{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,8,22]],"date-time":"2025-08-22T16:10:02Z","timestamp":1755879002608,"version":"3.44.0"},"publisher-location":"New York, NY, USA","reference-count":28,"publisher":"ACM","license":[{"start":{"date-parts":[[2024,1,19]],"date-time":"2024-01-19T00:00:00Z","timestamp":1705622400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2024,1,19]]},"DOI":"10.1145\/3653804.3654718","type":"proceedings-article","created":{"date-parts":[[2024,6,1]],"date-time":"2024-06-01T12:22:26Z","timestamp":1717244546000},"page":"1-6","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["DualU-Net Mixed with Convolution and Transformers"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8204-4466","authenticated-orcid":false,"given":"Lu","family":"Shen","sequence":"first","affiliation":[{"name":"National University of Defense Technology, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1160-0365","authenticated-orcid":false,"given":"Changjian","family":"Wang","sequence":"additional","affiliation":[{"name":"National University of Defense Technology, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8171-1861","authenticated-orcid":false,"given":"Yingwen","family":"Chen","sequence":"additional","affiliation":[{"name":"National University of Defense Technology, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-2245-9520","authenticated-orcid":false,"given":"Dandan","family":"Li","sequence":"additional","affiliation":[{"name":"National University of Defense Technology, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0208-6947","authenticated-orcid":false,"given":"Yunbin","family":"Xiao","sequence":"additional","affiliation":[{"name":"Hunan Children's Hospital, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2024,6]]},"reference":[{"volume-title":"2022 IEEE International Conference on Image Processing (ICIP) (pp. 151-155)","author":"Ben-Haim T.","unstructured":"Ben-Haim, T., Sofer, R.M., Ben-Arie, G., Shelef, I. and Raviv, T.R., 2022, October. A deep ensemble learning approach to lung CT segmentation for Covid-19 severity assessment. In 2022 IEEE International Conference on Image Processing (ICIP) (pp. 151-155). IEEE.","key":"e_1_3_2_1_1_1"},{"doi-asserted-by":"crossref","unstructured":"Salama W.M. and Aly M.H. 2021 December. Lung CT Image Segmentation: A Generalized Framework Based on U-Net Architecture and Preprocessing Models. In 2021 31st International Conference on Computer Theory and Applications (ICCTA) (pp. 141-146). IEEE.","key":"e_1_3_2_1_2_1","DOI":"10.1109\/ICCTA54562.2021.9916619"},{"doi-asserted-by":"publisher","key":"e_1_3_2_1_3_1","DOI":"10.7717\/peerj-cs.349"},{"volume-title":"2020 4th International Conference on Computer, Communication and Signal Processing (ICCCSP) (pp. 1-5). IEEE.","author":"Adarsh R.","unstructured":"Adarsh, R., Amarnageswarao, G., Pandeeswari, R. and Deivalakshmi, S., 2020, September. Inception block based residual auto encoder for lung segmentation. In 2020 4th International Conference on Computer, Communication and Signal Processing (ICCCSP) (pp. 1-5). IEEE.","key":"e_1_3_2_1_4_1"},{"doi-asserted-by":"publisher","key":"e_1_3_2_1_5_1","DOI":"10.1016\/j.cag.2020.05.011"},{"doi-asserted-by":"publisher","key":"e_1_3_2_1_6_1","DOI":"10.1002\/ima.22527"},{"unstructured":"Yan Q. Wang B. Gong D. Luo C. Zhao W. Shen J. Shi Q. Jin S. Zhang L. and You Z. 2020. COVID-19 chest CT image segmentation\u2013a deep convolutional neural network solution. arXiv preprint arXiv:2004.10987.","key":"e_1_3_2_1_7_1"},{"key":"e_1_3_2_1_8_1","volume-title":"Multi-resolution convolutional neural networks for fully automated segmentation of acutely injured lungs in multiple species. Medical image analysis, 60, 101592","author":"Gerard S. E.","year":"2020","unstructured":"Gerard, S. E., Herrmann, J., Kaczka, D. W., Musch, G., Fernandez-Bustamante, A., & Reinhardt, J. M. (2020). Multi-resolution convolutional neural networks for fully automated segmentation of acutely injured lungs in multiple species. Medical image analysis, 60, 101592."},{"key":"e_1_3_2_1_9_1","first-page":"234","volume-title":"U-net: Convolutional networks for biomedical image segmentation. In Medical image computing and computer-assisted intervention\u2013MICCAI 2015: 18th international conference","author":"Ronneberger O.","year":"2015","unstructured":"Ronneberger, O., Fischer, P. and Brox, T., 2015. U-net: Convolutional networks for biomedical image segmentation. In Medical image computing and computer-assisted intervention\u2013MICCAI 2015: 18th international conference, Munich, Germany, October 5-9, 2015, proceedings, part III 18 (pp. 234-241). Springer International Publishing."},{"doi-asserted-by":"crossref","unstructured":"Zhou Z. Rahman Siddiquee M.M. Tajbakhsh N. and Liang J. 2018. Unet++: A nested u-net architecture for medical image segmentation. In Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support: 4th International Workshop DLMIA 2018 and 8th International Workshop ML-CDS 2018 Held in Conjunction with MICCAI 2018 Granada Spain September 20 2018 Proceedings 4 (pp. 3-11). Springer International Publishing.","key":"e_1_3_2_1_10_1","DOI":"10.1007\/978-3-030-00889-5_1"},{"key":"e_1_3_2_1_11_1","volume-title":"2018 9th international conference on information technology in medicine and education (ITME) (pp. 327-331)","author":"Xiao X.","year":"2018","unstructured":"Xiao, X., Lian, S., Luo, Z., & Li, S. 2018, October. Weighted res-unet for high-quality retina vessel segmentation. In 2018 9th international conference on information technology in medicine and education (ITME) (pp. 327-331). IEEE."},{"doi-asserted-by":"crossref","unstructured":"Alom M. Z. Hasan M. Yakopcic C. Taha T. M. & Asari V. K. 2018. Recurrent residual convolutional neural network based on u-net (r2u-net) for medical image segmentation. arXiv preprint arXiv:1802.06955.","key":"e_1_3_2_1_12_1","DOI":"10.1109\/NAECON.2018.8556686"},{"unstructured":"Oktay O. Schlemper J. Folgoc L. L. Lee M. Heinrich M. Misawa K. ... & Rueckert D. 2018. Attention u-net: Learning where to look for the pancreas. arXiv preprint arXiv:1804.03999.","key":"e_1_3_2_1_13_1"},{"key":"e_1_3_2_1_14_1","volume-title":"Ternausnet: U-net with vgg11 encoder pre-trained on imagenet for image segmentation. ar Xiv preprint ar Xiv:1801.05746.","author":"Iglovikov V.","year":"2018","unstructured":"Iglovikov, V. and Shvets, A., 2018. Ternausnet: U-net with vgg11 encoder pre-trained on imagenet for image segmentation. ar Xiv preprint ar Xiv:1801.05746."},{"key":"e_1_3_2_1_15_1","volume-title":"Automated design of deep learning methods for biomedical image segmentation. ar Xiv preprint ar Xiv:1904.08128","author":"Isensee F.","year":"2019","unstructured":"Isensee, F., J\u00e4ger, P. F., Kohl, S. A., Petersen, J., & Maier-Hein, K. H. (2019). Automated design of deep learning methods for biomedical image segmentation. ar Xiv preprint ar Xiv:1904.08128."},{"volume-title":"2020 IEEE 33rd International symposium on computer-based medical systems (CBMS) (pp. 558-564)","author":"Jha D.","unstructured":"Jha, D., Riegler, M.A., Johansen, D., Halvorsen, P. and Johansen, H.D., 2020, July. Doubleu-net: A deep convolutional neural network for medical image segmentation. In 2020 IEEE 33rd International symposium on computer-based medical systems (CBMS) (pp. 558-564). IEEE.","key":"e_1_3_2_1_16_1"},{"key":"e_1_3_2_1_17_1","first-page":"758","volume-title":"Medical Imaging 2021: Image Processing (Vol. 11596","author":"Lou A.","unstructured":"Lou, A., Guan, S., & Loew, M. (2021, February). DC-UNet: rethinking the U-Net architecture with dual channel efficient CNN for medical image segmentation. In Medical Imaging 2021: Image Processing (Vol. 11596, pp. 758-768). SPIE."},{"doi-asserted-by":"publisher","key":"e_1_3_2_1_18_1","DOI":"10.1109\/TMI.2021.3130469"},{"doi-asserted-by":"publisher","key":"e_1_3_2_1_19_1","DOI":"10.1016\/j.compbiomed.2022.106207"},{"unstructured":"Dinh L. Sohl-Dickstein J. and Bengio S. 2016. Density estimation using real nvp. arXiv preprint arXiv:1605.08803.","key":"e_1_3_2_1_20_1"},{"key":"e_1_3_2_1_21_1","first-page":"2441","volume-title":"Proceedings of the AAAI conference on artificial intelligence (Vol. 36","author":"Wang H.","year":"2022","unstructured":"Wang, H., Cao, P., Wang, J., & Zaiane, O. R. 2022, June. Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In Proceedings of the AAAI conference on artificial intelligence (Vol. 36, No. 3, pp. 2441-2449)."},{"volume-title":"European conference on computer vision (pp. 205-218)","author":"Cao H.","unstructured":"Cao, H., Wang, Y., Chen, J., Jiang, D., Zhang, X., Tian, Q. and Wang, M., 2022, October. Swin-unet: Unet-like pure transformer for medical image segmentation. In European conference on computer vision (pp. 205-218). Cham: Springer Nature Switzerland.","key":"e_1_3_2_1_22_1"},{"key":"e_1_3_2_1_23_1","first-page":"109","volume-title":"Transbts: Multimodal brain tumor segmentation using transformer. In International Conference on Medical Image Computing and Computer-Assisted Intervention","author":"Wenxuan W.","year":"2021","unstructured":"Wenxuan, W., Chen, C., Meng, D., Hong, Y., Sen, Z. and Jiangyun, L., 2021. Transbts: Multimodal brain tumor segmentation using transformer. In International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer (pp. 109-119)."},{"key":"e_1_3_2_1_24_1","volume-title":"Transunet: Transformers make strong encoders for medical image segmentation. arXiv preprint arXiv:2102.04306.","author":"Chen J.","year":"2021","unstructured":"Chen, J., Lu, Y., Yu, Q., Luo, X., Adeli, E., Wang, Y., ... & Zhou, Y. 2021. Transunet: Transformers make strong encoders for medical image segmentation. arXiv preprint arXiv:2102.04306."},{"doi-asserted-by":"crossref","unstructured":"Gao Y. Zhou M. and Metaxas D.N. 2021. UTNet: a hybrid transformer architecture for medical image segmentation. In Medical Image Computing and Computer Assisted Intervention\u2013MICCAI 2021: 24th International Conference Strasbourg France September 27\u2013October 1 2021 Proceedings Part III 24 (pp. 61-71). Springer International Publishing.","key":"e_1_3_2_1_25_1","DOI":"10.1007\/978-3-030-87199-4_6"},{"doi-asserted-by":"publisher","key":"e_1_3_2_1_26_1","DOI":"10.1109\/ICCV48922.2021.00986"},{"doi-asserted-by":"publisher","key":"e_1_3_2_1_27_1","DOI":"10.1109\/CVPR.2018.00745"},{"doi-asserted-by":"publisher","key":"e_1_3_2_1_28_1","DOI":"10.1016\/j.cell.2020.04.045"}],"event":{"acronym":"CVDL 2024","name":"CVDL 2024: The International Conference on Computer Vision and Deep Learning","location":"Changsha China"},"container-title":["Proceedings of the International Conference on Computer Vision and Deep Learning"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3653804.3654718","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3653804.3654718","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,8,22]],"date-time":"2025-08-22T15:28:07Z","timestamp":1755876487000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3653804.3654718"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,1,19]]},"references-count":28,"alternative-id":["10.1145\/3653804.3654718","10.1145\/3653804"],"URL":"https:\/\/doi.org\/10.1145\/3653804.3654718","relation":{},"subject":[],"published":{"date-parts":[[2024,1,19]]},"assertion":[{"value":"2024-06-01","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}