{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,14]],"date-time":"2026-04-14T09:10:22Z","timestamp":1776157822232,"version":"3.50.1"},"reference-count":55,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T00:00:00Z","timestamp":1775001600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T00:00:00Z","timestamp":1775001600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T00:00:00Z","timestamp":1775001600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T00:00:00Z","timestamp":1775001600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T00:00:00Z","timestamp":1775001600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T00:00:00Z","timestamp":1775001600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T00:00:00Z","timestamp":1775001600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"funder":[{"DOI":"10.13039\/501100010256","name":"Guangzhou Municipal Science and Technology Project","doi-asserted-by":"publisher","award":["2025A04J4774"],"award-info":[{"award-number":["2025A04J4774"]}],"id":[{"id":"10.13039\/501100010256","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100010256","name":"Guangzhou Municipal Science and Technology Project","doi-asserted-by":"publisher","award":["2025A04J4773"],"award-info":[{"award-number":["2025A04J4773"]}],"id":[{"id":"10.13039\/501100010256","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003453","name":"Natural Science Foundation of Guangdong Province","doi-asserted-by":"publisher","award":["2025A1515011607"],"award-info":[{"award-number":["2025A1515011607"]}],"id":[{"id":"10.13039\/501100003453","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100005153","name":"China National Funds for Distinguished Young Scientists","doi-asserted-by":"publisher","award":["82202095"],"award-info":[{"award-number":["82202095"]}],"id":[{"id":"10.13039\/501100005153","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100005153","name":"China National Funds for Distinguished Young Scientists","doi-asserted-by":"publisher","award":["82402270"],"award-info":[{"award-number":["82402270"]}],"id":[{"id":"10.13039\/501100005153","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003785","name":"Guangdong Medical Research Foundation","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100003785","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["82471947"],"award-info":[{"award-number":["82471947"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["82272088"],"award-info":[{"award-number":["82272088"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["82271941"],"award-info":[{"award-number":["82271941"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["82472051"],"award-info":[{"award-number":["82472051"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["clinicalkey.com","clinicalkey.com.au","clinicalkey.es","clinicalkey.fr","clinicalkey.jp","elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Computerized Medical Imaging and Graphics"],"published-print":{"date-parts":[[2026,4]]},"DOI":"10.1016\/j.compmedimag.2026.102754","type":"journal-article","created":{"date-parts":[[2026,3,19]],"date-time":"2026-03-19T08:37:43Z","timestamp":1773909463000},"page":"102754","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"C","title":["STFNet: A spatial\u2013temporal feature aggregation network for breast lesion segmentation in ultrasound videos"],"prefix":"10.1016","volume":"130","author":[{"given":"Chuansong","family":"Fan","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lei","family":"Wu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yu","family":"Liu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mengxia","family":"Yao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lifen","family":"Yan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Naqian","family":"Yu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zijun","family":"Liu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Changhong","family":"Liang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jiayao","family":"Shi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9307-8522","authenticated-orcid":false,"given":"Zaiyi","family":"Liu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ying","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"78","reference":[{"issue":"8","key":"10.1016\/j.compmedimag.2026.102754_b1","doi-asserted-by":"crossref","first-page":"5023","DOI":"10.1007\/s11831-023-09968-z","article-title":"A comprehensive review on breast cancer detection, classification and segmentation using deep learning","volume":"30","author":"Abhisheka","year":"2023","journal-title":"Arch. Comput. Methods Eng."},{"key":"10.1016\/j.compmedimag.2026.102754_b2","doi-asserted-by":"crossref","DOI":"10.1016\/j.compbiomed.2024.109353","article-title":"Latup-net: A lightweight 3d attention u-net with parallel convolutions for brain tumor segmentation","volume":"184","author":"Alwadee","year":"2025","journal-title":"Comput. Biol. Med."},{"key":"10.1016\/j.compmedimag.2026.102754_b3","doi-asserted-by":"crossref","unstructured":"Arnab, A., Dehghani, M., Heigold, G., Sun, C., Lu\u010di\u0107, M., Schmid, C., 2021. Vivit: A video vision transformer. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision. pp. 6836\u20136846.","DOI":"10.1109\/ICCV48922.2021.00676"},{"key":"10.1016\/j.compmedimag.2026.102754_b4","doi-asserted-by":"crossref","DOI":"10.1016\/j.media.2023.102797","article-title":"Pdatt-unet: Pyramid dual-decoder attention unet for covid-19 infection segmentation from ct-scans","volume":"86","author":"Bougourzi","year":"2023","journal-title":"Med. Image Anal."},{"issue":"3","key":"10.1016\/j.compmedimag.2026.102754_b5","first-page":"229","article-title":"Global cancer statistics 2022: Globocan estimates of incidence and mortality worldwide for 36 cancers in 185 countries","volume":"74","author":"Bray","year":"2024","journal-title":"CA: Cancer J. Clin."},{"key":"10.1016\/j.compmedimag.2026.102754_b6","series-title":"2009 IEEE 12th International Conference on Computer Vision","first-page":"833","article-title":"Video object segmentation by tracking regions","author":"Brendel","year":"2009"},{"issue":"5","key":"10.1016\/j.compmedimag.2026.102754_b7","doi-asserted-by":"crossref","first-page":"1289","DOI":"10.1109\/TMI.2022.3226268","article-title":"Aau-net: an adaptive attention u-net for breast lesions segmentation in ultrasound images","volume":"42","author":"Chen","year":"2022","journal-title":"IEEE Trans. Med. Imaging"},{"key":"10.1016\/j.compmedimag.2026.102754_b8","series-title":"Transunet: Transformers make strong encoders for medical image segmentation","author":"Chen","year":"2021"},{"key":"10.1016\/j.compmedimag.2026.102754_b9","series-title":"European Conference on Computer Vision","first-page":"640","article-title":"Xmem: Long-term video object segmentation with an Atkinson-Shiffrin memory model","author":"Cheng","year":"2022"},{"key":"10.1016\/j.compmedimag.2026.102754_b10","first-page":"11781","article-title":"Rethinking space\u2013time networks with improved memory coverage for efficient video object segmentation","volume":"34","author":"Cheng","year":"2021","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"10.1016\/j.compmedimag.2026.102754_b11","doi-asserted-by":"crossref","DOI":"10.1016\/j.compmedimag.2025.102608","article-title":"Taming large vision model for medical image segmentation via dual visual prompt tuning","volume":"124","author":"Cui","year":"2025","journal-title":"Comput. Med. Imaging Graph."},{"key":"10.1016\/j.compmedimag.2026.102754_b12","series-title":"An image is worth 16 \u00d7 16 words: Transformers for image recognition at scale","author":"Dosovitskiy","year":"2020"},{"key":"10.1016\/j.compmedimag.2026.102754_b13","doi-asserted-by":"crossref","unstructured":"Duke, B., Ahmed, A., Wolf, C., Aarabi, P., Taylor, G.W., 2021. Sstvos: Sparse spatiotemporal transformers for video object segmentation. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. pp. 5912\u20135921.","DOI":"10.1109\/CVPR46437.2021.00585"},{"key":"10.1016\/j.compmedimag.2026.102754_b14","doi-asserted-by":"crossref","unstructured":"Fu, Z., Liu, Q., Fu, Z., Wang, Y., 2021. Stmtrack: Template-free visual tracking with space\u2013time memory networks. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. pp. 13774\u201313783.","DOI":"10.1109\/CVPR46437.2021.01356"},{"key":"10.1016\/j.compmedimag.2026.102754_b15","series-title":"2020 25th International Conference on Pattern Recognition","first-page":"1236","article-title":"Sa-unet: Spatial attention u-net for retinal vessel segmentation","author":"Guo","year":"2021"},{"key":"10.1016\/j.compmedimag.2026.102754_b16","doi-asserted-by":"crossref","unstructured":"Hatamizadeh, A., Tang, Y., Nath, V., Yang, D., Myronenko, A., Landman, B., Roth, H.R., Xu, D., 2022. Unetr: Transformers for 3d medical image segmentation. In: Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision. pp. 574\u2013584.","DOI":"10.1109\/WACV51458.2022.00181"},{"key":"10.1016\/j.compmedimag.2026.102754_b17","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J., 2016. Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp. 770\u2013778.","DOI":"10.1109\/CVPR.2016.90"},{"key":"10.1016\/j.compmedimag.2026.102754_b18","doi-asserted-by":"crossref","unstructured":"Hu, J., Shen, L., Sun, G., 2018. Squeeze-and-excitation networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp. 7132\u20137141.","DOI":"10.1109\/CVPR.2018.00745"},{"issue":"8","key":"10.1016\/j.compmedimag.2026.102754_b19","doi-asserted-by":"crossref","first-page":"5631","DOI":"10.1109\/JBHI.2025.3546345","article-title":"Emganet: Edge-aware multi-scale group-mix attention network for breast cancer ultrasound image segmentation","volume":"29","author":"Huang","year":"2025","journal-title":"IEEE J. Biomed. Health Informatics"},{"key":"10.1016\/j.compmedimag.2026.102754_b20","doi-asserted-by":"crossref","DOI":"10.1016\/j.media.2022.102490","article-title":"Extracting keyframes of breast ultrasound video using deep reinforcement learning","volume":"80","author":"Huang","year":"2022","journal-title":"Med. Image Anal."},{"key":"10.1016\/j.compmedimag.2026.102754_b21","doi-asserted-by":"crossref","first-page":"74","DOI":"10.1016\/j.neunet.2019.08.025","article-title":"Multiresunet: Rethinking the u-net architecture for multimodal biomedical image segmentation","volume":"121","author":"Ibtehaz","year":"2020","journal-title":"Neural Netw."},{"issue":"2","key":"10.1016\/j.compmedimag.2026.102754_b22","doi-asserted-by":"crossref","first-page":"203","DOI":"10.1038\/s41592-020-01008-z","article-title":"nnu-net: a self-configuring method for deep learning-based biomedical image segmentation","volume":"18","author":"Isensee","year":"2021","journal-title":"Nature Methods"},{"issue":"4","key":"10.1016\/j.compmedimag.2026.102754_b23","doi-asserted-by":"crossref","first-page":"227","DOI":"10.7863\/jum.1986.5.4.227","article-title":"Artifacts in ultrasound imaging","volume":"5","author":"Kremkau","year":"1986","journal-title":"J. Ultrasound Med."},{"key":"10.1016\/j.compmedimag.2026.102754_b24","series-title":"International Conference on Medical Image Computing and Computer-Assisted Intervention","first-page":"390","article-title":"Oralsam: One-shot segmentation for intraoral ultrasound videos with adaptive feature correlation and self-prompting strategy","author":"Kumaralingam","year":"2025"},{"key":"10.1016\/j.compmedimag.2026.102754_b25","series-title":"2011 International Conference on Computer Vision","first-page":"1995","article-title":"Key-segments for video object segmentation","author":"Lee","year":"2011"},{"key":"10.1016\/j.compmedimag.2026.102754_b26","doi-asserted-by":"crossref","unstructured":"Li, Y., Chen, Z., Yan, J., Ding, Z., Li, J., Huang, T., Pei, X., Zhang, Z., Wang, Q., Pang, Y., 2024. Optimized breast lesion segmentation in ultrasound videos across varied resource-scant environments. In: Proceedings of the Asian Conference on Computer Vision. pp. 4318\u20134333.","DOI":"10.1007\/978-981-96-0966-6_28"},{"key":"10.1016\/j.compmedimag.2026.102754_b27","first-page":"4652","article-title":"U-kan makes strong backbone for medical image segmentation and generation","volume":"vol. 39","author":"Li","year":"2025"},{"key":"10.1016\/j.compmedimag.2026.102754_b28","doi-asserted-by":"crossref","unstructured":"Li, X., Zhang, W., Pang, J., Chen, K., Cheng, G., Tong, Y., Loy, C.C., 2022. Video k-net: A simple, strong, and unified baseline for video segmentation. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. pp. 18847\u201318857.","DOI":"10.1109\/CVPR52688.2022.01828"},{"key":"10.1016\/j.compmedimag.2026.102754_b29","series-title":"International Conference on Medical Image Computing and Computer-Assisted Intervention","first-page":"391","article-title":"Rethinking breast lesion segmentation in ultrasound: A new video dataset and a baseline network","author":"Li","year":"2022"},{"key":"10.1016\/j.compmedimag.2026.102754_b30","article-title":"Cascaded inner-outer clip retformer for ultrasound video object segmentation","author":"Li","year":"2024","journal-title":"IEEE J. Biomed. Health Informatics"},{"key":"10.1016\/j.compmedimag.2026.102754_b31","series-title":"International Conference on Medical Image Computing and Computer-Assisted Intervention","first-page":"497","article-title":"Shifting more attention to breast lesion segmentation in ultrasound videos","author":"Lin","year":"2023"},{"key":"10.1016\/j.compmedimag.2026.102754_b32","doi-asserted-by":"crossref","unstructured":"Liu, Z., Lin, Y., Cao, Y., Hu, H., Wei, Y., Zhang, Z., Lin, S., Guo, B., 2021. Swin transformer: Hierarchical vision transformer using shifted windows. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision. pp. 10012\u201310022.","DOI":"10.1109\/ICCV48922.2021.00986"},{"key":"10.1016\/j.compmedimag.2026.102754_b33","series-title":"U-mamba: Enhancing long-range dependency for biomedical image segmentation","author":"Ma","year":"2024"},{"key":"10.1016\/j.compmedimag.2026.102754_b34","series-title":"2016 Fourth International Conference on 3D Vision","first-page":"565","article-title":"V-net: Fully convolutional neural networks for volumetric medical image segmentation","author":"Milletari","year":"2016"},{"key":"10.1016\/j.compmedimag.2026.102754_b35","doi-asserted-by":"crossref","DOI":"10.1016\/j.compmedimag.2025.102627","article-title":"Enhancing cardiac function assessment: Developing and validating a domain adaptive framework for automating the segmentation of echocardiogram videos","volume":"124","author":"Nazari","year":"2025","journal-title":"Comput. Med. Imaging Graph."},{"issue":"1","key":"10.1016\/j.compmedimag.2026.102754_b36","doi-asserted-by":"crossref","first-page":"335","DOI":"10.1186\/1471-2407-9-335","article-title":"Early detection of breast cancer: benefits and risks of supplemental breast ultrasound in asymptomatic women with mammographically dense breast tissue. a systematic review","volume":"9","author":"Nothacker","year":"2009","journal-title":"BMC Cancer"},{"key":"10.1016\/j.compmedimag.2026.102754_b37","doi-asserted-by":"crossref","unstructured":"Oh, S.W., Lee, J.-Y., Xu, N., Kim, S.J., 2019. Video object segmentation using space\u2013time memory networks. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision. pp. 9226\u20139235.","DOI":"10.1109\/ICCV.2019.00932"},{"key":"10.1016\/j.compmedimag.2026.102754_b38","series-title":"Attention u-net: Learning where to look for the pancreas","author":"Oktay","year":"2018"},{"key":"10.1016\/j.compmedimag.2026.102754_b39","doi-asserted-by":"crossref","DOI":"10.1109\/JBHI.2025.3543435","article-title":"Efficient breast lesion segmentation from ultrasound videos across multiple source-limited platforms","author":"Pang","year":"2025","journal-title":"IEEE J. Biomed. Health Informatics"},{"key":"10.1016\/j.compmedimag.2026.102754_b40","unstructured":"Qin, C., Cao, J., Khan, F.S., Khan, S., Fu, H., Ahissar, E., Anwer, R.M., 2025. Real-time breast lesion detection in videos via spatial\u2013temporal feature aggregation. In: Medical Imaging with Deep Learning."},{"key":"10.1016\/j.compmedimag.2026.102754_b41","first-page":"234","article-title":"U-net: Convolutional networks for biomedical image segmentation","volume":"vol. 18","author":"Ronneberger","year":"2015"},{"issue":"1","key":"10.1016\/j.compmedimag.2026.102754_b42","doi-asserted-by":"crossref","first-page":"149","DOI":"10.1148\/radiol.212319","article-title":"A practical ceus thyroid reporting system for thyroid nodules","volume":"305","author":"Ruan","year":"2022","journal-title":"Radiology"},{"key":"10.1016\/j.compmedimag.2026.102754_b43","series-title":"International Conference on Medical Image Computing and Computer-Assisted Intervention","first-page":"380","article-title":"Light-weight spatio-temporal graphs for segmentation and ejection fraction prediction in cardiac ultrasound","author":"Thomas","year":"2022"},{"key":"10.1016\/j.compmedimag.2026.102754_b44","series-title":"A spatial\u2013temporal progressive fusion network for breast lesion segmentation in ultrasound videos","author":"Tu","year":"2024"},{"key":"10.1016\/j.compmedimag.2026.102754_b45","first-page":"36","article-title":"Medical transformer: Gated axial-attention for medical image segmentation","volume":"vol. 24","author":"Valanarasu","year":"2021"},{"key":"10.1016\/j.compmedimag.2026.102754_b46","article-title":"Attention is all you need","volume":"vol. 30","author":"Vaswani","year":"2017"},{"key":"10.1016\/j.compmedimag.2026.102754_b47","series-title":"International Conference on Medical Image Computing and Computer-Assisted Intervention","first-page":"238","article-title":"Key-frame guided network for thyroid nodule recognition using ultrasound videos","author":"Wang","year":"2022"},{"key":"10.1016\/j.compmedimag.2026.102754_b48","series-title":"2023 IEEE International Conference on Bioinformatics and Biomedicine","first-page":"3574","article-title":"An end-to-end multi-stage network for ultrasound video object segmentation","author":"Wang","year":"2023"},{"key":"10.1016\/j.compmedimag.2026.102754_b49","first-page":"2491","article-title":"Associating objects with transformers for video object segmentation","volume":"34","author":"Yang","year":"2021","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"10.1016\/j.compmedimag.2026.102754_b50","doi-asserted-by":"crossref","DOI":"10.1109\/TCSVT.2025.3563411","article-title":"Vivim: a video vision mamba for ultrasound video segmentation","author":"Yang","year":"2025","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"10.1016\/j.compmedimag.2026.102754_b51","doi-asserted-by":"crossref","DOI":"10.1016\/j.dsp.2025.105331","article-title":"Dflnet: Disentangled feature learning network for breast cancer ultrasound image segmentation","volume":"165","author":"Ye","year":"2025","journal-title":"Digit. Signal Process."},{"key":"10.1016\/j.compmedimag.2026.102754_b52","series-title":"2024 IEEE International Symposium on Biomedical Imaging","first-page":"1","article-title":"Is two-shot all you need? a label-efficient approach for video segmentation in breast ultrasound","author":"Zeng","year":"2024"},{"issue":"5","key":"10.1016\/j.compmedimag.2026.102754_b53","doi-asserted-by":"crossref","first-page":"749","DOI":"10.1109\/LGRS.2018.2802944","article-title":"Road extraction by deep residual u-net","volume":"15","author":"Zhang","year":"2018","journal-title":"IEEE Geosci. Remote. Sens. Lett."},{"key":"10.1016\/j.compmedimag.2026.102754_b54","first-page":"1","article-title":"Spatio-temporal context fusion network for endoscopic ultrasound video segmentation","author":"Zheng","year":"2024","journal-title":"J. Shanghai Jiaotong Univ. (Science)"},{"key":"10.1016\/j.compmedimag.2026.102754_b55","first-page":"3","article-title":"Unet++: A nested u-net architecture for medical image segmentation","volume":"vol. 4","author":"Zhou","year":"2018"}],"container-title":["Computerized Medical Imaging and Graphics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0895611126000571?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0895611126000571?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,4,14]],"date-time":"2026-04-14T08:19:31Z","timestamp":1776154771000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0895611126000571"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,4]]},"references-count":55,"alternative-id":["S0895611126000571"],"URL":"https:\/\/doi.org\/10.1016\/j.compmedimag.2026.102754","relation":{},"ISSN":["0895-6111"],"issn-type":[{"value":"0895-6111","type":"print"}],"subject":[],"published":{"date-parts":[[2026,4]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"STFNet: A spatial\u2013temporal feature aggregation network for breast lesion segmentation in ultrasound videos","name":"articletitle","label":"Article Title"},{"value":"Computerized Medical Imaging and Graphics","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.compmedimag.2026.102754","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 Elsevier Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies.","name":"copyright","label":"Copyright"}],"article-number":"102754"}}