{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,23]],"date-time":"2025-12-23T15:40:27Z","timestamp":1766504427610,"version":"3.37.3"},"reference-count":26,"publisher":"Springer Science and Business Media LLC","issue":"3","license":[{"start":{"date-parts":[[2024,4,16]],"date-time":"2024-04-16T00:00:00Z","timestamp":1713225600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,4,16]],"date-time":"2024-04-16T00:00:00Z","timestamp":1713225600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["61866037"],"award-info":[{"award-number":["61866037"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Int J Intell Robot Appl"],"published-print":{"date-parts":[[2024,9]]},"DOI":"10.1007\/s41315-024-00337-y","type":"journal-article","created":{"date-parts":[[2024,4,16]],"date-time":"2024-04-16T09:01:56Z","timestamp":1713258116000},"page":"609-618","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Skin lesion image segmentation based on improved U-shaped network"],"prefix":"10.1007","volume":"8","author":[{"given":"Yuhang","family":"Zhao","sequence":"first","affiliation":[]},{"given":"Tianxing","family":"Yan","sequence":"additional","affiliation":[]},{"given":"Yaermaimaiti","family":"Yilihamu","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,4,16]]},"reference":[{"key":"337_CR1","doi-asserted-by":"publisher","first-page":"78","DOI":"10.1016\/j.patcog.2018.08.001","volume":"85","author":"L Bi","year":"2019","unstructured":"Bi, L., Kim, J., Ahn, E., et al.: Step-wise integration of deep class-specific learning for dermoscopic image segmentation. Pattern Recogn. 85, 78\u201389 (2019)","journal-title":"Pattern Recogn."},{"key":"337_CR2","first-page":"833","volume-title":"Computer Vision-ECCV 2018. Lecture Notes in Computer Science","author":"LC Chen","year":"2018","unstructured":"Chen, L.C., Zhu, Y.K., Papandreou, G., et al.: Encoder\u2013decoder with atrous separable convolution for semantic image segmentation. In: Ferrari, V., Hebert, M., Sminchisescu, C., et al. (eds.) Computer Vision-ECCV 2018. Lecture Notes in Computer Science, pp. 833\u2013851. Springer, Cham (2018)"},{"key":"337_CR3","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2023.109728","volume":"142","author":"G Chen","year":"2022","unstructured":"Chen, G., et al.: Rethinking the unpretentious U-net for medical ultrasound image segmentation. Pattern Recognit. 142, 109728 (2022)","journal-title":"Pattern Recognit."},{"issue":"5","key":"337_CR4","doi-asserted-by":"publisher","first-page":"1289","DOI":"10.1109\/TMI.2022.3226268","volume":"42","author":"G Chen","year":"2023","unstructured":"Chen, G., Li, L., Dai, Y., Zhang, J., Yap, M.H.: AAU-Net: an adaptive attention U-Net for breast lesions segmentation in ultrasound images. IEEE Trans. Med. Imaging 42(5), 1289\u20131300 (2023). https:\/\/doi.org\/10.1109\/TMI.2022.3226268","journal-title":"IEEE Trans. Med. Imaging"},{"key":"337_CR5","doi-asserted-by":"crossref","unstructured":"Codella, N.C.F., Gutman, D., Celebi, M.E., et al.: Skin lesion analysis toward melanoma detection: a challenge at the 2017 International symposium on biomedical imaging (ISBI), hosted by the international skin imaging collaboration (ISIC). In: 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018), pp. 168\u2013172 (2018)","DOI":"10.1109\/ISBI.2018.8363547"},{"issue":"5","key":"337_CR6","doi-asserted-by":"publisher","first-page":"9963","DOI":"10.3233\/JIFS-202566","volume":"40","author":"X Ding","year":"2021","unstructured":"Ding, X., Wang, S.: Efficient unet with depth-aware gatedfusion for automatic skin lesion segmentation. J. Intell. Fuzzy Syst. 40(5), 9963\u20139975 (2021)","journal-title":"J. Intell. Fuzzy Syst."},{"key":"337_CR7","doi-asserted-by":"crossref","unstructured":"Gasparini, F., Schettini, R.: Color correction for digital photographs. In: 12th International Conference on Image Analysis and Processing. Proceedings, pp. 646\u2013651 (2003)","DOI":"10.1109\/ICIAP.2003.1234123"},{"key":"337_CR8","doi-asserted-by":"crossref","unstructured":"Ge, Z., Demyanov, S., Chakravorty, R., et al.: Skin disease recognition using deep saliency features and multimodal learning of dermoscopy and clinical images. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 250\u2013258 (2017)","DOI":"10.1007\/978-3-319-66179-7_29"},{"key":"337_CR9","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., et al.: Deep residual learning for image recognition. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition. IEEE, Las Vegas, NV, USA, pp. 770\u2013778 (2016)","DOI":"10.1109\/CVPR.2016.90"},{"key":"337_CR10","doi-asserted-by":"publisher","first-page":"134","DOI":"10.1016\/j.patcog.2018.05.014","volume":"83","author":"Z Hu","year":"2018","unstructured":"Hu, Z., Tang, J., Wang, Z., et al.: Deep learning for image-based cancer detection and diagnosis\u2014a survey. Pattern Recogn. 83, 134\u2013149 (2018)","journal-title":"Pattern Recogn."},{"key":"337_CR11","doi-asserted-by":"crossref","unstructured":"Kirillov, A., Wu, Y., He, K., et al.: Pointrend: image segmentation as rendering. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 9796\u20139805 (2020)","DOI":"10.1109\/CVPR42600.2020.00982"},{"issue":"4","key":"337_CR12","doi-asserted-by":"publisher","first-page":"835","DOI":"10.1002\/ijc.33259","volume":"148","author":"B Lauro","year":"2021","unstructured":"Lauro, B., Silvia, M., Emanuele, C., et al.: Mid-term trends and recent birth-cohort-dependent changes in incidence rates of cutaneous malignant melanoma in Italy. Int. J. Cancer 148(4), 835\u2013844 (2021)","journal-title":"Int. J. Cancer"},{"issue":"7553","key":"337_CR13","doi-asserted-by":"publisher","first-page":"436","DOI":"10.1038\/nature14539","volume":"521","author":"Y Lecun","year":"2015","unstructured":"Lecun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521(7553), 436\u2013444 (2015)","journal-title":"Nature"},{"key":"337_CR14","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2020.101716","volume":"64","author":"B Lei","year":"2020","unstructured":"Lei, B., Xia, Z., Jiang, F., et al.: Skin lesion segmentation via generative adversarial networks with dual discriminators. Med. Image Anal. 64, 101716 (2020)","journal-title":"Med. Image Anal."},{"key":"337_CR15","doi-asserted-by":"publisher","first-page":"158","DOI":"10.1016\/j.neucom.2022.07.054","volume":"506","author":"H Liu","year":"2021","unstructured":"Liu, H., Liu, F., Fan, X., et al.: Polarized self-attention: towards high-quality pixel-wise mapping. Neurocomputing 506, 158\u2013167 (2021)","journal-title":"Neurocomputing"},{"key":"337_CR16","doi-asserted-by":"publisher","first-page":"15539","DOI":"10.1109\/ACCESS.2022.3148402","volume":"10","author":"R Ramadan","year":"2022","unstructured":"Ramadan, R., Aly, S.: CU-net: a new improved multiinput color U-net model for skin lesion semantic segmentation. IEEE Access 10, 15539\u201315564 (2022)","journal-title":"IEEE Access"},{"issue":"2","key":"337_CR17","doi-asserted-by":"publisher","first-page":"799","DOI":"10.1007\/s12652-021-02933-3","volume":"13","author":"Y Ren","year":"2022","unstructured":"Ren, Y., Yu, L., Tian, S., et al.: Serial attention network for skin lesion segmentation. Ambient Intell. Human. Comput. 13(2), 799\u2013810 (2022)","journal-title":"Ambient Intell. Human. Comput."},{"key":"337_CR18","first-page":"234","volume-title":"International Conference on Medical Image Computing and Computer-Assisted Intervention","author":"O Ronneberger","year":"2015","unstructured":"Ronneberger, O., Fischer, P., Brox, T.: U-net: convolutional networks for biomedical image segmentation. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 234\u2013241. Springer, Cham (2015)"},{"key":"337_CR19","first-page":"238","volume":"15","author":"R Rout","year":"2023","unstructured":"Rout, R., Parida, P., Dash, S.: Automatic skin lesion segmentation using a hybrid deep learning network. Int. J. Comput. Inf. Syst. Ind. Manage. Appl. 15, 238\u2013249 (2023)","journal-title":"Int. J. Comput. Inf. Syst. Ind. Manage. Appl."},{"issue":"5","key":"337_CR20","doi-asserted-by":"publisher","first-page":"534","DOI":"10.1016\/j.bjoms.2020.11.008","volume":"59","author":"A Sayan","year":"2021","unstructured":"Sayan, A., Plant, R., Eccles, B., et al.: Recent advances in the management of cutaneous malignant melanoma: our case cohort. Br. J. Oral Maxillofac. Surg. 59(5), 534\u2013545 (2021)","journal-title":"Br. J. Oral Maxillofac. Surg."},{"key":"337_CR21","doi-asserted-by":"publisher","first-page":"39700","DOI":"10.1109\/ACCESS.2020.2974512","volume":"8","author":"Y Tang","year":"2020","unstructured":"Tang, Y., Fang, Z., Yuan, S., et al.: iMSCGnet: iterative multi-scale context-guided segmentation of skin lesion in dermoscopic images. IEEE Access 8, 39700\u201339712 (2020)","journal-title":"IEEE Access"},{"key":"337_CR22","doi-asserted-by":"publisher","first-page":"111","DOI":"10.1016\/j.compbiomed.2018.11.010","volume":"104","author":"P Tschandl","year":"2018","unstructured":"Tschandl, P., Sinz, C., Kittler, H.: Domain-specific classification-pretrained fully convolutional network encoders for skin lesion segmentation. Comput. Biol. Med. 104, 111\u2013116 (2018)","journal-title":"Comput. Biol. Med."},{"key":"337_CR23","doi-asserted-by":"crossref","unstructured":"Wang, P., Chen, P., Yuan, Y., et al.: Understanding convolution for semantic segmentation. In: Proceedings of 2018 IEEE Winter Conference on Applications of Computer Vision (WACV). Lake Tahoe, pp. 1451\u20131460 (2018)","DOI":"10.1109\/WACV.2018.00163"},{"key":"337_CR24","doi-asserted-by":"publisher","first-page":"102","DOI":"10.1016\/j.media.2021.102327","volume":"76","author":"H Wu","year":"2022","unstructured":"Wu, H., Chen, S., Chen, G., et al.: FAT-Net: feature adaptive transformers for automated skin lesion segmentation. Med. Image Anal. 76, 102\u2013115 (2022)","journal-title":"Med. Image Anal."},{"issue":"11","key":"337_CR25","doi-asserted-by":"publisher","first-page":"3500","DOI":"10.16208\/j.issn1000-7024.2018.11.035","volume":"33","author":"G Yang","year":"2018","unstructured":"Yang, G., Hong, Z., Wang, Z., et al.: The skin lesions of image segmentation based on improved the convolution network. Comput. Eng. Des. 33(11), 3500\u20133505 (2018). https:\/\/doi.org\/10.16208\/j.issn1000-7024.2018.11.035","journal-title":"Comput. Eng. Des."},{"issue":"2","key":"337_CR26","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s11416-021-00378-y","volume":"17","author":"Y Zhang","year":"2021","unstructured":"Zhang, Y., Li, H., Zheng, Y., et al.: Enhanced DNNs for malware classification with GAN-based adversarial training. J. Comput. Virol. Hack. Techn. 17(2), 1\u201311 (2021)","journal-title":"J. Comput. Virol. Hack. Techn."}],"container-title":["International Journal of Intelligent Robotics and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s41315-024-00337-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s41315-024-00337-y\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s41315-024-00337-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,9,9]],"date-time":"2024-09-09T02:27:57Z","timestamp":1725848877000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s41315-024-00337-y"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,4,16]]},"references-count":26,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2024,9]]}},"alternative-id":["337"],"URL":"https:\/\/doi.org\/10.1007\/s41315-024-00337-y","relation":{},"ISSN":["2366-5971","2366-598X"],"issn-type":[{"type":"print","value":"2366-5971"},{"type":"electronic","value":"2366-598X"}],"subject":[],"published":{"date-parts":[[2024,4,16]]},"assertion":[{"value":"10 May 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"21 March 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"16 April 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 known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}