{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,3]],"date-time":"2026-02-03T18:08:46Z","timestamp":1770142126797,"version":"3.49.0"},"reference-count":63,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2024,1,13]],"date-time":"2024-01-13T00:00:00Z","timestamp":1705104000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,1,13]],"date-time":"2024-01-13T00:00:00Z","timestamp":1705104000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100004775","name":"Natural Science Foundation of Gansu Province","doi-asserted-by":"publisher","award":["22JR11RA042"],"award-info":[{"award-number":["22JR11RA042"]}],"id":[{"id":"10.13039\/501100004775","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004775","name":"Natural Science Foundation of Gansu Province","doi-asserted-by":"publisher","award":["22JR11RA042"],"award-info":[{"award-number":["22JR11RA042"]}],"id":[{"id":"10.13039\/501100004775","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004775","name":"Natural Science Foundation of Gansu Province","doi-asserted-by":"publisher","award":["22JR11RA042"],"award-info":[{"award-number":["22JR11RA042"]}],"id":[{"id":"10.13039\/501100004775","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004775","name":"Natural Science Foundation of Gansu Province","doi-asserted-by":"publisher","award":["22JR11RA042"],"award-info":[{"award-number":["22JR11RA042"]}],"id":[{"id":"10.13039\/501100004775","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Multimedia Systems"],"published-print":{"date-parts":[[2024,2]]},"DOI":"10.1007\/s00530-023-01215-6","type":"journal-article","created":{"date-parts":[[2024,1,13]],"date-time":"2024-01-13T02:02:19Z","timestamp":1705111339000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Multi-label neural architecture search for chest radiography image classification"],"prefix":"10.1007","volume":"30","author":[{"given":"Yi","family":"Yang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jiaxuan","family":"Wei","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhixuan","family":"Yu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ruisheng","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,1,13]]},"reference":[{"key":"1215_CR1","doi-asserted-by":"publisher","first-page":"64","DOI":"10.1016\/j.patrec.2018.04.015","volume":"111","author":"Z Ahmadi","year":"2018","unstructured":"Ahmadi, Z., Kramer, S.: A label compression method for online multi-label classification. Pattern Recogn. Lett. 111, 64\u201371 (2018). https:\/\/doi.org\/10.1016\/j.patrec.2018.04.015","journal-title":"Pattern Recogn. Lett."},{"issue":"1","key":"1215_CR2","doi-asserted-by":"publisher","first-page":"196","DOI":"10.1148\/radiol.2018180921","volume":"291","author":"M Annarumma","year":"2019","unstructured":"Annarumma, M., Withey, S.J., Bakewell, R.J., et al.: Automated triaging of adult chest radiographs with deep artificial neural networks. Radiology 291(1), 196\u2013202 (2019). https:\/\/doi.org\/10.1148\/radiol.2018180921","journal-title":"Radiology"},{"issue":"8","key":"1215_CR3","doi-asserted-by":"publisher","first-page":"663","DOI":"10.3844\/jcssp.2008.663.667","volume":"4","author":"MJ Baemani","year":"2008","unstructured":"Baemani, M.J., Monadjemi, A., Moallem, P.: Detection of respiratory abnormalities using artificial neural networks. J. Comput. Sci. 4(8), 663 (2008)","journal-title":"J. Comput. Sci."},{"key":"1215_CR4","unstructured":"Baker, B., Gupta, O., Naik, N., et\u00a0al.: Designing neural network architectures using reinforcement learning (2016). arXiv preprint arXiv:1611.02167"},{"issue":"9","key":"1215_CR5","doi-asserted-by":"publisher","first-page":"1757","DOI":"10.1016\/j.patcog.2004.03.009","volume":"37","author":"MR Boutell","year":"2004","unstructured":"Boutell, M.R., Luo, J., Shen, X., et al.: Learning multi-label scene classification. Pattern Recogn. 37(9), 1757\u20131771 (2004). https:\/\/doi.org\/10.1016\/j.patcog.2004.03.009","journal-title":"Pattern Recogn."},{"key":"1215_CR6","doi-asserted-by":"publisher","first-page":"385","DOI":"10.1016\/j.knosys.2015.07.019","volume":"89","author":"F Charte","year":"2015","unstructured":"Charte, F., Rivera, A.J., del Jesus, M.J., et al.: Mlsmote: approaching imbalanced multilabel learning through synthetic instance generation. Knowl.-Based Syst. 89, 385\u2013397 (2015). https:\/\/doi.org\/10.1016\/j.knosys.2015.07.019","journal-title":"Knowl.-Based Syst."},{"key":"1215_CR7","doi-asserted-by":"publisher","first-page":"321","DOI":"10.1613\/jair.953","volume":"16","author":"NV Chawla","year":"2002","unstructured":"Chawla, N.V., Bowyer, K.W., Hall, L.O., et al.: Smote: synthetic minority over-sampling technique. J. Artif. Intell. Res. 16, 321\u2013357 (2002). https:\/\/doi.org\/10.1613\/jair.953","journal-title":"J. Artif. Intell. Res."},{"key":"1215_CR8","doi-asserted-by":"crossref","unstructured":"Chen, X., Xie, L., Wu, J., et\u00a0al.: Progressive differentiable architecture search: Bridging the depth gap between search and evaluation. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp 1294\u20131303 (2019a)","DOI":"10.1109\/ICCV.2019.00138"},{"key":"1215_CR9","doi-asserted-by":"crossref","unstructured":"Chen, ZM., Wei, XS., Wang, P., et\u00a0al.: Multi-label image recognition with graph convolutional networks. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2019b)","DOI":"10.1109\/CVPR.2019.00532"},{"key":"1215_CR10","doi-asserted-by":"publisher","unstructured":"Dey, R., Lu, Z., Hong, Y.: Diagnostic classification of lung nodules using 3d neural networks. In: 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018), pp. 774\u2013778 (2018). https:\/\/doi.org\/10.1109\/ISBI.2018.8363687","DOI":"10.1109\/ISBI.2018.8363687"},{"key":"1215_CR11","doi-asserted-by":"crossref","unstructured":"Elisseeff, A., Weston, J.: A kernel method for multi-labelled classification. Adv. Neural Inf. Process. Syst. 14 (2001)","DOI":"10.7551\/mitpress\/1120.003.0092"},{"issue":"1","key":"1215_CR12","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1080\/21681163.2015.1124249","volume":"6","author":"M Gao","year":"2018","unstructured":"Gao, M., Bagci, U., Lu, L., et al.: Holistic classification of ct attenuation patterns for interstitial lung diseases via deep convolutional neural networks. Comput. Methods Biomech. Biomed. Eng. Imaging Vis. 6(1), 1\u20136 (2018). https:\/\/doi.org\/10.1080\/21681163.2015.1124249","journal-title":"Comput. Methods Biomech. Biomed. Eng. Imaging Vis."},{"key":"1215_CR13","doi-asserted-by":"crossref","unstructured":"Grauman, K., Darrell, T.: The pyramid match kernel: Discriminative classification with sets of image features. In: Tenth IEEE International Conference on Computer Vision (ICCV\u201905) vol. 12, pp. 1458\u20131465 (2005)","DOI":"10.1109\/ICCV.2005.239"},{"key":"1215_CR14","doi-asserted-by":"publisher","first-page":"259","DOI":"10.1016\/j.patrec.2018.10.027","volume":"130","author":"Q Guan","year":"2020","unstructured":"Guan, Q., Huang, Y.: Multi-label chest x-ray image classification via category-wise residual attention learning. Pattern Recogn. Lett. 130, 259\u2013266 (2020). https:\/\/doi.org\/10.1016\/j.patrec.2018.10.027","journal-title":"Pattern Recogn. Lett."},{"key":"1215_CR15","doi-asserted-by":"crossref","unstructured":"He, Y., Yang, D., Roth, H., et\u00a0al.: Dints: Differentiable neural network topology search for 3d medical image segmentation. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 5841\u20135850 (2021)","DOI":"10.1109\/CVPR46437.2021.00578"},{"key":"1215_CR16","doi-asserted-by":"publisher","first-page":"539","DOI":"10.1007\/978-3-030-59725-2_52","volume":"2020","author":"R Hermoza","year":"2020","unstructured":"Hermoza, R., Maicas, G., Nascimento, J.C., et al.: Region proposals for saliency map refinement for weakly-supervised disease localisation and classification. Med. Image Comput. Comput. Assist. Interv. MICCAI 2020, 539\u2013549 (2020). https:\/\/doi.org\/10.1007\/978-3-030-59725-2_52","journal-title":"Med. Image Comput. Comput. Assist. Interv. MICCAI"},{"key":"1215_CR17","doi-asserted-by":"publisher","first-page":"188","DOI":"10.1016\/j.isprsjprs.2019.01.015","volume":"149","author":"Y Hua","year":"2019","unstructured":"Hua, Y., Mou, L., Zhu, X.X.: Recurrently exploring class-wise attention in a hybrid convolutional and bidirectional lstm network for multi-label aerial image classification. ISPRS J. Photogramm. Remote. Sens. 149, 188\u2013199 (2019). https:\/\/doi.org\/10.1016\/j.isprsjprs.2019.01.015","journal-title":"ISPRS J. Photogramm. Remote. Sens."},{"key":"1215_CR18","unstructured":"Iandola, F., Moskewicz, M., Karayev, S., et\u00a0al.: Densenet: implementing efficient convnet descriptor pyramids (2014). arXiv preprint arXiv:1404.1869"},{"key":"1215_CR19","doi-asserted-by":"crossref","unstructured":"Jin, J., Nakayama, H.: Annotation order matters: Recurrent image annotator for arbitrary length image tagging. In: 2016 23rd International Conference on Pattern Recognition (ICPR), pp. 2452\u20132457 (2016)","DOI":"10.1109\/ICPR.2016.7900004"},{"key":"1215_CR20","unstructured":"Jozefowicz, R., Zaremba, W., Sutskever, I.: An empirical exploration of recurrent network architectures. In: International Conference on Machine Learning, pp. 2342\u20132350 (2015)"},{"issue":"3","key":"1215_CR21","first-page":"143","volume":"81","author":"B Kelly","year":"2012","unstructured":"Kelly, B.: The chest radiograph. Ulst. Med. J. 81(3), 143 (2012)","journal-title":"Ulst. Med. J."},{"key":"1215_CR22","doi-asserted-by":"crossref","unstructured":"Khobragade, S., Tiwari, A., Patil, C., et\u00a0al.: Automatic detection of major lung diseases using chest radiographs and classification by feed-forward artificial neural network. In: 2016 IEEE 1st International Conference on Power Electronics, Intelligent Control and Energy Systems (ICPEICES), IEEE, pp. 1\u20135 (2016)","DOI":"10.1109\/ICPEICES.2016.7853683"},{"key":"1215_CR23","doi-asserted-by":"crossref","unstructured":"Kim, E., Kim, S., Seo, M., et\u00a0al.: Xprotonet: diagnosis in chest radiography with global and local explanations. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 15719\u201315728 (2021)","DOI":"10.1109\/CVPR46437.2021.01546"},{"key":"1215_CR24","doi-asserted-by":"publisher","DOI":"10.1145\/3065386","author":"A Krizhevsky","year":"2012","unstructured":"Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Adv. Neural Inf. Process. Syst. (2012). https:\/\/doi.org\/10.1145\/3065386","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"1215_CR25","doi-asserted-by":"crossref","unstructured":"Li, Z., Wang, C., Han, M., et\u00a0al.: Thoracic disease identification and localization with limited supervision. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 8290\u20138299 (2018a)","DOI":"10.1109\/CVPR.2018.00865"},{"key":"1215_CR26","doi-asserted-by":"crossref","unstructured":"Li, Z., Wang, C., Han, M., et\u00a0al.: Thoracic disease identification and localization with limited supervision. In: 2018 IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 8290\u20138299 (2018b)","DOI":"10.1109\/CVPR.2018.00865"},{"issue":"9","key":"1215_CR27","doi-asserted-by":"publisher","first-page":"2567","DOI":"10.1007\/s11517-022-02604-1","volume":"60","author":"L Li","year":"2022","unstructured":"Li, L., Cao, P., Yang, J., et al.: Modeling global and local label correlation with graph convolutional networks for multi-label chest x-ray image classification. Med. Biol. Eng. Comput. 60(9), 2567\u20132588 (2022). https:\/\/doi.org\/10.1007\/s11517-022-02604-1","journal-title":"Med. Biol. Eng. Comput."},{"key":"1215_CR28","doi-asserted-by":"crossref","unstructured":"Liu, C., Chen, LC., Schroff, F., et\u00a0al.: Auto-deeplab: Hierarchical neural architecture search for semantic image segmentation. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 82\u201392 (2019a)","DOI":"10.1109\/CVPR.2019.00017"},{"key":"1215_CR29","unstructured":"Liu, H., Simonyan, K., Yang, Y.: DARTS: Differentiable architecture search. In: International Conference on Learning Representations (2019b)"},{"key":"1215_CR30","doi-asserted-by":"crossref","unstructured":"Liu, J., Zhao, G., Fei, Y., et\u00a0al.: Align, attend and locate: Chest x-ray diagnosis via contrast induced attention network with limited supervision. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 10632\u201310641 (2019c)","DOI":"10.1109\/ICCV.2019.01073"},{"key":"1215_CR31","doi-asserted-by":"publisher","unstructured":"Loza\u00a0Mencia, E., Furnkranz, J .: Pairwise learning of multilabel classifications with perceptrons. In: 2008 IEEE International Joint Conference on Neural Networks (IEEE World Congress on Computational Intelligence), pp. 2899\u20132906 (2008). https:\/\/doi.org\/10.1109\/IJCNN.2008.4634206","DOI":"10.1109\/IJCNN.2008.4634206"},{"issue":"8","key":"1215_CR32","doi-asserted-by":"publisher","first-page":"1971","DOI":"10.1109\/TMM.2019.2894964","volume":"21","author":"F Lyu","year":"2019","unstructured":"Lyu, F., Wu, Q., Hu, F., et al.: Attend and imagine: multi-label image classification with visual attention and recurrent neural networks. IEEE Trans. Multimedia 21(8), 1971\u20131981 (2019). https:\/\/doi.org\/10.1109\/TMM.2019.2894964","journal-title":"IEEE Trans. Multimedia"},{"key":"1215_CR33","doi-asserted-by":"crossref","unstructured":"Ma, C., Wang, H., Hoi, SC.: Multi-label thoracic disease image classification with cross-attention networks. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 730\u2013738 (2019a)","DOI":"10.1007\/978-3-030-32226-7_81"},{"key":"1215_CR34","doi-asserted-by":"publisher","first-page":"730","DOI":"10.1007\/978-3-030-32226-7\\_81","volume":"2019","author":"C Ma","year":"2019","unstructured":"Ma, C., Wang, H., Hoi, S.C.H.: Multi-label thoracic disease image classification with cross-attention networks. Med. Image Comput. Comput. Assist. Interv. MICCAI 2019, 730\u2013738 (2019). https:\/\/doi.org\/10.1007\/978-3-030-32226-7_81","journal-title":"Med. Image Comput. Comput. Assist. Interv. MICCAI"},{"key":"1215_CR35","doi-asserted-by":"publisher","unstructured":"Miranda, E., Aryuni, M., Irwansyah, E.: A survey of medical image classification techniques. In: 2016 International Conference on Information Management and Technology (ICIMTech), pp. 56\u201361 (2016). https:\/\/doi.org\/10.1109\/ICIMTech.2016.7930302","DOI":"10.1109\/ICIMTech.2016.7930302"},{"key":"1215_CR36","doi-asserted-by":"publisher","unstructured":"Oliveira, H., dos Santos, J.: Deep transfer learning for segmentation of anatomical structures in chest radiographs. In: 2018 31st SIBGRAPI Conference on Graphics, Patterns and Images (SIBGRAPI), pp. 204\u2013211 (2018). https:\/\/doi.org\/10.1109\/SIBGRAPI.2018.00033","DOI":"10.1109\/SIBGRAPI.2018.00033"},{"issue":"6","key":"1215_CR37","doi-asserted-by":"publisher","first-page":"1118","DOI":"10.1109\/TEVC.2021.3083315","volume":"25","author":"D O\u2019Neill","year":"2021","unstructured":"O\u2019Neill, D., Xue, B., Zhang, M.: Evolutionary neural architecture search for high-dimensional skip-connection structures on densenet style networks. IEEE Trans. Evol. Comput. 25(6), 1118\u20131132 (2021). https:\/\/doi.org\/10.1109\/TEVC.2021.3083315","journal-title":"IEEE Trans. Evol. Comput."},{"key":"1215_CR38","doi-asserted-by":"crossref","unstructured":"Pauletto, L., Amini, M.R., Babbar, R., et\u00a0al.: Neural Architecture Search for extreme multi-label classification: an evolutionary approach. In: The Fourth International Workshop on Automation in Machine Learning (AutoML 2020) (2020)","DOI":"10.1007\/978-3-030-63836-8_24"},{"key":"1215_CR39","unstructured":"Pham, H., Guan, M., Zoph, B., et\u00a0al.: Efficient neural architecture search via parameters sharing. In: International Conference on Machine Learning, pp. 4095\u20134104 (2018)"},{"key":"1215_CR40","unstructured":"Real, E., Moore, S., Selle, A., et\u00a0al.: Large-scale evolution of image classifiers. In: International Conference on Machine Learning, pp. 2902\u20132911 (2017)"},{"issue":"01","key":"1215_CR41","doi-asserted-by":"publisher","first-page":"4780","DOI":"10.1609\/aaai.v33i01.33014780","volume":"33","author":"E Real","year":"2019","unstructured":"Real, E., Aggarwal, A., Huang, Y., et al.: Regularized evolution for image classifier architecture search. Proc. AAAI Conf. Artif. Intell. 33(01), 4780\u20134789 (2019). https:\/\/doi.org\/10.1609\/aaai.v33i01.33014780","journal-title":"Proc. AAAI Conf. Artif. Intell."},{"issue":"9773","key":"1215_CR42","doi-asserted-by":"publisher","first-page":"1264","DOI":"10.1016\/S0140-6736(10)61459-6","volume":"377","author":"O Ruuskanen","year":"2011","unstructured":"Ruuskanen, O., Lahti, E., Jennings, L.C., et al.: Viral pneumonia. Lancet 377(9773), 1264\u20131275 (2011). https:\/\/doi.org\/10.1016\/S0140-6736(10)61459-6","journal-title":"Lancet"},{"key":"1215_CR43","doi-asserted-by":"publisher","unstructured":"Shen, Y., Gao, M.: Dynamic routing on deep neural network for thoracic disease classification and sensitive area localization. In: Machine Learning in Medical Imaging, pp. 389\u2013397 (2018). https:\/\/doi.org\/10.1007\/978-3-030-00919-9_45","DOI":"10.1007\/978-3-030-00919-9_45"},{"issue":"1","key":"1215_CR44","doi-asserted-by":"publisher","first-page":"199","DOI":"10.1148\/radiol.2019182465","volume":"294","author":"Y Sim","year":"2020","unstructured":"Sim, Y., Chung, M.J., Kotter, E., et al.: Deep convolutional neural network-based software improves radiologist detection of malignant lung nodules on chest radiographs. Radiology 294(1), 199\u2013209 (2020). https:\/\/doi.org\/10.1148\/radiol.2019182465","journal-title":"Radiology"},{"key":"1215_CR45","unstructured":"Tai, Y.: A deep learning based workflow for detection of lung nodules with chest radiograph (2021). arXiv preprint arXiv:2112.10184"},{"key":"1215_CR46","doi-asserted-by":"publisher","first-page":"249","DOI":"10.1007\/978-3-030-00919-9_29","volume-title":"Machine Learning in Medical Imaging","author":"Y Tang","year":"2018","unstructured":"Tang, Y., Wang, X., Harrison, A.P., et al.: Attention-guided curriculum learning for weakly supervised classification and localization of thoracic diseases on chest radiographs. In: Shi, Y., Suk, H.I., Liu, M. (eds.) Machine Learning in Medical Imaging, pp. 249\u2013258. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-00919-9_29"},{"key":"1215_CR47","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2021.107965","volume":"118","author":"AN Tarekegn","year":"2021","unstructured":"Tarekegn, A.N., Giacobini, M., Michalak, K.: A review of methods for imbalanced multi-label classification. Pattern Recogn. 118, 107965 (2021). https:\/\/doi.org\/10.1016\/j.patcog.2021.107965","journal-title":"Pattern Recogn."},{"key":"1215_CR48","unstructured":"Targ, S., Almeida, D., Lyman, K.: Resnet in resnet: generalizing residual architectures (2016). arXiv preprint arXiv:1603.08029"},{"key":"1215_CR49","first-page":"406","volume":"2007","author":"G Tsoumakas","year":"2007","unstructured":"Tsoumakas, G., Vlahavas, I.: Random k-labelsets: an ensemble method for multilabel classification. Mach. Learn. ECML 2007, 406\u2013417 (2007)","journal-title":"Mach. Learn. ECML"},{"key":"1215_CR50","doi-asserted-by":"crossref","unstructured":"Wang, J., Yang, Y., Mao, J., et\u00a0al.: Cnn-rnn: a unified framework for multi-label image classification. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2016)","DOI":"10.1109\/CVPR.2016.251"},{"key":"1215_CR51","doi-asserted-by":"crossref","unstructured":"Wang, X., Peng, Y., Lu, L., et\u00a0al.: Chestx-ray8: hospital-scale chest X-ray database and benchmarks on weakly-supervised classification and localization of common thorax diseases. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2097\u20132106 (2017)","DOI":"10.1109\/CVPR.2017.369"},{"key":"1215_CR52","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-32606-7_3","volume-title":"Medical image classification using deep learning","author":"W Wang","year":"2020","unstructured":"Wang, W., Liang, D., Chen, Q., et al.: Medical image classification using deep learning. Healthc. Paradigms Appl, Deep Learn (2020). https:\/\/doi.org\/10.1007\/978-3-030-32606-7_3"},{"key":"1215_CR53","doi-asserted-by":"crossref","unstructured":"Xie, L., Yuille, A.: Genetic cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1379\u20131388 (2017)","DOI":"10.1109\/ICCV.2017.154"},{"key":"1215_CR54","unstructured":"Xu, Y., Xie, L., Zhang, X., et\u00a0al.: Pc-darts: Partial channel connections for memory-efficient architecture search. In: International Conference on Learning Representations (2020)"},{"key":"1215_CR55","doi-asserted-by":"crossref","unstructured":"Yang, Z., Wang, Y., Chen, X., et\u00a0al.: Cars: continuous evolution for efficient neural architecture search. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2020)","DOI":"10.1109\/CVPR42600.2020.00190"},{"key":"1215_CR56","doi-asserted-by":"publisher","DOI":"10.1007\/s11227-023-05541-4","author":"Y Yang","year":"2023","unstructured":"Yang, Y., Wei, J., Yu, Z., et al.: A trustworthy neural architecture search framework for pneumonia image classification utilizing blockchain technology. J. Supercomput. (2023). https:\/\/doi.org\/10.1007\/s11227-023-05541-4","journal-title":"J. Supercomput."},{"key":"1215_CR57","unstructured":"Yao, L., Prosky, J., Poblenz, E., et\u00a0al.: Weakly supervised medical diagnosis and localization from multiple resolutions (2018). arXiv preprint arXiv:1803.07703"},{"key":"1215_CR58","doi-asserted-by":"publisher","unstructured":"Zhang, ML., Zhang, K.: Multi-label learning by exploiting label dependency. In: Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining\u2014KDD \u201910. Association for Computing Machinery, New York, NY, USA, KDD \u201910, pp. 999\u20131008 (2010). https:\/\/doi.org\/10.1145\/1835804.1835930","DOI":"10.1145\/1835804.1835930"},{"key":"1215_CR59","doi-asserted-by":"publisher","unstructured":"Zhang, M.L., Zhou, Z.H.: A k-nearest neighbor based algorithm for multi-label classification. In: 2005 IEEE International Conference on Granular Computing, vol. 2, pp. 718\u2013721 (2005). https:\/\/doi.org\/10.1109\/GRC.2005.1547385","DOI":"10.1109\/GRC.2005.1547385"},{"issue":"8","key":"1215_CR60","doi-asserted-by":"publisher","first-page":"1819","DOI":"10.1109\/TKDE.2013.39","volume":"26","author":"ML Zhang","year":"2014","unstructured":"Zhang, M.L., Zhou, Z.H.: A review on multi-label learning algorithms. IEEE Trans. Knowl. Data Eng. 26(8), 1819\u20131837 (2014). https:\/\/doi.org\/10.1109\/TKDE.2013.39","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"1215_CR61","doi-asserted-by":"crossref","unstructured":"Zhang, X., Hou, P., Zhang, X., et\u00a0al.: Neural architecture search with random labels. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 10907\u201310916 (2021)","DOI":"10.1109\/CVPR46437.2021.01076"},{"key":"1215_CR62","unstructured":"Zoph, B., Le, Q.V.: Neural architecture search with reinforcement learning (2016). arXiv preprint arXiv:1611.01578"},{"key":"1215_CR63","doi-asserted-by":"crossref","unstructured":"Zoph, B., Vasudevan, V., Shlens, J., et\u00a0al.: Learning transferable architectures for scalable image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 8697\u20138710 (2018)","DOI":"10.1109\/CVPR.2018.00907"}],"container-title":["Multimedia Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00530-023-01215-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00530-023-01215-6\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00530-023-01215-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,11,8]],"date-time":"2024-11-08T00:18:32Z","timestamp":1731025112000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00530-023-01215-6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,1,13]]},"references-count":63,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2024,2]]}},"alternative-id":["1215"],"URL":"https:\/\/doi.org\/10.1007\/s00530-023-01215-6","relation":{},"ISSN":["0942-4962","1432-1882"],"issn-type":[{"value":"0942-4962","type":"print"},{"value":"1432-1882","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,1,13]]},"assertion":[{"value":"7 August 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"8 December 2023","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"13 January 2024","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare that they have no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"This work does not involve any work related to ethics.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval"}},{"value":"All authors consent to publication.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent to publish"}}],"article-number":"8"}}