{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,8,22]],"date-time":"2025-08-22T15:40:03Z","timestamp":1755877203857,"version":"3.44.0"},"publisher-location":"New York, NY, USA","reference-count":30,"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\/3653804.3654609","type":"proceedings-article","created":{"date-parts":[[2024,6,1]],"date-time":"2024-06-01T12:22:26Z","timestamp":1717244546000},"page":"1-11","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["TransFuseNetS: A new Image Medical Segmentation Method based on TransFuse Architecture"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0009-0008-3247-4979","authenticated-orcid":false,"given":"Wei","family":"Chen","sequence":"first","affiliation":[{"name":"School of Computer and Information Engineering, Heilongjiang University of Science and Technology, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-2242-7070","authenticated-orcid":false,"given":"Zhiheng","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Computer and Information Engineering, Heilongjiang University of Science and Technology, China"}]}],"member":"320","published-online":{"date-parts":[[2024,6]]},"reference":[{"issue":"1","key":"e_1_3_2_1_1_1","first-page":"1","article-title":"A comprehensive survey to study the utilities of image segmentation methods in clinical routine[J]","volume":"13","author":"Mohapatra R K","year":"2024","unstructured":"Mohapatra R K, Jolly L, Lyngdoh D C, A comprehensive survey to study the utilities of image segmentation methods in clinical routine[J]. Network Modeling Analysis in Health Informatics and Bioinformatics, 2024, 13(1): 1-26.","journal-title":"Network Modeling Analysis in Health Informatics and Bioinformatics"},{"key":"e_1_3_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1049\/ipr2.12419"},{"key":"e_1_3_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.3390\/su13031224"},{"key":"e_1_3_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2021.102214"},{"key":"e_1_3_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.1007\/s11042-021-10515-w"},{"volume-title":"ICMTEL 2021, Virtual Event, April 8\u20139, 2021, Proceedings, Part II 3. Springer International Publishing","author":"Chen X","key":"e_1_3_2_1_6_1","unstructured":"Chen X, Zhao D, Zhong W, Research on brain image segmentation based on KFCM algorithm optimization[C]\/\/Multimedia Technology and Enhanced Learning: Third EAI International Conference, ICMTEL 2021, Virtual Event, April 8\u20139, 2021, Proceedings, Part II 3. Springer International Publishing, 2021: 278-289."},{"key":"e_1_3_2_1_7_1","volume-title":"Van Leemput K, A generative model for image segmentation based on label fusion[J]","author":"Sabuncu M R","year":"2010","unstructured":"Sabuncu M R, Yeo B T T, Van Leemput K, A generative model for image segmentation based on label fusion[J]. IEEE transactions on medical imaging, 2010, 29(10): 1714-1729."},{"key":"e_1_3_2_1_8_1","volume-title":"Medical image segmentation based on threshold SVM[C]\/\/2010 International Conference on Biomedical Engineering and Computer Science","author":"Chen X","year":"2010","unstructured":"Chen X, Li D. Medical image segmentation based on threshold SVM[C]\/\/2010 International Conference on Biomedical Engineering and Computer Science. IEEE, 2010: 1-3."},{"key":"e_1_3_2_1_9_1","doi-asserted-by":"publisher","DOI":"10.26438\/ijcse\/v7i6.523528"},{"key":"e_1_3_2_1_10_1","volume-title":"An adaptive level set method for medical image segmentation[C]\/\/Biennial International Conference on Information Processing in Medical Imaging","author":"Droske M","year":"2001","unstructured":"Droske M, Meyer B, Rumpf M, An adaptive level set method for medical image segmentation[C]\/\/Biennial International Conference on Information Processing in Medical Imaging. Berlin, Heidelberg: Springer Berlin Heidelberg, 2001: 416-422."},{"key":"e_1_3_2_1_11_1","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2017.2666042"},{"key":"e_1_3_2_1_12_1","volume-title":"MIMI 2007","author":"Chen Y T","year":"2007","unstructured":"Chen Y T, Tseng D C. Medical image segmentation based on the Bayesian level set method[C]\/\/Medical Imaging and Informatics: 2nd International Conference, MIMI 2007, Beijing, China, August 14-16, 2007 Revised Selected Papers. Springer Berlin Heidelberg, 2008: 25-34."},{"key":"e_1_3_2_1_13_1","volume-title":"Computational technique based on machine learning and image processing for medical image analysis of breast cancer diagnosis[J]. Security and communication networks","author":"Jasti V D P","year":"2022","unstructured":"Jasti V D P, Zamani A S, Arumugam K, Computational technique based on machine learning and image processing for medical image analysis of breast cancer diagnosis[J]. Security and communication networks, 2022, 2022: 1-7."},{"key":"e_1_3_2_1_14_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2023.107858"},{"key":"e_1_3_2_1_15_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.array.2019.100004"},{"key":"e_1_3_2_1_16_1","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2018.2791721"},{"key":"e_1_3_2_1_17_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2023.105818"},{"key":"e_1_3_2_1_18_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2023.105772"},{"key":"e_1_3_2_1_19_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2023.107803"},{"key":"e_1_3_2_1_20_1","doi-asserted-by":"publisher","DOI":"10.1007\/s42979-023-02422-8"},{"key":"e_1_3_2_1_21_1","doi-asserted-by":"publisher","DOI":"10.1109\/TRPMS.2018.2890359"},{"key":"e_1_3_2_1_22_1","first-page":"2022","article-title":"U-Net-Based medical image segmentation[J]","author":"Yin X X","year":"2022","unstructured":"Yin X X, Sun L, Fu Y, U-Net-Based medical image segmentation[J]. Journal of Healthcare Engineering, 2022, 2022.","journal-title":"Journal of Healthcare Engineering"},{"key":"e_1_3_2_1_23_1","volume-title":"Self-learning AI framework for skin lesion image segmentation and classification[J]. arXiv preprint arXiv:2001.05838","author":"Kamalakannan A","year":"2020","unstructured":"Kamalakannan A, Ganesan S S, Rajamanickam G. Self-learning AI framework for skin lesion image segmentation and classification[J]. arXiv preprint arXiv:2001.05838, 2020."},{"key":"e_1_3_2_1_24_1","volume-title":"MIScnn: a framework for medical image segmentation with convolutional neural networks and deep learning[J]. BMC medical imaging","author":"M\u00fcller D","year":"2021","unstructured":"M\u00fcller D, Kramer F. MIScnn: a framework for medical image segmentation with convolutional neural networks and deep learning[J]. BMC medical imaging, 2021, 21(1): 1-11."},{"key":"e_1_3_2_1_25_1","volume-title":"SMESwin Unet: Merging CNN and transformer for medical image segmentation[C]\/\/International Conference on Medical Image Computing and Computer-Assisted Intervention","author":"Wang Z","year":"2022","unstructured":"Wang Z, Min X, Shi F, SMESwin Unet: Merging CNN and transformer for medical image segmentation[C]\/\/International Conference on Medical Image Computing and Computer-Assisted Intervention. Cham: Springer Nature Switzerland, 2022: 517-526."},{"key":"e_1_3_2_1_26_1","doi-asserted-by":"publisher","DOI":"10.1142\/S0219519423401073"},{"key":"e_1_3_2_1_27_1","volume-title":"Doubleu-net: A deep convolutional neural network for medical image segmentation[C]\/\/2020 IEEE 33rd International symposium on computer-based medical systems (CBMS)","author":"Jha D","year":"2020","unstructured":"Jha D, Riegler M A, Johansen D, Doubleu-net: A deep convolutional neural network for medical image segmentation[C]\/\/2020 IEEE 33rd International symposium on computer-based medical systems (CBMS). IEEE, 2020: 558-564."},{"volume-title":"PMLR","author":"Luo X","key":"e_1_3_2_1_28_1","unstructured":"Luo X, Hu M, Song T, Semi-supervised medical image segmentation via cross teaching between cnn and transformer[C]\/\/International Conference on Medical Imaging with Deep Learning. PMLR, 2022: 820-833."},{"key":"e_1_3_2_1_29_1","volume-title":"Chen J","author":"Cao H","year":"2022","unstructured":"Cao H, Wang Y, Chen J, Swin-unet: Unet-like pure transformer for medical image segmentation[C]\/\/European conference on computer vision. Cham: Springer Nature Switzerland, 2022: 205-218."},{"key":"e_1_3_2_1_30_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2022.109512"}],"event":{"name":"CVDL 2024: The International Conference on Computer Vision and Deep Learning","acronym":"CVDL 2024","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\/3653804.3654609","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3653804.3654609","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,8,22]],"date-time":"2025-08-22T15:26:57Z","timestamp":1755876417000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3653804.3654609"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,1,19]]},"references-count":30,"alternative-id":["10.1145\/3653804.3654609","10.1145\/3653804"],"URL":"https:\/\/doi.org\/10.1145\/3653804.3654609","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"}}]}}