{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,16]],"date-time":"2025-09-16T20:57:14Z","timestamp":1758056234877,"version":"3.44.0"},"publisher-location":"Cham","reference-count":21,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783032051752","type":"print"},{"value":"9783032051769","type":"electronic"}],"license":[{"start":{"date-parts":[[2025,9,15]],"date-time":"2025-09-15T00:00:00Z","timestamp":1757894400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,9,15]],"date-time":"2025-09-15T00:00:00Z","timestamp":1757894400000},"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":[[2026]]},"DOI":"10.1007\/978-3-032-05176-9_2","type":"book-chapter","created":{"date-parts":[[2025,9,15]],"date-time":"2025-09-15T13:40:25Z","timestamp":1757943625000},"page":"15-25","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Deep Neural Networks to the Detection of Lumbar Hernias: Methodology and Preliminary Results"],"prefix":"10.1007","author":[{"given":"Ant\u00f3nio","family":"Fernandes","sequence":"first","affiliation":[]},{"given":"Jo\u00e3o","family":"Rodriguez","sequence":"additional","affiliation":[]},{"given":"Susana","family":"Moleirinho","sequence":"additional","affiliation":[]},{"given":"Irina","family":"Trofimenko","sequence":"additional","affiliation":[]},{"given":"Ekaterina","family":"Guseva","sequence":"additional","affiliation":[]},{"given":"Alexander","family":"Martinovich","sequence":"additional","affiliation":[]},{"given":"Ilzane","family":"Morais","sequence":"additional","affiliation":[]},{"given":"Louise","family":"Bisolo","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0281-2339","authenticated-orcid":false,"given":"Luis M.","family":"Gomes","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4121-6169","authenticated-orcid":false,"given":"Jos\u00e9 M.","family":"Machado","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,9,15]]},"reference":[{"key":"2_CR1","unstructured":"Radiology ES of Radiology staffing and workload in Europe: a growing crisis (2023)"},{"key":"2_CR2","doi-asserted-by":"crossref","unstructured":"Brady, A.P., et al.: Developing, purchasing, implementing and monitoring AI tools in radiology: practical considerations. A multi-society statement from the ACR, CAR, ESR, RANZCR & RSNA. Can. Assoc. Radiol. J. 75(2), 226\u2013244 (2024)","DOI":"10.1177\/08465371231222229"},{"issue":"2","key":"2_CR3","doi-asserted-by":"publisher","first-page":"505","DOI":"10.1148\/rg.2017160130","volume":"37","author":"BJ Erickson","year":"2017","unstructured":"Erickson, B.J., Korfiatis, P., Akkus, Z., Kline, T.L.: Machine learning for medical imaging. Radiographics 37(2), 505\u2013515 (2017)","journal-title":"Radiographics"},{"key":"2_CR4","unstructured":"Grand Challenge AI Database (2024)"},{"issue":"10137","key":"2_CR5","doi-asserted-by":"publisher","first-page":"2356","DOI":"10.1016\/S0140-6736(18)30480-X","volume":"391","author":"J Hartvigsen","year":"2018","unstructured":"Hartvigsen, J., et al.: What low back pain is and why we need to pay attention. Lancet 391(10137), 2356\u20132367 (2018)","journal-title":"Lancet"},{"issue":"6","key":"2_CR6","doi-asserted-by":"publisher","first-page":"299","DOI":"10.21037\/atm.2020.02.175","volume":"8","author":"A Wu","year":"2020","unstructured":"Wu, A., et al.: Global low back pain prevalence and years lived with disability from 1990 to 2017: estimates from the Global Burden of Disease Study 2017. Ann. Transl. Med. 8(6), 299 (2020)","journal-title":"Ann. Transl. Med."},{"key":"2_CR7","doi-asserted-by":"crossref","unstructured":"Awadalla, A.M., et al.: Management of lumbar disc herniation: a systematic review. Cureus 15(10) (2023)","DOI":"10.7759\/cureus.47908"},{"issue":"1","key":"2_CR8","doi-asserted-by":"publisher","first-page":"11","DOI":"10.1186\/s41747-023-00408-y","volume":"8","author":"F Galbusera","year":"2024","unstructured":"Galbusera, F., Cina, A.: Image annotation and curation in radiology: an overview for machine learning practitioners. Eur. Radiol. Exp. 8(1), 11 (2024)","journal-title":"Eur. Radiol. Exp."},{"issue":"10137","key":"2_CR9","doi-asserted-by":"publisher","first-page":"2368","DOI":"10.1016\/S0140-6736(18)30489-6","volume":"391","author":"NE Foster","year":"2018","unstructured":"Foster, N.E., et al.: Prevention and treatment of low back pain: evidence, challenges, and promising directions. Lancet 391(10137), 2368\u20132383 (2018)","journal-title":"Lancet"},{"key":"2_CR10","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_CR11","unstructured":"Tan, M., Le, Q.: Efficientnet: rethinking model scaling for convolutional neural networks. In: International Conference on Machine Learning, pp. 6105\u20136114. PMLR (2019)"},{"key":"2_CR12","doi-asserted-by":"crossref","unstructured":"Hu, J., Shen, L., Sun, G.: Squeeze-and-excitation networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 7132\u20137141 (2018)","DOI":"10.1109\/CVPR.2018.00745"},{"key":"2_CR13","unstructured":"Dosovitskiy, A., et al.: An image is worth 16\u00a0\u00d7\u00a016 words: transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020)"},{"key":"2_CR14","doi-asserted-by":"crossref","unstructured":"Ronneberger, O., Fischer, P., Brox, T.: U-net: convolutional networks for biomedical image segmentation. In: Medical Image Computing and Computer-Assisted Intervention\u2013MICCAI 2015: 18th International Conference, Munich, Germany, 5\u20139 October 2015, Proceedings, Part III 18, pp. 234\u2013241. Springer, Cham (2015)","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"2_CR15","doi-asserted-by":"crossref","unstructured":"Zhou, Z., Rahman Siddiquee, M.M., Tajbakhsh, N., Liang, J.: UNet++: a nested u-net architecture for medical image segmentation. In: Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support: 4th International Workshop, DLMIA 2018, and 8th International Workshop, ML-CDS 2018, Held in Conjunction with MICCAI 2018, Granada, Spain, 20 September 20, Proceedings 4, pp. 3\u201311. Springer, Cham (2018)","DOI":"10.1007\/978-3-030-00889-5_1"},{"key":"2_CR16","doi-asserted-by":"crossref","unstructured":"Chen, L.C., Zhu, Y., Papandreou, G., Schroff, F., Adam, H.: Encoder-decoder with atrous separable convolution for semantic image segmentation. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 801\u2013818 (2018)","DOI":"10.1007\/978-3-030-01234-2_49"},{"key":"2_CR17","unstructured":"Xie, E., Wang, W., Yu, Z., Anandkumar, A., Alvarez, J.M., Luo, P.: SegFormer: simple and efficient design for semantic segmentation with transformers. In: Advances in Neural Information Processing Systems, vol. 34, 12077\u201312090 (2021)"},{"issue":"1","key":"2_CR18","doi-asserted-by":"publisher","first-page":"20098","DOI":"10.1038\/s41598-023-46580-4","volume":"13","author":"K Hettihewa","year":"2023","unstructured":"Hettihewa, K., Kobchaisawat, T., Tanpowpong, N., Chalidabhongse, T.H.: MANet: a multi-attention network for automatic liver tumor segmentation in computed tomography (CT) imaging. Sci. Rep. 13(1), 20098 (2023)","journal-title":"Sci. Rep."},{"key":"2_CR19","unstructured":"Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)"},{"issue":"3","key":"2_CR20","doi-asserted-by":"publisher","first-page":"297","DOI":"10.2307\/1932409","volume":"26","author":"LR Dice","year":"1945","unstructured":"Dice, L.R.: Measures of the amount of ecologic association between species. Ecology 26(3), 297\u2013302 (1945)","journal-title":"Ecology"},{"issue":"1","key":"2_CR21","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1145\/584091.584093","volume":"5","author":"CE Shannon","year":"2001","unstructured":"Shannon, C.E.: A mathematical theory of communication. ACM SIGMOBILE Mob. Comput. Commun. Rev. 5(1), 3\u201355 (2001)","journal-title":"ACM SIGMOBILE Mob. Comput. Commun. Rev."}],"container-title":["Lecture Notes in Computer Science","Progress in Artificial Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-032-05176-9_2","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,15]],"date-time":"2025-09-15T13:40:40Z","timestamp":1757943640000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-032-05176-9_2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,9,15]]},"ISBN":["9783032051752","9783032051769"],"references-count":21,"URL":"https:\/\/doi.org\/10.1007\/978-3-032-05176-9_2","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,9,15]]},"assertion":[{"value":"15 September 2025","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"The authors have no competing interests to declare that are relevant to the content of this article.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Disclosure of Interests"}},{"value":"EPIA","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"EPIA Conference on Artificial Intelligence","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Faro","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Portugal","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2025","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"1 October 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"3 October 2025","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"24","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"epia2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/epia2025.ualg.pt\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}