{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,19]],"date-time":"2026-01-19T08:47:02Z","timestamp":1768812422377,"version":"3.49.0"},"publisher-location":"Cham","reference-count":27,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783030009335","type":"print"},{"value":"9783030009342","type":"electronic"}],"license":[{"start":{"date-parts":[[2018,1,1]],"date-time":"2018-01-01T00:00:00Z","timestamp":1514764800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2018,1,1]],"date-time":"2018-01-01T00:00:00Z","timestamp":1514764800000},"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":[[2018]]},"DOI":"10.1007\/978-3-030-00934-2_3","type":"book-chapter","created":{"date-parts":[[2018,9,12]],"date-time":"2018-09-12T23:24:33Z","timestamp":1536794673000},"page":"21-29","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":99,"title":["SLSDeep: Skin Lesion Segmentation Based on Dilated Residual and Pyramid Pooling Networks"],"prefix":"10.1007","author":[{"given":"Md. Mostafa Kamal","family":"Sarker","sequence":"first","affiliation":[]},{"given":"Hatem A.","family":"Rashwan","sequence":"additional","affiliation":[]},{"given":"Farhan","family":"Akram","sequence":"additional","affiliation":[]},{"given":"Syeda Furruka","family":"Banu","sequence":"additional","affiliation":[]},{"given":"Adel","family":"Saleh","sequence":"additional","affiliation":[]},{"given":"Vivek Kumar","family":"Singh","sequence":"additional","affiliation":[]},{"given":"Forhad U. H.","family":"Chowdhury","sequence":"additional","affiliation":[]},{"given":"Saddam","family":"Abdulwahab","sequence":"additional","affiliation":[]},{"given":"Santiago","family":"Romani","sequence":"additional","affiliation":[]},{"given":"Petia","family":"Radeva","sequence":"additional","affiliation":[]},{"given":"Domenec","family":"Puig","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2018,9,26]]},"reference":[{"issue":"1","key":"3_CR1","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s11263-010-0390-2","volume":"92","author":"S Baker","year":"2011","unstructured":"Baker, S., Scharstein, D., Lewis, J., Roth, S., Black, M.J., Szeliski, R.: A database and evaluation methodology for optical flow. IJCV 92(1), 1\u201331 (2011)","journal-title":"IJCV"},{"key":"3_CR2","unstructured":"Berseth, M.: ISIC 2017-skin lesion analysis towards melanoma detection. arXiv preprint arXiv:1703.00523 (2017)"},{"key":"3_CR3","unstructured":"Bi, L., Kim, J., Ahn, E., Feng, D.: Automatic skin lesion analysis using large-scale dermoscopy images and deep residual networks. preprint arXiv:1703.04197 (2017)"},{"key":"3_CR4","doi-asserted-by":"publisher","first-page":"97","DOI":"10.1201\/b19107-5","volume-title":"Dermoscopy Image Analysis","author":"M Celebi","year":"2015","unstructured":"Celebi, M.E., Wen, Q., Iyatomi, H., Shimizu, K., Zhou, H., Schaefer, G.: A state-of-the-art survey on lesion border detection in dermoscopy images. In: Dermoscopy Image Analysis, pp. 97\u2013129 (2015)"},{"key":"3_CR5","unstructured":"Chen, L.C., Papandreou, G., Kokkinos, I., Murphy, K., Yuille, A.L.: DeepLab: semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected CRFs. arXiv preprint arXiv:1606.00915 (2016)"},{"key":"3_CR6","doi-asserted-by":"crossref","unstructured":"Codella, N.C., 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). arXiv preprint arXiv:1710.05006 (2017)","DOI":"10.1109\/ISBI.2018.8363547"},{"issue":"1","key":"3_CR7","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1034\/j.1600-0846.2000.006001001.x","volume":"6","author":"GR Day","year":"2000","unstructured":"Day, G.R., Barbour, R.H.: Automated melanoma diagnosis: where are we at? Skin Res. Technol. 6(1), 1\u20135 (2000)","journal-title":"Skin Res. Technol."},{"key":"3_CR8","unstructured":"Gutman, D., et al.: Skin lesion analysis toward melanoma detection: a challenge at the international symposium on biomedical imaging (ISBI) 2016, hosted by the international skin imaging collaboration (ISIC). arXiv preprint arXiv:1605.01397 (2016)"},{"issue":"9","key":"3_CR9","doi-asserted-by":"publisher","first-page":"1904","DOI":"10.1109\/TPAMI.2015.2389824","volume":"37","author":"K He","year":"2015","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Spatial pyramid pooling in deep convolutional networks for visual recognition. IEEE Trans. PAMI 37(9), 1904\u20131916 (2015)","journal-title":"IEEE Trans. PAMI"},{"key":"3_CR10","unstructured":"Ioffe, S., Szegedy, C.: Batch normalization: accelerating deep network training by reducing internal covariate shift. In: ICML, pp. 448\u2013456 (2015)"},{"issue":"1","key":"3_CR11","doi-asserted-by":"publisher","first-page":"1","DOI":"10.3315\/jdcr.2014.1161","volume":"8","author":"A Kardynal","year":"2014","unstructured":"Kardynal, A., Olszewska, M.: Modern non-invasive diagnostic techniques in the detection of early cutaneous melanoma. J. Dermatol. Case Rep. 8(1), 1 (2014)","journal-title":"J. Dermatol. Case Rep."},{"key":"3_CR12","unstructured":"Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)"},{"key":"3_CR13","doi-asserted-by":"crossref","unstructured":"Lin, B.S., Michael, K., Kalra, S., Tizhoosh, H.: Skin lesion segmentation: U-nets versus clustering. arXiv preprint arXiv:1710.01248 (2017)","DOI":"10.1109\/SSCI.2017.8280804"},{"key":"3_CR14","doi-asserted-by":"crossref","unstructured":"Long, J., Shelhamer, E., Darrell, T.: Fully convolutional networks for semantic segmentation. In: Proceedings of CVPR, pp. 3431\u20133440 (2015)","DOI":"10.1109\/CVPR.2015.7298965"},{"key":"3_CR15","unstructured":"Paszke, A., Gross, S., Chintala, S., Chanan, G.: Pytorch (2017)"},{"key":"3_CR16","doi-asserted-by":"crossref","unstructured":"Rahman, M., Alpaslan, N., Bhattacharya, P.: Developing a retrieval based diagnostic aid for automated melanoma recognition of dermoscopic images. In: Applied Imagery Pattern Recognition Workshop (AIPR), pp. 1\u20137. IEEE (2016)","DOI":"10.1109\/AIPR.2016.8010594"},{"key":"3_CR17","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"234","DOI":"10.1007\/978-3-319-24574-4_28","volume-title":"Medical Image Computing and Computer-Assisted Intervention \u2013 MICCAI 2015","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.) MICCAI 2015. LNCS, vol. 9351, pp. 234\u2013241. Springer, Cham (2015). https:\/\/doi.org\/10.1007\/978-3-319-24574-4_28"},{"issue":"1","key":"3_CR18","doi-asserted-by":"publisher","first-page":"7","DOI":"10.3322\/caac.21387","volume":"67","author":"RL Siegel","year":"2017","unstructured":"Siegel, R.L., Miller, K.D., Jemal, A.: Cancer statistics, 2017. CA Cancer J. Clin. 67(1), 7\u201330 (2017). https:\/\/doi.org\/10.3322\/caac.21387","journal-title":"CA Cancer J. Clin."},{"issue":"1","key":"3_CR19","first-page":"1929","volume":"15","author":"N Srivastava","year":"2014","unstructured":"Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: a simple way to prevent neural networks from overfitting. J. Mach. Learn. Res. 15(1), 1929\u20131958 (2014)","journal-title":"J. Mach. Learn. Res."},{"key":"3_CR20","doi-asserted-by":"crossref","unstructured":"Szegedy, C., et al.: Going deeper with convolutions. In: CVPR (2015)","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"3_CR21","unstructured":"Yu, F., Koltun, V.: Multi-scale context aggregation by dilated convolutions. arXiv preprint arXiv:1511.07122 (2015)"},{"key":"3_CR22","doi-asserted-by":"crossref","unstructured":"Yu, F., Koltun, V., Funkhouser, T.: Dilated residual networks. In: Computer Vision and Pattern Recognition, vol. 1 (2017)","DOI":"10.1109\/CVPR.2017.75"},{"issue":"4","key":"3_CR23","first-page":"994","volume":"36","author":"L Yu","year":"2017","unstructured":"Yu, L., Chen, H., Dou, Q., Qin, J., Heng, P.A.: Automated melanoma recognition in dermoscopy images via very deep residual networks. TMI 36(4), 994\u20131004 (2017)","journal-title":"TMI"},{"key":"3_CR24","doi-asserted-by":"crossref","unstructured":"Yuan, Y., Chao, M., Lo, Y.C.: Automatic skin lesion segmentation with fully convolutional-deconvolutional networks. arXiv preprint arXiv:1703.05165 (2017)","DOI":"10.1109\/TMI.2017.2695227"},{"issue":"sup1","key":"3_CR25","doi-asserted-by":"publisher","first-page":"267","DOI":"10.1080\/24699322.2017.1389405","volume":"22","author":"X Zhang","year":"2017","unstructured":"Zhang, X.: Melanoma segmentation based on deep learning. Comput. Assist. Surg. 22(sup1), 267\u2013277 (2017)","journal-title":"Comput. Assist. Surg."},{"key":"3_CR26","doi-asserted-by":"crossref","unstructured":"Zhao, H., Shi, J., Qi, X., Wang, X., Jia, J.: Pyramid scene parsing network. In: IEEE Conference on CVPR, pp. 2881\u20132890 (2017)","DOI":"10.1109\/CVPR.2017.660"},{"key":"3_CR27","doi-asserted-by":"crossref","unstructured":"Zhou, B., Zhao, H., Puig, X., Fidler, S., Barriuso, A., Torralba, A.: Scene parsing through ADE20K dataset. In: Proceedings of the IEEE Conference CVPR (2017)","DOI":"10.1109\/CVPR.2017.544"}],"container-title":["Lecture Notes in Computer Science","Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2018"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-00934-2_3","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,9,13]],"date-time":"2023-09-13T00:11:35Z","timestamp":1694563895000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-00934-2_3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018]]},"ISBN":["9783030009335","9783030009342"],"references-count":27,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-00934-2_3","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2018]]},"assertion":[{"value":"26 September 2018","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"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":"Granada","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Spain","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2018","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"16 September 2018","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"20 September 2018","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"11","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"miccai2018","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/www.miccai2018.org\/en\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"This content has been made available to all.","name":"free","label":"Free to read"}]}}