113. Springer International Publishing, Cham (2021). https:\/\/doi.org\/10.1007\/978-3-030-87199-4_10"},{"key":"2_CR25","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"104","DOI":"10.1007\/978-3-030-87199-4_10","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2021","author":"G van Tulder","year":"2021","unstructured":"van Tulder, G., Tong, Y., Marchiori, E.: Multi-view analysis of unregistered medical images using cross-view transformers. In: de Bruijne, M., et al. (eds.) MICCAI 2021. LNCS, vol. 12903, pp. 104\u2013113. Springer, Cham (2021). https:\/\/doi.org\/10.1007\/978-3-030-87199-4_10"},{"key":"2_CR26","first-page":"5998","volume":"30","author":"A Vaswani","year":"2017","unstructured":"Vaswani, A., et al.: Attention is all you need. Adv. Neural. Inf. Process. Syst. 30, 5998\u20136008 (2017)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"2_CR27","doi-asserted-by":"crossref","unstructured":"Veeling, B.S., Linmans, J., Winkens, J., Cohen, T., Welling, M.: Rotation equivariant CNNs for digital pathology (2018)","DOI":"10.1007\/978-3-030-00934-2_24"},{"key":"2_CR28","doi-asserted-by":"publisher","unstructured":"Wu, Y., He, K.: Group normalization. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) Computer Vision - ECCV 2018 - 15th European Conference, Munich, Germany, September 8-14, 2018, Proceedings, Part XIII. Lecture Notes in Computer Science, vol. 11217, pp. 3\u201319. Springer (2018). https:\/\/doi.org\/10.1007\/978-3-030-01261-8_1","DOI":"10.1007\/978-3-030-01261-8_1"},{"key":"2_CR29","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2021.102083","volume":"71","author":"Y Yan","year":"2021","unstructured":"Yan, Y., Conze, P.H., Lamard, M., Quellec, G., Cochener, B., Coatrieux, G.: Towards improved breast mass detection using dual-view mammogram matching. Med. Image Anal. 71, 102083 (2021)","journal-title":"Med. Image Anal."},{"key":"2_CR30","doi-asserted-by":"crossref","unstructured":"Zhang, B., Vakanski, A., Xian, M.: BI-RADS-Net: an explainable multitask learning approach for cancer diagnosis in breast ultrasound images. In: 2021 IEEE 31st International Workshop on Machine Learning for Signal Processing (MLSP), pp.\u00a01\u20136. IEEE (2021)","DOI":"10.1109\/MLSP52302.2021.9596314"}],"container-title":["Lecture Notes in Computer Science","Pattern Recognition"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-78104-9_2","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,12,1]],"date-time":"2024-12-01T23:27:45Z","timestamp":1733095665000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-78104-9_2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,12,2]]},"ISBN":["9783031781032","9783031781049"],"references-count":30,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-78104-9_2","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2024,12,2]]},"assertion":[{"value":"2 December 2024","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":"Kolkata","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"India","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2024","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"1 December 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"5 December 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"27","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"icpr2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/icpr2024.org\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}