{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,16]],"date-time":"2026-06-16T12:34:19Z","timestamp":1781613259953,"version":"3.54.5"},"publisher-location":"Cham","reference-count":26,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783032051684","type":"print"},{"value":"9783032051691","type":"electronic"}],"license":[{"start":{"date-parts":[[2025,9,20]],"date-time":"2025-09-20T00:00:00Z","timestamp":1758326400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,9,20]],"date-time":"2025-09-20T00:00:00Z","timestamp":1758326400000},"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\/978-3-032-05169-1_42","type":"book-chapter","created":{"date-parts":[[2025,9,19]],"date-time":"2025-09-19T21:50:21Z","timestamp":1758318621000},"page":"433-442","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["NQNN: Noise-Aware Quantum Neural Networks for\u00a0Medical Image Classification"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0009-0007-5240-0662","authenticated-orcid":false,"given":"Maqsudur","family":"Rahman","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7142-2193","authenticated-orcid":false,"given":"Jun","family":"Zhuang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,9,20]]},"reference":[{"issue":"1","key":"42_CR1","doi-asserted-by":"publisher","first-page":"126","DOI":"10.1186\/s12880-023-01084-5","volume":"23","author":"N Ajlouni","year":"2023","unstructured":"Ajlouni, N., \u00d6zyava\u015f, A., Takao\u011flu, M., Takao\u011flu, F., Ajlouni, F.: Medical image diagnosis based on adaptive hybrid quantum cnn. BMC Med. Imaging 23(1), 126 (2023)","journal-title":"BMC Med. Imaging"},{"key":"42_CR2","unstructured":"Aliev, V., et al.: Label denoising with large ensembles of heterogeneous neural networks. In: Proceedings of the 2nd Workshop on YouTube-8M Large-Scale Video Understanding (2018)"},{"key":"42_CR3","doi-asserted-by":"publisher","first-page":"1069985","DOI":"10.3389\/fphy.2022.1069985","volume":"10","author":"D Bokhan","year":"2022","unstructured":"Bokhan, D., Mastiukova, A.S., Boev, A.S., Trubnikov, D.N., Fedorov, A.K.: Multiclass classification using quantum convolutional neural networks with hybrid quantum-classical learning. Front. Phys. 10, 1069985 (2022)","journal-title":"Front. Phys."},{"key":"42_CR4","unstructured":"Damian, A., Warmuth, M.K., Zhang, L., Chen, B., Li, F.: Analysis of classifiers robust to noisy labels. In: Proceedings of the 36th International Conference on Machine Learning, vol. 97, pp. 1500\u20131509 (2019), http:\/\/proceedings.mlr.press\/v97\/damian19a.html"},{"issue":"2","key":"42_CR5","doi-asserted-by":"publisher","first-page":"34","DOI":"10.1007\/s11128-023-04237-1","volume":"23","author":"B Dhara","year":"2024","unstructured":"Dhara, B., Agrawal, M., Roy, S.D.: Multi-class classification using quantum transfer learning. Quantum Inf. Process. 23(2), 34 (2024)","journal-title":"Quantum Inf. Process."},{"key":"42_CR6","unstructured":"D\u00edaz, A., Steele, D.: Analysis of classifiers robust to noisy labels. arXiv preprint arXiv:2106.00274 (2021)"},{"key":"42_CR7","unstructured":"Englesson, E., Azizpour, H.: Robust classification via regression for learning with noisy labels. In: ICLR 2024-The Twelfth International Conference on Learning Representations, Messe Wien Exhibition and Congress Center, Vienna, Austria, 7-11th May 2024 (2024)"},{"issue":"6","key":"42_CR8","doi-asserted-by":"publisher","first-page":"2825","DOI":"10.1109\/TIP.2017.2689998","volume":"26","author":"BB Gao","year":"2017","unstructured":"Gao, B.B., Xing, C., Xie, C.W., Wu, J., Geng, X.: Deep label distribution learning with label ambiguity. IEEE Trans. Image Process. 26(6), 2825\u20132838 (2017)","journal-title":"IEEE Trans. Image Process."},{"issue":"6","key":"42_CR9","doi-asserted-by":"publisher","first-page":"1533","DOI":"10.1109\/TMI.2022.3141425","volume":"41","author":"L Ju","year":"2022","unstructured":"Ju, L., et al.: Improving medical images classification with label noise using dual-uncertainty estimation. IEEE Trans. Med. Imaging 41(6), 1533\u20131546 (2022)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"42_CR10","doi-asserted-by":"publisher","first-page":"101759","DOI":"10.1016\/j.media.2020.101759","volume":"65","author":"D Karimi","year":"2020","unstructured":"Karimi, D., Dou, H., Warfield, S.K., Gholipour, A.: Deep learning with noisy labels: exploring techniques and remedies in medical image analysis. Med. Image Anal. 65, 101759 (2020)","journal-title":"Med. Image Anal."},{"key":"42_CR11","doi-asserted-by":"crossref","unstructured":"Khanal, B., Shrestha, P., Amgain, S., Khanal, B., Bhattarai, B., Linte, C.A.: Investigating the robustness of vision transformers against label noise in medical image classification. arXiv preprint arXiv:2402.16734 (2024)","DOI":"10.1109\/EMBC53108.2024.10782929"},{"key":"42_CR12","doi-asserted-by":"publisher","first-page":"881","DOI":"10.22331\/q-2022-12-22-881","volume":"6","author":"J Landman","year":"2022","unstructured":"Landman, J., et al.: Quantum methods for neural networks and application to medical image classification. Quantum 6, 881 (2022)","journal-title":"Quantum"},{"key":"42_CR13","doi-asserted-by":"crossref","unstructured":"Lee, K.H., He, X., Zhang, L., Yang, L.: Cleannet: transfer learning for scalable image classifier training with label noise. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5447\u20135456 (2018)","DOI":"10.1109\/CVPR.2018.00571"},{"issue":"9","key":"42_CR14","doi-asserted-by":"publisher","first-page":"290311","DOI":"10.1007\/s11433-021-1734-3","volume":"64","author":"J Liu","year":"2021","unstructured":"Liu, J., Lim, K.H., Wood, K.L., Huang, W., Guo, C., Huang, H.L.: Hybrid quantum-classical convolutional neural networks. Sci. China Phys. Mech. Astron. 64(9), 290311 (2021)","journal-title":"Sci. China Phys. Mech. Astron."},{"key":"42_CR15","unstructured":"Mathur, N., et al.: Medical image classification via quantum neural networks. arXiv preprint arXiv:2109.01831 (2021)"},{"key":"42_CR16","doi-asserted-by":"crossref","unstructured":"Mazher, M., Qayyum, A., Khan, M.A., Niederer, S., Mokayef, M., Hassan, C.: Hybrid classical and quantum deep learning models for medical image classification (2024)","DOI":"10.5954\/ICAROB.2024.OS7-4"},{"key":"42_CR17","unstructured":"Northcutt, C.G., Wu, T., Chuang, I.L.: Learning with confident examples: rank pruning for robust classification with noisy labels. arXiv preprint arXiv:1705.01936 (2017)"},{"key":"42_CR18","doi-asserted-by":"crossref","unstructured":"Ostyakov, P., et al.: Label denoising with large ensembles of heterogeneous neural networks. In: Proceedings of the European Conference on Computer Vision (ECCV) Workshops (2018)","DOI":"10.1007\/978-3-030-11018-5_23"},{"key":"42_CR19","unstructured":"Shu, J., et al.: Meta-weight-net: Learning an explicit mapping for sample weighting. In: Advances in Neural Information Processing Systems, vol. 32 (2019)"},{"key":"42_CR20","doi-asserted-by":"crossref","unstructured":"Trochun, Y., Stirenko, S., Rokovyi, O., Alienin, O., Pavlov, E., Gordienko, Y.: Hybrid classic-quantum neural networks for image classification. In: 2021 11th IEEE International Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications (IDAACS), vol.\u00a02, pp. 968\u2013972. IEEE (2021)","DOI":"10.1109\/IDAACS53288.2021.9661011"},{"key":"42_CR21","doi-asserted-by":"crossref","unstructured":"Veit, A., Alldrin, N., Chechik, G., Krasin, I., Gupta, A., Belongie, S.: Learning from noisy large-scale datasets with minimal supervision. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 839\u2013847 (2017)","DOI":"10.1109\/CVPR.2017.696"},{"issue":"6","key":"42_CR22","doi-asserted-by":"publisher","first-page":"1371","DOI":"10.1109\/TMI.2021.3140140","volume":"41","author":"C Xue","year":"2022","unstructured":"Xue, C., Yu, L., Chen, P., Dou, Q., Heng, P.A.: Robust medical image classification from noisy labeled data with global and local representation guided co-training. IEEE Trans. Med. Imaging 41(6), 1371\u20131382 (2022)","journal-title":"IEEE Trans. Med. Imaging"},{"issue":"1","key":"42_CR23","doi-asserted-by":"publisher","first-page":"41","DOI":"10.1038\/s41597-022-01721-8","volume":"10","author":"J Yang","year":"2023","unstructured":"Yang, J., et al.: Medmnist v2-a large-scale lightweight benchmark for 2d and 3d biomedical image classification. Sci. Data 10(1), 41 (2023)","journal-title":"Sci. Data"},{"key":"42_CR24","doi-asserted-by":"crossref","unstructured":"Yousif, M., Al-Khateeb, B., Garcia-Zapirain, B.: A new quantum circuits of quantum convolutional neural network for x-ray images classification. IEEE Access (2024)","DOI":"10.1109\/ACCESS.2024.3396411"},{"key":"42_CR25","doi-asserted-by":"crossref","unstructured":"Zhuang, J., Al\u00a0Hasan, M.: Defending graph convolutional networks against dynamic graph perturbations via bayesian self-supervision. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol.\u00a036, pp. 4405\u20134413 (2022)","DOI":"10.1609\/aaai.v36i4.20362"},{"key":"42_CR26","unstructured":"Zhuang, J., Guan, C.: Large language models can help mitigate barren plateaus. arXiv preprint arXiv:2502.13166 (2025)"}],"container-title":["Lecture Notes in Computer Science","Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2025"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-032-05169-1_42","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,19]],"date-time":"2025-09-19T21:50:26Z","timestamp":1758318626000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-032-05169-1_42"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,9,20]]},"ISBN":["9783032051684","9783032051691"],"references-count":26,"URL":"https:\/\/doi.org\/10.1007\/978-3-032-05169-1_42","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,9,20]]},"assertion":[{"value":"20 September 2025","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"The authors declare no competing interests. All contributions were made for academic purposes.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Disclosure of Interests"}},{"value":"MICCAI","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Medical Image Computing and Computer-Assisted Intervention","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Daejeon","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Korea (Republic of)","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":"23 September 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"27 September 2025","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"28","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"miccai2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/conferences.miccai.org\/2025\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}