{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,25]],"date-time":"2025-03-25T14:25:11Z","timestamp":1742912711302,"version":"3.40.3"},"publisher-location":"Cham","reference-count":12,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030643393"},{"type":"electronic","value":"9783030643409"}],"license":[{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021]]},"DOI":"10.1007\/978-3-030-64340-9_10","type":"book-chapter","created":{"date-parts":[[2021,7,9]],"date-time":"2021-07-09T13:02:51Z","timestamp":1625835771000},"page":"81-89","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Multi-resolution Multi-task Network and Polyp Tracking"],"prefix":"10.1007","author":[{"given":"Hanbo","family":"Chen","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2021,7,10]]},"reference":[{"key":"10_CR1","unstructured":"Chen, L.-C., Papandreou, G., Schroff, F., & Adam, H. (2017). Rethinking atrous convolution for semantic image segmentation. arXiv:1706.05587."},{"key":"10_CR2","doi-asserted-by":"crossref","unstructured":"Chu, Q., Ouyang, W., Li, H., Wang, X., Liu, B., & Yu, N. (2017). Online multi-object tracking using CNN-based single object tracker with spatial-temporal attention mechanism. In Proceedings of the IEEE International Conference on Computer Vision (pp.\u00a04836\u20134845).","DOI":"10.1109\/ICCV.2017.518"},{"key":"10_CR3","doi-asserted-by":"crossref","unstructured":"Huang, G., Liu, Z., Van Der\u00a0Maaten, L., & Weinberger, K.\u00a0Q. (2017). Densely connected convolutional networks. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (pp.\u00a04700\u20134708).","DOI":"10.1109\/CVPR.2017.243"},{"key":"10_CR4","doi-asserted-by":"crossref","unstructured":"Li, K., Wu, Z., Peng, K.-C., Ernst, J., & Fu, Y. (2018). Tell me where to look: Guided attention inference network. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (pp.\u00a09215\u20139223).","DOI":"10.1109\/CVPR.2018.00960"},{"key":"10_CR5","doi-asserted-by":"crossref","unstructured":"Liu, J., Li, W., Zhao, N., Cao, K., Yin, Y., Song, Q., et al. (2018). Integrate domain knowledge in training CNN for ultrasonography breast cancer diagnosis. In International Conference on Medical Image Computing and Computer-Assisted Intervention (pp.\u00a0868\u2013875). Springer.","DOI":"10.1007\/978-3-030-00934-2_96"},{"key":"10_CR6","unstructured":"Lucas, B.\u00a0D., & Kanade, T. (1981). An iterative image registration technique with an application to stereo vision. In Proceedings DARPA Image Understanding (p. 121430)."},{"key":"10_CR7","doi-asserted-by":"crossref","unstructured":"Merkow, J., Marsden, A., Kriegman, D., & Tu, Z. (2016). Dense volume-to-volume vascular boundary detection. In International Conference on Medical Image Computing and Computer-Assisted Intervention (pp.\u00a0371\u2013379). Springer.","DOI":"10.1007\/978-3-319-46726-9_43"},{"key":"10_CR8","doi-asserted-by":"crossref","unstructured":"Ronneberger, O., Fischer, P., & Brox, T. (2015). U-Net: Convolutional networks for biomedical image segmentation. In International Conference on Medical Image Computing and Computer-Assisted Intervention (pp.\u00a0234\u2013241). Springer.","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"10_CR9","doi-asserted-by":"crossref","unstructured":"Xie, S., & Tu, Z. (2015). Holistically-nested edge detection. In Proceedings of the IEEE International Conference on Computer Vision (pp.\u00a01395\u20131403).","DOI":"10.1109\/ICCV.2015.164"},{"key":"10_CR10","doi-asserted-by":"crossref","unstructured":"Zhao, H., Shi, J., Qi, X., Wang, X., & Jia, J. (2017). Pyramid scene parsing network. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (pp.\u00a02881\u20132890).","DOI":"10.1109\/CVPR.2017.660"},{"key":"10_CR11","doi-asserted-by":"crossref","unstructured":"Zheng, H., Chen, H., Huang, J., Li, X., Han, X., & Yao, J. (2019). Polyp tracking in video colonoscopy using optical flow with an on-the-fly trained CNN. In 2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019) (pp.\u00a079\u201382). IEEE.","DOI":"10.1109\/ISBI.2019.8759180"},{"key":"10_CR12","doi-asserted-by":"crossref","unstructured":"Zhou, B., Khosla, A., Lapedriza, A., Oliva, A., & Torralba, A. (2016). Learning deep features for discriminative localization. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (pp.\u00a02921\u20132929).","DOI":"10.1109\/CVPR.2016.319"}],"container-title":["Computer-Aided Analysis of Gastrointestinal Videos"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-64340-9_10","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,7,9]],"date-time":"2021-07-09T13:05:29Z","timestamp":1625835929000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-64340-9_10"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"ISBN":["9783030643393","9783030643409"],"references-count":12,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-64340-9_10","relation":{},"subject":[],"published":{"date-parts":[[2021]]},"assertion":[{"value":"10 July 2021","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}}]}}