{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,20]],"date-time":"2026-02-20T05:42:49Z","timestamp":1771566169062,"version":"3.50.1"},"reference-count":43,"publisher":"Springer Science and Business Media LLC","issue":"9","license":[{"start":{"date-parts":[[2024,5,2]],"date-time":"2024-05-02T00:00:00Z","timestamp":1714608000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,5,2]],"date-time":"2024-05-02T00:00:00Z","timestamp":1714608000000},"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":["Med Biol Eng Comput"],"published-print":{"date-parts":[[2024,9]]},"DOI":"10.1007\/s11517-024-03098-9","type":"journal-article","created":{"date-parts":[[2024,5,2]],"date-time":"2024-05-02T01:01:25Z","timestamp":1714611685000},"page":"2717-2735","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Adversarial attacks and adversarial training for burn image segmentation based on deep learning"],"prefix":"10.1007","volume":"62","author":[{"given":"Luying","family":"Chen","sequence":"first","affiliation":[]},{"given":"Jiakai","family":"Liang","sequence":"additional","affiliation":[]},{"given":"Chao","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Keqiang","family":"Yue","sequence":"additional","affiliation":[]},{"given":"Wenjun","family":"Li","sequence":"additional","affiliation":[]},{"given":"Zhihui","family":"Fu","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,5,2]]},"reference":[{"key":"3098_CR1","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2022.116815","volume":"198","author":"S Kaviani","year":"2022","unstructured":"Kaviani S, Han KJ, Sohn I (2022) Adversarial attacks and defenses on ai in medical imaging informatics: A survey. Expert Syst Appl 198:116815","journal-title":"Expert Syst Appl"},{"key":"3098_CR2","doi-asserted-by":"crossref","unstructured":"Esteva A, Kuprel B, Novoa RA, Ko J, Swetter SM, Blau HM, Thrun S (2017) Dermatologist-level classification of skin cancer with deep neural networks. nature 542(7639):115\u2013118","DOI":"10.1038\/nature21056"},{"key":"3098_CR3","doi-asserted-by":"crossref","unstructured":"Kermany DS, Goldbaum M, Cai W, Valentim CC, Liang H, Baxter SL, McKeown A, Yang G, Wu X, Yan F, et al (2018) Identifying medical diagnoses and treatable diseases by image-based deep learning. cell 172(5):1122\u20131131","DOI":"10.1016\/j.cell.2018.02.010"},{"issue":"3","key":"3098_CR4","doi-asserted-by":"publisher","first-page":"1218","DOI":"10.1002\/mp.13349","volume":"46","author":"Y Qin","year":"2019","unstructured":"Qin Y, Zheng H, Huang X, Yang J, Zhu YM (2019) Pulmonary nodule segmentation with ct sample synthesis using adversarial networks. Med Phys 46(3):1218\u20131229","journal-title":"Med Phys"},{"issue":"6","key":"3098_CR5","doi-asserted-by":"publisher","first-page":"271","DOI":"10.1016\/S2589-7500(19)30123-2","volume":"1","author":"X Liu","year":"2019","unstructured":"Liu X, Faes L, Kale AU, Wagner SK, Fu DJ, Bruynseels A, Mahendiran T, Moraes G, Shamdas M, Kern C et al (2019) A comparison of deep learning performance against health-care professionals in detecting diseases from medical imaging: a systematic review and meta-analysis. The lancet digital health 1(6):271\u2013297","journal-title":"The lancet digital health"},{"issue":"6","key":"3098_CR6","doi-asserted-by":"publisher","first-page":"491","DOI":"10.1097\/BCR.0000000000000031","volume":"35","author":"TR Resch","year":"2014","unstructured":"Resch TR, Drake RM, Helmer SD, Jost GD, Osland JS (2014) Estimation of burn depth at burn centers in the united states: a survey. Journal of Burn Care & Research 35(6):491\u2013497","journal-title":"Journal of Burn Care & Research"},{"issue":"2","key":"3098_CR7","doi-asserted-by":"publisher","first-page":"154","DOI":"10.1016\/S0305-4179(00)00079-6","volume":"27","author":"A Watts","year":"2001","unstructured":"Watts A, Tyler M, Perry M, Roberts A, McGrouther D (2001) Burn depth and its histological measurement. Burns 27(2):154\u2013160","journal-title":"Burns"},{"key":"3098_CR8","doi-asserted-by":"publisher","DOI":"10.1016\/j.artmed.2021.102128","volume":"118","author":"B Zhang","year":"2021","unstructured":"Zhang B, Zhou J (2021) Multi-feature representation for burn depth classification via burn images. Artif Intell Med 118:102128","journal-title":"Artif Intell Med"},{"key":"3098_CR9","unstructured":"Szegedy C, Zaremba W, Sutskever I, Bruna J, Erhan D, Goodfellow I, Fergus R (2013) Intriguing properties of neural networks. arXiv:1312.6199"},{"key":"3098_CR10","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2020.107332","volume":"110","author":"X Ma","year":"2021","unstructured":"Ma X, Niu Y, Gu L, Wang Y, Zhao Y, Bailey J, Lu F (2021) Understanding adversarial attacks on deep learning based medical image analysis systems. Pattern Recogn 110:107332","journal-title":"Pattern Recogn"},{"key":"3098_CR11","doi-asserted-by":"crossref","unstructured":"Li X, Pan D, Zhu D (2021) Defending against adversarial attacks on medical imaging ai system, classification or detection? In: 2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI), pp. 1677\u20131681. IEEE","DOI":"10.1109\/ISBI48211.2021.9433761"},{"key":"3098_CR12","unstructured":"Goodfellow IJ, Shlens J, Szegedy C (2014) Explaining and harnessing adversarial examples. arXiv:1412.6572"},{"key":"3098_CR13","unstructured":"Madry A, Makelov A, Schmidt L, Tsipras D, Vladu A (2017) Towards deep learning models resistant to adversarial attacks. arXiv:1706.06083"},{"key":"3098_CR14","doi-asserted-by":"crossref","unstructured":"Carlini N, Wagner D (2017) Towards evaluating the robustness of neural networks. In: 2017 Ieee Symposium on Security and Privacy (sp), pp. 39\u201357. Ieee","DOI":"10.1109\/SP.2017.49"},{"key":"3098_CR15","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2019.101539","volume":"58","author":"L Chen","year":"2019","unstructured":"Chen L, Bentley P, Mori K, Misawa K, Fujiwara M, Rueckert D (2019) Self-supervised learning for medical image analysis using image context restoration. Med Image Anal 58:101539","journal-title":"Med Image Anal"},{"key":"3098_CR16","unstructured":"Kingma DP, Welling M (2013) Auto-encoding variational bayes. arXiv:1312.6114"},{"issue":"11","key":"3098_CR17","doi-asserted-by":"publisher","first-page":"139","DOI":"10.1145\/3422622","volume":"63","author":"I Goodfellow","year":"2020","unstructured":"Goodfellow I, Pouget-Abadie J, Mirza M, Xu B, Warde-Farley D, Ozair S, Courville A, Bengio Y (2020) Generative adversarial networks. Commun ACM 63(11):139\u2013144","journal-title":"Commun ACM"},{"key":"3098_CR18","unstructured":"Wei H, Tang H, Jia X, Wang Z, Yu H, Li Z, Satoh S, Van Gool L, Wang Z (2022) Physical adversarial attack meets computer vision: A decade survey. arXiv:2209.15179"},{"key":"3098_CR19","doi-asserted-by":"crossref","unstructured":"Bai T, Luo J, Zhao J, Wen B, Wang Q (2021) Recent advances in adversarial training for adversarial robustness. arXiv:2102.01356","DOI":"10.24963\/ijcai.2021\/591"},{"key":"3098_CR20","doi-asserted-by":"publisher","first-page":"582","DOI":"10.1007\/s10278-019-00227-x","volume":"32","author":"MH Hesamian","year":"2019","unstructured":"Hesamian MH, Jia W, He X, Kennedy P (2019) Deep learning techniques for medical image segmentation: achievements and challenges. J Digit Imaging 32:582\u2013596","journal-title":"J Digit Imaging"},{"key":"3098_CR21","doi-asserted-by":"publisher","first-page":"10523","DOI":"10.1609\/aaai.v35i12.17259","volume":"35","author":"Q Xu","year":"2021","unstructured":"Xu Q, Tao G, Cheng S, Zhang X (2021) Towards feature space adversarial attack by style perturbation. Proceedings of the AAAI Conference on Artificial Intelligence 35:10523\u201310531","journal-title":"Proceedings of the AAAI Conference on Artificial Intelligence"},{"key":"3098_CR22","doi-asserted-by":"publisher","first-page":"149","DOI":"10.1609\/aaai.v36i1.19889","volume":"36","author":"Z Cai","year":"2022","unstructured":"Cai Z, Xie X, Li S, Yin M, Song C, Krishnamurthy SV, Roy-Chowdhury AK, Asif MS (2022) Context-aware transfer attacks for object detection. Proceedings of the AAAI Conference on Artificial Intelligence 36:149\u2013157","journal-title":"Proceedings of the AAAI Conference on Artificial Intelligence"},{"key":"3098_CR23","doi-asserted-by":"crossref","unstructured":"Tan J, Ji N, Xie H, Xiang X (2021) Legitimate adversarial patches: Evading human eyes and detection models in the physical world. In:Proceedings of the 29th ACM International Conference on Multimedia, pp. 5307\u20135315","DOI":"10.1145\/3474085.3475653"},{"key":"3098_CR24","doi-asserted-by":"crossref","unstructured":"Komkov S, Petiushko A (2021) Advhat: Real-world adversarial attack on arcface face id system. In: 2020 25th International Conference on Pattern Recognition (ICPR), pp. 819\u2013826. IEEE","DOI":"10.1109\/ICPR48806.2021.9412236"},{"key":"3098_CR25","doi-asserted-by":"crossref","unstructured":"Zhao Z, Liu Z, Larson M (2020) Towards large yet imperceptible adversarial image perturbations with perceptual color distance. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 1039\u20131048","DOI":"10.1109\/CVPR42600.2020.00112"},{"key":"3098_CR26","unstructured":"Madry A, Makelov A, Schmidt L, Tsipras D, Vladu A (2017) Towards deep learning models resistant to adversarial attacks. arXiv:1706.06083"},{"key":"3098_CR27","unstructured":"Li Y, Fang EX, Xu H, Zhao T (2020) International conference on learning representations 2020. In: International Conference on Learning Representations 2020"},{"key":"3098_CR28","doi-asserted-by":"crossref","unstructured":"Bai T, Luo J, Zhao J, Wen B, Wang Q (2021) Recent advances in adversarial training for adversarial robustness. arXiv:2102.01356","DOI":"10.24963\/ijcai.2021\/591"},{"key":"3098_CR29","doi-asserted-by":"publisher","first-page":"10691","DOI":"10.1609\/aaai.v35i12.17278","volume":"35","author":"N Ye","year":"2021","unstructured":"Ye N, Li Q, Zhou XY, Zhu Z (2021) Amata: An annealing mechanism for adversarial training acceleration. Proceedings of the AAAI Conference on Artificial Intelligence 35:10691\u201310699","journal-title":"Proceedings of the AAAI Conference on Artificial Intelligence"},{"key":"3098_CR30","unstructured":"Wang Y, Ma X, Bailey J, Yi J, Zhou B, Gu Q (2021) On the convergence and robustness of adversarial training. arXiv:2112.08304"},{"key":"3098_CR31","unstructured":"Song C, He K, Lin J, Wang L, Hopcroft JE (2019) Robust local features for improving the generalization of adversarial training. arXiv:1909.10147"},{"key":"3098_CR32","first-page":"11821","volume":"34","author":"G Sriramanan","year":"2021","unstructured":"Sriramanan G, Addepalli S, Baburaj A et al (2021) Towards efficient and effective adversarial training. Adv Neural Inf Process Syst 34:11821\u201311833","journal-title":"Adv Neural Inf Process Syst"},{"key":"3098_CR33","first-page":"8270","volume":"33","author":"Y Dong","year":"2020","unstructured":"Dong Y, Deng Z, Pang T, Zhu J, Su H (2020) Adversarial distributional training for robust deep learning. Adv Neural Inf Process Syst 33:8270\u20138283","journal-title":"Adv Neural Inf Process Syst"},{"key":"3098_CR34","unstructured":"Wang Y, Ma X, Bailey J, Yi J, Zhou B, Gu Q (2021) On the convergence and robustness of adversarial training. arXiv:2112.08304"},{"key":"3098_CR35","doi-asserted-by":"crossref","unstructured":"Jia X, Zhang Y, Wu B, Ma K, Wang J, Cao X (2022) Las-at: adversarial training with learnable attack strategy. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 13398\u201313408","DOI":"10.1109\/CVPR52688.2022.01304"},{"key":"3098_CR36","doi-asserted-by":"crossref","unstructured":"Drozdzal M, Vorontsov E, Chartrand G, Kadoury S, Pal C (2016) The importance of skip connections in biomedical image segmentation. In: International Workshop on Deep Learning in Medical Image Analysis, International Workshop on Large-Scale Annotation of Biomedical Data and Expert Label Synthesis, pp. 179\u2013187. Springer","DOI":"10.1007\/978-3-319-46976-8_19"},{"key":"3098_CR37","doi-asserted-by":"crossref","unstructured":"He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770\u2013778","DOI":"10.1109\/CVPR.2016.90"},{"key":"3098_CR38","unstructured":"Wada K, et al (2016) Labelme: Image polygonal annotation with python"},{"key":"3098_CR39","doi-asserted-by":"publisher","first-page":"13001","DOI":"10.1609\/aaai.v34i07.7000","volume":"34","author":"Z Zhong","year":"2020","unstructured":"Zhong Z, Zheng L, Kang G, Li S, Yang Y (2020) Random erasing data augmentation. Proceedings of the AAAI Conference on Artificial Intelligence 34:13001\u201313008","journal-title":"Proceedings of the AAAI Conference on Artificial Intelligence"},{"issue":"3","key":"3098_CR40","doi-asserted-by":"publisher","first-page":"234","DOI":"10.1109\/34.49051","volume":"12","author":"T Lindeberg","year":"1990","unstructured":"Lindeberg T (1990) Scale-space for discrete signals. IEEE Trans Pattern Anal Mach Intell 12(3):234\u2013254","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"3098_CR41","unstructured":"Stein EM, Shakarchi R (2010) Complex Analysis vol. 2. Princeton University Press"},{"key":"3098_CR42","unstructured":"Zhang J, Xu X, Han B, Niu G, Cui L, Sugiyama M, Kankanhalli M (2020) Attacks which do not kill training make adversarial learning stronger. In: International Conference on Machine Learning, pp. 11278\u201311287. PMLR"},{"key":"3098_CR43","doi-asserted-by":"crossref","unstructured":"Singarimbun RN, Nababan EB, Sitompul OS (2019) Adaptive moment estimation to minimize square error in backpropagation algorithm. In: 2019 International Conference of Computer Science and Information Technology (ICoSNIKOM), pp. 1\u20137 (2019). IEEE","DOI":"10.1109\/ICoSNIKOM48755.2019.9111563"}],"container-title":["Medical & Biological Engineering & Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11517-024-03098-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11517-024-03098-9\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11517-024-03098-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,8,17]],"date-time":"2024-08-17T17:11:55Z","timestamp":1723914715000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11517-024-03098-9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,5,2]]},"references-count":43,"journal-issue":{"issue":"9","published-print":{"date-parts":[[2024,9]]}},"alternative-id":["3098"],"URL":"https:\/\/doi.org\/10.1007\/s11517-024-03098-9","relation":{},"ISSN":["0140-0118","1741-0444"],"issn-type":[{"value":"0140-0118","type":"print"},{"value":"1741-0444","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,5,2]]},"assertion":[{"value":"28 January 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"14 April 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"2 May 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":"We declare that we have no conflicts of interest to this work. We declare that we do not have any commercial or associative interest that represents a conflict of interest in connection with the work submitted. The contents of this manuscript have not been copyrighted or published previously. The contents of this manuscript are not now under consideration for publication elsewhere. There are no directly related manuscript or abstracts, published or unpublished, by any authors of this manuscript.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}