{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,25]],"date-time":"2026-05-25T07:05:20Z","timestamp":1779692720396,"version":"3.53.1"},"reference-count":43,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"funder":[{"DOI":"10.13039\/501100012245","name":"Science and Technology Planning Project of Guangdong Province","doi-asserted-by":"publisher","award":["2023A0505020002"],"award-info":[{"award-number":["2023A0505020002"]}],"id":[{"id":"10.13039\/501100012245","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62171290"],"award-info":[{"award-number":["62171290"]}],"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":["62471305"],"award-info":[{"award-number":["62471305"]}],"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":["62101342"],"award-info":[{"award-number":["62101342"]}],"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":["12326619"],"award-info":[{"award-number":["12326619"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100021171","name":"Basic and Applied Basic Research Foundation of Guangdong Province","doi-asserted-by":"publisher","award":["2023A1515012960"],"award-info":[{"award-number":["2023A1515012960"]}],"id":[{"id":"10.13039\/501100021171","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100021171","name":"Basic and Applied Basic Research Foundation of Guangdong Province","doi-asserted-by":"publisher","award":["2025A1515011448"],"award-info":[{"award-number":["2025A1515011448"]}],"id":[{"id":"10.13039\/501100021171","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Neural Networks"],"published-print":{"date-parts":[[2026,8]]},"DOI":"10.1016\/j.neunet.2026.108806","type":"journal-article","created":{"date-parts":[[2026,3,4]],"date-time":"2026-03-04T16:23:30Z","timestamp":1772641410000},"page":"108806","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"C","title":["Projection-based tokenization with Pseudo feature learning for ovarian lesion segementation in ultrasound images"],"prefix":"10.1016","volume":"200","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1016-3535","authenticated-orcid":false,"given":"Yanlin","family":"Chen","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuhan","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ruobing","family":"Huang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ao","family":"Chang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jian","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jianhua","family":"Zhou","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dong","family":"Ni","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.neunet.2026.108806_bib0001","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: 1802.06955,.","DOI":"10.1109\/NAECON.2018.8556686"},{"issue":"2","key":"10.1016\/j.neunet.2026.108806_bib0002","doi-asserted-by":"crossref","first-page":"733","DOI":"10.1109\/TIP.2011.2164416","article-title":"Ct reconstruction from parallel and fan-beam projections by a 2-d discrete radon transform","volume":"21","author":"Averbuch","year":"2011","journal-title":"IEEE Transactions on Image Processing"},{"issue":"3","key":"10.1016\/j.neunet.2026.108806_bib0003","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: A Cancer Journal for Clinicians,"},{"key":"10.1016\/j.neunet.2026.108806_bib0004","series-title":"European conference on computer vision","first-page":"205","article-title":"Swinunet: Unet-like pure transformer for medical image segmentation","author":"Cao","year":"2022"},{"key":"10.1016\/j.neunet.2026.108806_bib0005","doi-asserted-by":"crossref","DOI":"10.1016\/j.patcog.2024.110761","article-title":"Complementary pseudo multimodal feature for point cloud anomaly detection","volume":"156","author":"Cao","year":"2024","journal-title":"Pattern Recognition"},{"key":"10.1016\/j.neunet.2026.108806_bib0006","doi-asserted-by":"crossref","DOI":"10.1016\/j.ijepes.2025.110982","article-title":"Resilient memory sampled-data controller for synchronization of semi-markovian jump competitive neural networks with mixed delays","volume":"171","author":"Chandrasekar","year":"2025","journal-title":"International Journal of Electrical Power & Energy Systems"},{"key":"10.1016\/j.neunet.2026.108806_bib0007","series-title":"Proceedings of the IEEE\/CVF international conference on computer vision","first-page":"357","article-title":"Crossvit: Cross-attention multi-scale vision transformer for image classification","author":"Chen","year":"2021"},{"key":"10.1016\/j.neunet.2026.108806_bib0008","unstructured":"Chen, J., Lu, Y., Yu, Q., Luo, X., Adeli, E., Wang, Y., Lu, L., Yuille, A. L., & Zhou, Y. (2021b). TransUNet: Transformers make strong encoders for medical image segmentation. arXiv: 2102.04306,."},{"key":"10.1016\/j.neunet.2026.108806_bib0009","unstructured":"Chen, L.-C., Papandreou, G., Schroff, F., & Adam, H. (2017). Rethinking atrous convolution for semantic image segmentation. arXiv: 1706.05587,."},{"issue":"10","key":"10.1016\/j.neunet.2026.108806_bib0010","doi-asserted-by":"crossref","first-page":"1664","DOI":"10.1109\/PROC.1967.5957","article-title":"What is the fast fourier transform?","volume":"55","author":"Cochran","year":"1967","journal-title":"Proceedings of the IEEE"},{"key":"10.1016\/j.neunet.2026.108806_bib0011","doi-asserted-by":"crossref","DOI":"10.1109\/TCDS.2025.3625782","article-title":"Aligned pseudo feature generation for zero-shot object detection","author":"Dai","year":"2025","journal-title":"IEEE Transactions on Cognitive and Developmental Systems"},{"key":"10.1016\/j.neunet.2026.108806_bib0012","unstructured":"Dong, B., Wang, W., Fan, D.-P., Li, J., Fu, H., & Shao, L. (2021). Polyp-PVT: Polyp segmentation with pyramid vision transformers. arXiv: 2108.06932,."},{"key":"10.1016\/j.neunet.2026.108806_bib0013","unstructured":"Dosovitskiy, A. (2020). An image is worth 16x16 words: Transformers for image recognition at scale. arXiv: 2010.11929,."},{"key":"10.1016\/j.neunet.2026.108806_bib0014","doi-asserted-by":"crossref","DOI":"10.3389\/fnins.2023.1153356","article-title":"Gl-segnet: Global-local representation learning net for medical image segmentation","volume":"17","author":"Gai","year":"2023","journal-title":"Frontiers in Neuroscience"},{"issue":"4","key":"10.1016\/j.neunet.2026.108806_bib0015","doi-asserted-by":"crossref","first-page":"56","DOI":"10.1038\/scientificamerican1075-56","article-title":"Image reconstruction from projections","volume":"233","author":"Gordon","year":"1975","journal-title":"Scientific American,"},{"key":"10.1016\/j.neunet.2026.108806_bib0016","doi-asserted-by":"crossref","first-page":"354","DOI":"10.1016\/j.patcog.2017.10.013","article-title":"Recent advances in convolutional neural networks","volume":"77","author":"Gu","year":"2018","journal-title":"Pattern Recognition"},{"issue":"7553","key":"10.1016\/j.neunet.2026.108806_bib0017","doi-asserted-by":"crossref","first-page":"436","DOI":"10.1038\/nature14539","article-title":"Deep learning","volume":"521","author":"LeCun","year":"2015","journal-title":"Nature"},{"key":"10.1016\/j.neunet.2026.108806_bib0018","series-title":"Proceedings of the european conference on computer vision (ECCV)","first-page":"201","article-title":"Stacked cross attention for image-text matching","author":"Lee","year":"2018"},{"issue":"3","key":"10.1016\/j.neunet.2026.108806_bib0019","doi-asserted-by":"crossref","first-page":"1053","DOI":"10.2353\/ajpath.2010.100105","article-title":"Ovarian cancer development and metastasis","volume":"177","author":"Lengyel","year":"2010","journal-title":"The American Journal of Pathology"},{"key":"10.1016\/j.neunet.2026.108806_bib0020","series-title":"15Th international meeting on fully three-dimensional image reconstruction in radiology and nuclear medicine","first-page":"345","article-title":"A sinogram inpainting method based on generative adversarial network for limited-angle computed tomography","volume":"vol. 11072","author":"Li","year":"2019"},{"issue":"8","key":"10.1016\/j.neunet.2026.108806_bib0021","doi-asserted-by":"crossref","first-page":"2451","DOI":"10.1109\/TMI.2023.3250474","article-title":"Ckd-transbts: Clinical knowledge-driven hybrid transformer with modality-correlated cross-attention for brain tumor segmentation","volume":"42","author":"Lin","year":"2023","journal-title":"IEEE Transactions on Medical Imaging"},{"key":"10.1016\/j.neunet.2026.108806_bib0022","doi-asserted-by":"crossref","first-page":"129","DOI":"10.1016\/j.patcog.2018.03.006","article-title":"Attribute-based synthetic network (ABS-net): learning more from pseudo feature representations","volume":"80","author":"Lu","year":"2018","journal-title":"Pattern Recognition"},{"key":"10.1016\/j.neunet.2026.108806_bib0023","article-title":"Lgffm: A localized and globalized frequency fusion model for ultrasound image segmentation","author":"Luo","year":"2025","journal-title":"IEEE Transactions on Medical Imaging"},{"issue":"11","key":"10.1016\/j.neunet.2026.108806_bib0024","doi-asserted-by":"crossref","first-page":"1482","DOI":"10.1109\/TUFFC.2023.3316284","article-title":"Enhanced needle visualization with reflection tuned apodization based on the radon transform for ultrasound imaging","volume":"70","author":"Malamal","year":"2023","journal-title":"IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control"},{"key":"10.1016\/j.neunet.2026.108806_bib0025","series-title":"2024 International conference on multimedia analysis and pattern recognition (MAPR)","first-page":"1","article-title":"A method for ovarian tumor segmentation based on segment anything model","author":"Nguyen","year":"2024"},{"key":"10.1016\/j.neunet.2026.108806_bib0026","series-title":"2024 10Th international conference on applied system innovation (ICASI)","first-page":"345","article-title":"Adaptive radon transform-based fundus image enhancement algorithm for blood vessel segmentation","author":"Nguyen","year":"2024"},{"issue":"2","key":"10.1016\/j.neunet.2026.108806_bib0027","doi-asserted-by":"crossref","first-page":"476","DOI":"10.1109\/TMI.2021.3116087","article-title":"Smu-net: Saliency-guided morphology-aware u-net for breast lesion segmentation in ultrasound image","volume":"41","author":"Ning","year":"2021","journal-title":"IEEE Transactions on Medical Imaging"},{"key":"10.1016\/j.neunet.2026.108806_bib0028","unstructured":"Oktay, O., Schlemper, J., Folgoc, L. L., Lee, M., Heinrich, M., Misawa, K., Mori, K., McDonagh, S., Hammerla, N. Y., Kainz, B. et al. (2018). Attention u-net: Learning where to look for the pancreas. arXiv: 1804.03999,."},{"key":"10.1016\/j.neunet.2026.108806_bib0029","doi-asserted-by":"crossref","DOI":"10.1016\/j.bspc.2022.104558","article-title":"Breast cancer detection and diagnosis using hybrid deep learning architecture","volume":"82","author":"Raaj","year":"2023","journal-title":"Biomedical Signal Processing and Control"},{"key":"10.1016\/j.neunet.2026.108806_bib0030","series-title":"Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition","first-page":"11769","article-title":"Emcad: Efficient multi-scale convolutional attention decoding for medical image segmentation","author":"Rahman","year":"2024"},{"key":"10.1016\/j.neunet.2026.108806_bib0031","series-title":"Medical image computing and computer-assisted intervention\u2013MICCAI 2015: 18th international conference, munich, germany","first-page":"234","article-title":"U-Net: Convolutional networks for biomedical image segmentation","author":"Ronneberger","year":"2015"},{"key":"10.1016\/j.neunet.2026.108806_bib0032","series-title":"Deep generative models, and data augmentation, labelling, and imperfections: First workshop, held in conjunction with MICCAI 2021, strasbourg, france","first-page":"243","article-title":"Zero-shot domain adaptation in CT segmentation by filtered back projection augmentation","author":"Saparov","year":"2021"},{"issue":"3","key":"10.1016\/j.neunet.2026.108806_bib0033","doi-asserted-by":"crossref","DOI":"10.1148\/radiol.230685","article-title":"O-RADS US V2022: An update from the american college of radiology\u2019s ovarian-adnexal reporting and data system US committee","volume":"308","author":"Strachowski","year":"2023","journal-title":"Radiology"},{"key":"10.1016\/j.neunet.2026.108806_bib0034","first-page":"1","article-title":"Robust dissipative sliding mode control synchronization of memristive inertial competitive neural networks with time-varying delay","author":"Subhashri","year":"2025","journal-title":"The European Physical Journal Special Topics"},{"key":"10.1016\/j.neunet.2026.108806_bib0035","doi-asserted-by":"crossref","DOI":"10.1016\/j.compmedimag.2023.102228","article-title":"Cerebrovascular segmentation in phase-contrast magnetic resonance angiography by a radon projection composition network","volume":"107","author":"Weng","year":"2023","journal-title":"Computerized Medical Imaging and Graphics,"},{"key":"10.1016\/j.neunet.2026.108806_bib0036","article-title":"Attention-guided learning with feature reconstruction for skin lesion diagnosis using clinical and ultrasound images","author":"Xiao","year":"2024","journal-title":"IEEE Transactions on Medical Imaging"},{"key":"10.1016\/j.neunet.2026.108806_bib0037","doi-asserted-by":"crossref","DOI":"10.1016\/j.neunet.2025.107943","article-title":"X-UNEt: A novel global context-aware collaborative fusion u-shaped network with progressive feature fusion of codec for medical image segmentation","author":"Xu","year":"2025","journal-title":"Neural Networks"},{"key":"10.1016\/j.neunet.2026.108806_bib0038","doi-asserted-by":"crossref","DOI":"10.1016\/j.media.2021.101989","article-title":"Global guidance network for breast lesion segmentation in ultrasound images","volume":"70","author":"Xue","year":"2021","journal-title":"Medical Image Analysis"},{"key":"10.1016\/j.neunet.2026.108806_bib0039","doi-asserted-by":"crossref","DOI":"10.1016\/j.media.2021.102134","article-title":"Contrastive rendering with semi-supervised learning for ovary and follicle segmentation from 3d ultrasound","volume":"73","author":"Yang","year":"2021","journal-title":"Medical Image Analysis"},{"key":"10.1016\/j.neunet.2026.108806_bib0040","series-title":"Proceedings of the winter conference on applications of computer vision (WACV)","first-page":"7710","article-title":"U-Mixformer: UNet-like transformer with mix-attention for efficient semantic segmentation","author":"Yeom","year":"2025"},{"key":"10.1016\/j.neunet.2026.108806_bib0041","doi-asserted-by":"crossref","first-page":"298","DOI":"10.1016\/j.neunet.2023.11.034","article-title":"Ct-net: Asymmetric compound branch transformer for medical image segmentation","volume":"170","author":"Zhang","year":"2024","journal-title":"Neural Networks"},{"key":"10.1016\/j.neunet.2026.108806_bib0042","series-title":"Medical image computing and computer assisted intervention\u2013MICCAI 2021: 24th international conference, strasbourg, france","first-page":"14","article-title":"Transfuse: Fusing transformers and CNNs for medical image segmentation","author":"Zhang","year":"2021"},{"key":"10.1016\/j.neunet.2026.108806_bib0043","article-title":"A multi-modality ovarian tumor ultrasound image dataset for unsupervised cross-domain semantic segmentation","author":"Zhao","year":"2022","journal-title":"CoRR"}],"container-title":["Neural Networks"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0893608026002686?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0893608026002686?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,5,25]],"date-time":"2026-05-25T06:40:31Z","timestamp":1779691231000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0893608026002686"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,8]]},"references-count":43,"alternative-id":["S0893608026002686"],"URL":"https:\/\/doi.org\/10.1016\/j.neunet.2026.108806","relation":{},"ISSN":["0893-6080"],"issn-type":[{"value":"0893-6080","type":"print"}],"subject":[],"published":{"date-parts":[[2026,8]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Projection-based tokenization with Pseudo feature learning for ovarian lesion segementation in ultrasound images","name":"articletitle","label":"Article Title"},{"value":"Neural Networks","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.neunet.2026.108806","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":"108806"}}