{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,26]],"date-time":"2025-03-26T03:37:19Z","timestamp":1742960239385,"version":"3.40.3"},"publisher-location":"Cham","reference-count":20,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031355066"},{"type":"electronic","value":"9783031355073"}],"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\/978-3-031-35507-3_14","type":"book-chapter","created":{"date-parts":[[2023,6,2]],"date-time":"2023-06-02T21:04:13Z","timestamp":1685739853000},"page":"137-146","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Using Clinical Data and\u00a0Deep Features in\u00a0Renal Pathologies Classification"],"prefix":"10.1007","author":[{"given":"Laiara","family":"Silva","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Vin\u00edcius","family":"Machado","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Rodrigo","family":"Veras","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Keylla","family":"Aita","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Semiramis","family":"do Monte","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Nayze","family":"Aldeman","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Justino","family":"Santos","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,6,3]]},"reference":[{"key":"14_CR1","doi-asserted-by":"crossref","unstructured":"de\u00a0Ara\u00fajo, I.C., Schnitman, L., Duarte, A.A., dos Santos, W.: Automated detection of segmental glomerulosclerosis in kidney histopathology. In: XIII Brazilian Congress on Computational Intelligence, p. 12 (2017)","DOI":"10.21528\/CBIC2017-10"},{"key":"14_CR2","doi-asserted-by":"crossref","unstructured":"Claro, M., et al.: An hybrid feature space from texture information and transfer learning for glaucoma classification. J. Vis. Commun. Image Represent. 64, 102597 (2019), https:\/\/www.sciencedirect.com\/science\/article\/pii\/S1047320319302184","DOI":"10.1016\/j.jvcir.2019.102597"},{"issue":"4\u20135","key":"14_CR3","doi-asserted-by":"publisher","first-page":"198","DOI":"10.1016\/j.compmedimag.2007.02.002","volume":"31","author":"K Doi","year":"2007","unstructured":"Doi, K.: Computer-aided diagnosis in medical imaging: historical review, current status and future potential. Comput. Med. Imaging Graph. 31(4\u20135), 198\u2013211 (2007)","journal-title":"Comput. Med. Imaging Graph."},{"key":"14_CR4","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"},{"issue":"10","key":"14_CR5","doi-asserted-by":"publisher","first-page":"1968","DOI":"10.1681\/ASN.2019020144","volume":"30","author":"M Hermsen","year":"2019","unstructured":"Hermsen, M., de Bel, T., Den Boer, M., Steenbergen, E.J., Kers, J., Florquin, S., Roelofs, J.J., Stegall, M.D., Alexander, M.P., Smith, B.H., et al.: Deep learning-based histopathologic assessment of kidney tissue. J. Am. Soc. Nephrol. 30(10), 1968\u20131979 (2019)","journal-title":"J. Am. Soc. Nephrol."},{"key":"14_CR6","doi-asserted-by":"crossref","unstructured":"Huo, Y., Deng, R., Liu, Q., Fogo, A.B., Yang, H.: Ai applications in renal pathology. Kidney International (2021)","DOI":"10.1016\/j.kint.2021.01.015"},{"issue":"7","key":"14_CR7","doi-asserted-by":"publisher","first-page":"955","DOI":"10.1016\/j.ekir.2019.04.008","volume":"4","author":"S Kannan","year":"2019","unstructured":"Kannan, S., Morgan, L.A., Liang, B., Cheung, M.G., Lin, C.Q., Mun, D., Nader, R.G., Belghasem, M.E., Henderson, J.M., Francis, J.M., Chitalia, V.C., Kolachalama, V.B.: Segmentation of glomeruli within trichrome images using deep learning. Kidney Int. Rep. 4(7), 955\u2013962 (2019)","journal-title":"Kidney Int. Rep."},{"key":"14_CR8","doi-asserted-by":"crossref","unstructured":"Kornblith, S., Shlens, J., Le, Q.V.: Do better ImageNet models transfer better? In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 2661\u20132671 (2019)","DOI":"10.1109\/CVPR.2019.00277"},{"key":"14_CR9","doi-asserted-by":"crossref","unstructured":"Landis, J.R., Koch, G.G.: The measurement of observer agreement for categorical data. Biometrics, pp. 159\u2013174 (1977)","DOI":"10.2307\/2529310"},{"issue":"6","key":"14_CR10","doi-asserted-by":"publisher","first-page":"6869","DOI":"10.1007\/s11042-018-6404-8","volume":"78","author":"N Moura","year":"2018","unstructured":"Moura, N., Veras, R., Aires, K., Machado, V., Silva, R., Ara\u00fajo, F., Claro, M.: ABCD rule and pre-trained CNNs for melanoma diagnosis. Multimedia Tools Appl. 78(6), 6869\u20136888 (2018). https:\/\/doi.org\/10.1007\/s11042-018-6404-8","journal-title":"Multimedia Tools Appl."},{"issue":"3","key":"14_CR11","doi-asserted-by":"publisher","first-page":"211","DOI":"10.1007\/s11263-015-0816-y","volume":"115","author":"O Russakovsky","year":"2015","unstructured":"Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., et al.: ImageNet large scale visual recognition challenge. Int. J. Comput. Vis. 115(3), 211\u2013252 (2015)","journal-title":"Int. J. Comput. Vis."},{"key":"14_CR12","doi-asserted-by":"crossref","unstructured":"Sandler, M., Howard, A., Zhu, M., Zhmoginov, A., Chen, L.C.: Mobilenetv2: Inverted residuals and linear bottlenecks. In: 2018 IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 4510\u20134520 (2018)","DOI":"10.1109\/CVPR.2018.00474"},{"key":"14_CR13","doi-asserted-by":"crossref","unstructured":"Santos, J.D., et al.: A hybrid of deep and textural features to differentiate glomerulosclerosis and minimal change disease from glomerulus biopsy images. Biomed. Signal Process. Control 70, 103020 (2021) https:\/\/www.sciencedirect.com\/science\/article\/pii\/S1746809421006170","DOI":"10.1016\/j.bspc.2021.103020"},{"key":"14_CR14","unstructured":"Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015), http:\/\/arxiv.org\/abs\/1409.1556"},{"key":"14_CR15","first-page":"329","volume":"43","author":"FL Sodr\u00e9","year":"2007","unstructured":"Sodr\u00e9, F.L., Costa, J.C.B., Lima, J.C.C.: Evaluation of renal function and damage: a laboratorial challenge. J. Brasileiro de Patologia e Medicina Laboratorial 43, 329\u2013337 (2007)","journal-title":"J. Brasileiro de Patologia e Medicina Laboratorial"},{"key":"14_CR16","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. pp. 2818\u20132826 (2016)","DOI":"10.1109\/CVPR.2016.308"},{"key":"14_CR17","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":"14_CR18","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijmedinf.2020.104231","volume":"141","author":"E Uchino","year":"2020","unstructured":"Uchino, E., et al.: Classification of glomerular pathological findings using deep learning and nephrologist-AI collective intelligence approach. Int. J. Med. Inf. 141, 104231 (2020)","journal-title":"Int. J. Med. Inf."},{"key":"14_CR19","doi-asserted-by":"crossref","unstructured":"Vogado, L., et al.: Diagnosis of leukaemia in blood slides based on a fine-tuned and highly generalisable deep learning model. Sensors 21(9) (2021). https:\/\/www.mdpi.com\/1424-8220\/21\/9\/2989","DOI":"10.3390\/s21092989"},{"key":"14_CR20","doi-asserted-by":"crossref","unstructured":"Zheng, Z., et al.: Deep learning-based artificial intelligence system for automatic assessment of glomerular pathological findings in lupus nephritis. Diagnostics 11(11) (2021)","DOI":"10.3390\/diagnostics11111983"}],"container-title":["Lecture Notes in Networks and Systems","Intelligent Systems Design and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-35507-3_14","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,6,2]],"date-time":"2023-06-02T21:25:44Z","timestamp":1685741144000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-35507-3_14"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031355066","9783031355073"],"references-count":20,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-35507-3_14","relation":{},"ISSN":["2367-3370","2367-3389"],"issn-type":[{"type":"print","value":"2367-3370"},{"type":"electronic","value":"2367-3389"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"3 June 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ISDA","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Intelligent Systems Design and Applications","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2022","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"12 December 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"14 December 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"isda2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/www.mirlabs.net\/isda22\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}