{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T05:59:12Z","timestamp":1760162352874,"version":"3.40.3"},"publisher-location":"Cham","reference-count":30,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031755422"},{"type":"electronic","value":"9783031755439"}],"license":[{"start":{"date-parts":[[2024,10,17]],"date-time":"2024-10-17T00:00:00Z","timestamp":1729123200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,10,17]],"date-time":"2024-10-17T00:00:00Z","timestamp":1729123200000},"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\/978-3-031-75543-9_13","type":"book-chapter","created":{"date-parts":[[2024,10,16]],"date-time":"2024-10-16T23:03:34Z","timestamp":1729119814000},"page":"168-181","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Leveraging Pre-trained Models for\u00a0Robust Federated Learning for\u00a0Kidney Stone Type Recognition"],"prefix":"10.1007","author":[{"given":"Ivan","family":"Reyes-Amezcua","sequence":"first","affiliation":[]},{"given":"Michael","family":"Rojas-Ruiz","sequence":"additional","affiliation":[]},{"given":"Gilberto","family":"Ochoa-Ruiz","sequence":"additional","affiliation":[]},{"given":"Andres","family":"Mendez-Vazquez","sequence":"additional","affiliation":[]},{"given":"Christian","family":"Daul","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,10,17]]},"reference":[{"key":"13_CR1","doi-asserted-by":"publisher","first-page":"75","DOI":"10.1038\/s41597-023-01981-y","volume":"10","author":"S Ali","year":"2023","unstructured":"Ali, S., et al.: A multi-centre polyp detection and segmentation dataset for generalisability assessment. Sci. Data 10, 75 (2023). https:\/\/doi.org\/10.1038\/s41597-023-01981-y","journal-title":"Sci. Data"},{"key":"13_CR2","unstructured":"Beutel, D.J., et\u00a0al.: Flower: a friendly federated learning research framework (2020). preprint at https:\/\/arxiv.org\/abs\/2007.14390"},{"key":"13_CR3","doi-asserted-by":"publisher","unstructured":"Bi, W.L., et\u00a0al.: Artificial intelligence in cancer imaging: clinical challenges and applications. CA Cancer J. Clin. 69, 127\u2013157 (2019). https:\/\/doi.org\/10.3322\/caac.21552","DOI":"10.3322\/caac.21552"},{"key":"13_CR4","doi-asserted-by":"publisher","unstructured":"Chowdhury, D., et al.: Federated learning based COVID-19 detection. Expert Syst. 40, e13173 (2023). https:\/\/doi.org\/10.1111\/exsy.13173","DOI":"10.1111\/exsy.13173"},{"issue":"1","key":"13_CR5","doi-asserted-by":"publisher","first-page":"13","DOI":"10.1016\/j.euf.2020.11.004","volume":"7","author":"M Corrales","year":"2021","unstructured":"Corrales, M., Doizi, S., Barghouthy, Y., Traxer, O., Daudon, M.: Classification of stones according to michel daudon: a narrative review. Eur. Urol. Focus 7(1), 13\u201321 (2021)","journal-title":"Eur. Urol. Focus"},{"issue":"2","key":"13_CR6","doi-asserted-by":"publisher","first-page":"p31","DOI":"10.1159\/000080261","volume":"98","author":"M Daudon","year":"2004","unstructured":"Daudon, M., Jungers, P.: Clinical value of crystalluria and quantitative morphoconstitutional analysis of urinary calculi. Nephron Physiol. 98(2), p31\u2013p36 (2004)","journal-title":"Nephron Physiol."},{"key":"13_CR7","unstructured":"Deng, Y., Gazagnadou, N., Hong, J., Mahdavi, M., Lyu, L.: On the hardness of robustness transfer: a perspective from rademacher complexity over symmetric difference hypothesis space (2023). preprint at https:\/\/arxiv.org\/abs\/2302.12351"},{"key":"13_CR8","doi-asserted-by":"crossref","unstructured":"El\u00a0Beze, J., et al.: Evaluation and understanding of automated urinary stone recognition methods. BJU Int. (2022)","DOI":"10.1111\/bju.15767"},{"issue":"2","key":"13_CR9","doi-asserted-by":"publisher","first-page":"F26","DOI":"10.1016\/j.fpurol.2017.03.002","volume":"27","author":"V Estrade","year":"2017","unstructured":"Estrade, V., Daudon, M., Traxer, O., M\u00e9ria, P., et al.: Pourquoi l\u2019urologue doit savoir reconna\u00eetre un calcul et comment faire? les bases de la reconnaissance endoscopique. Progr\u00e8s en Urologie-FMC 27(2), F26\u2013F35 (2017)","journal-title":"Progr\u00e8s en Urologie-FMC"},{"key":"13_CR10","doi-asserted-by":"crossref","unstructured":"Fonio, S.: Benchmarking federated learning frameworks for medical imaging tasks (2024), paper presented at Image Analysis and Processing - ICIAP 2023 Workshops, 21 January 2024","DOI":"10.1007\/978-3-031-51026-7_20"},{"key":"13_CR11","doi-asserted-by":"publisher","unstructured":"Hamdi, M., Bourouis, S., Rastislav, K., Mohmed, F.: Evaluation of neuro images for the diagnosis of Alzheimer\u2019s disease using deep learning neural network. Front. Public Health 10, 834032 (2022). https:\/\/doi.org\/10.3389\/fpubh.2022.834032","DOI":"10.3389\/fpubh.2022.834032"},{"key":"13_CR12","unstructured":"Hendrycks, D., Dietterich, T.: Benchmarking neural network robustness to common corruptions and perturbations (2019). preprint at https:\/\/arxiv.org\/abs\/1903.12261"},{"key":"13_CR13","unstructured":"Hendrycks, D., Lee, K., Mazeika, M.: Using pre-training can improve model robustness and uncertainty (2019). preprint at https:\/\/arxiv.org\/abs\/1901.09960"},{"key":"13_CR14","doi-asserted-by":"crossref","unstructured":"Hendrycks, D., Zhao, K., Basart, S., Steinhardt, J., Song, D.: Natural adversarial examples (2021). preprint at https:\/\/arxiv.org\/abs\/1907.07174","DOI":"10.1109\/CVPR46437.2021.01501"},{"key":"13_CR15","unstructured":"Hong, J., Wang, H., Wang, Z., Zhou, J.: Federated robustness propagation: sharing adversarial robustness in federated learning (2022). preprint at https:\/\/arxiv.org\/abs\/2106.10196"},{"key":"13_CR16","doi-asserted-by":"crossref","unstructured":"Kornblith, S., Shlens, J., Le, Q.V.: Do better imagenet models transfer better? (2019). preprint at https:\/\/arxiv.org\/abs\/1805.08974","DOI":"10.1109\/CVPR.2019.00277"},{"key":"13_CR17","unstructured":"Li, S., Cheng, Y., Wang, W., Liu, Y., Chen, T.: Learning to detect malicious clients for robust federated learning (2020). preprint at https:\/\/arxiv.org\/abs\/2002.00211"},{"key":"13_CR18","doi-asserted-by":"publisher","unstructured":"Li, T., Sahu, A.K., Talwalkar, A., Smith, V.: Federated learning: challenges, methods, and future directions. IEEE Signal Process. Mag. 37, 50\u201360 (2020). https:\/\/doi.org\/10.1109\/MSP.2020.2975749","DOI":"10.1109\/MSP.2020.2975749"},{"key":"13_CR19","doi-asserted-by":"crossref","unstructured":"Lopez, F., et al.: Assessing deep learning methods for the identification of kidney stones in endoscopic images (2021), paper presented at the 43rd Annual International Conference of the IEEE Engineering in Medicine and Biology Conference (EMBC), November 2021","DOI":"10.1109\/EMBC46164.2021.9630211"},{"key":"13_CR20","doi-asserted-by":"publisher","unstructured":"Lopez-Tiro, F., et al.: Boosting kidney stone identification in endoscopic images using two-step transfer learning. In: Calvo, H., Martinez-Villasenor, L., Ponce, H. (eds.) Advances in Soft Computing. MICAI 2023. LNCS, vol. 14392, pp. 131\u2013141. Springer, Cham (2024). https:\/\/doi.org\/10.1007\/978-3-031-47640-2_11","DOI":"10.1007\/978-3-031-47640-2_11"},{"key":"13_CR21","doi-asserted-by":"publisher","unstructured":"Lyu, L., et al.: Privacy and robustness in federated learning: attacks and defenses. IEEE Trans. Neural Netw. Learn. Syst. 1\u201321 (2022). https:\/\/doi.org\/10.1109\/TNNLS.2022.3216981","DOI":"10.1109\/TNNLS.2022.3216981"},{"key":"13_CR22","doi-asserted-by":"publisher","unstructured":"Maier-Hein, L., et\u00a0al.: Surgical data science\u2013from concepts toward clinical translation. Med. Image Anal. 76, 102306 (2022). https:\/\/doi.org\/10.1016\/j.media.2021.102306","DOI":"10.1016\/j.media.2021.102306"},{"key":"13_CR23","unstructured":"McMahan, B., Moore, E., Ramage, D., Hampson, S., y\u00a0Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data (2017). preprint at https:\/\/arxiv.org\/abs\/1602.05629"},{"key":"13_CR24","doi-asserted-by":"publisher","unstructured":"Ng, D., Lan, X., Yao, M.M.S., Chan, W.P., Feng, M.: Federated learning: a collaborative effort to achieve better medical imaging models for individual sites that have small labelled datasets. Quant. Imaging Med. Surg. 11, 852 (2021). https:\/\/doi.org\/10.21037\/qims-20-595","DOI":"10.21037\/qims-20-595"},{"key":"13_CR25","unstructured":"Raghu, M., Zhang, C., Kleinberg, J., Bengio, S.: Transfusion: understanding transfer learning for medical imaging (2019). preprint at https:\/\/arxiv.org\/abs\/1902.07208"},{"key":"13_CR26","doi-asserted-by":"crossref","unstructured":"Reyes-Amezcua, I., Ochoa-Ruiz, G., Mendez-Vazquez, A.: Enhancing image classification robustness through adversarial sampling with delta data augmentation (DDA), paper presented at the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (2024)","DOI":"10.1109\/CVPRW63382.2024.00032"},{"key":"13_CR27","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"53","DOI":"10.1007\/978-3-030-58580-8_4","volume-title":"Computer Vision \u2013 ECCV 2020","author":"E Rusak","year":"2020","unstructured":"Rusak, E., et al.: A simple way to make neural networks robust against diverse image corruptions. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12348, pp. 53\u201369. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-58580-8_4"},{"key":"13_CR28","doi-asserted-by":"publisher","unstructured":"Sheller, M.J., et\u00a0al.: Federated learning in medicine: facilitating multi-institutional collaborations without sharing patient data. Sci. Rep. 10, 12598 (2020). https:\/\/doi.org\/10.1038\/s41598-020-69250-1","DOI":"10.1038\/s41598-020-69250-1"},{"key":"13_CR29","unstructured":"Vaishnavi, P., Eykholt, K., Rahmati, A.: Transferring adversarial robustness through robust representation matching, paper presented at the 31st USENIX Security Symposium (USENIX Security 22) (2022)"},{"key":"13_CR30","doi-asserted-by":"crossref","unstructured":"Wang, S., Veldhuis, R., Brune, C., Strisciuglio, N.: A survey on the robustness of computer vision models against common corruptions (2023). preprint at https:\/\/arxiv.org\/abs\/2305.06024","DOI":"10.2139\/ssrn.4960634"}],"container-title":["Lecture Notes in Computer Science","Advances in Soft Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-75543-9_13","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,16]],"date-time":"2024-10-16T23:19:01Z","timestamp":1729120741000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-75543-9_13"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,10,17]]},"ISBN":["9783031755422","9783031755439"],"references-count":30,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-75543-9_13","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2024,10,17]]},"assertion":[{"value":"17 October 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"MICAI","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Mexican International Conference on Artificial Intelligence","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Tonantzintla","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Mexico","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":"21 October 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"25 October 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"23","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"micai2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/www.micai.org\/2024\/index.php","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}