{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,4]],"date-time":"2026-08-04T07:45:42Z","timestamp":1785829542133,"version":"3.56.0"},"reference-count":74,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"funder":[{"DOI":"10.13039\/501100004152","name":"Macao Polytechnic University","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100004152","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Medical Image Analysis"],"published-print":{"date-parts":[[2026,9]]},"DOI":"10.1016\/j.media.2026.104224","type":"journal-article","created":{"date-parts":[[2026,7,20]],"date-time":"2026-07-20T23:11:34Z","timestamp":1784589094000},"page":"104224","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"C","title":["S2DENet: Shallow suppression and deep enhancement network for general ultrasound image segmentation"],"prefix":"10.1016","volume":"113","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6436-1919","authenticated-orcid":false,"given":"Xintao","family":"Pang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-2027-5057","authenticated-orcid":false,"given":"Jinlin","family":"Yang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1576-4439","authenticated-orcid":false,"given":"Zhifan","family":"Gao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1779-1753","authenticated-orcid":false,"given":"Chuan","family":"Lin","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yue","family":"Sun","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shuo","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Peter H.N.","family":"de With","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tao","family":"Tan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"issue":"9","key":"10.1016\/j.media.2026.104224_b1","doi-asserted-by":"crossref","first-page":"997","DOI":"10.1109\/TBME.2002.1028423","article-title":"Real-time speckle reduction and coherence enhancement in ultrasound imaging via nonlinear anisotropic diffusion","volume":"49","author":"Abd-Elmoniem","year":"2002","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"10.1016\/j.media.2026.104224_b2","doi-asserted-by":"crossref","DOI":"10.1016\/j.dib.2019.104863","article-title":"Dataset of breast ultrasound images","volume":"28","author":"Al-Dhabyani","year":"2020","journal-title":"Data Brief"},{"issue":"11","key":"10.1016\/j.media.2026.104224_b3","doi-asserted-by":"crossref","first-page":"2514","DOI":"10.1109\/TMI.2018.2837502","article-title":"Deep learning techniques for automatic MRI cardiac multi-structures segmentation and diagnosis: is the problem solved?","volume":"37","author":"Bernard","year":"2018","journal-title":"IEEE Trans. Med. Imaging"},{"key":"10.1016\/j.media.2026.104224_b4","doi-asserted-by":"crossref","DOI":"10.1016\/j.media.2020.101651","article-title":"Automatic labeling of cortical sulci using patch-or CNN-based segmentation techniques combined with bottom-up geometric constraints","volume":"62","author":"Borne","year":"2020","journal-title":"Med. Image Anal."},{"key":"10.1016\/j.media.2026.104224_b5","series-title":"European Conference on Computer Vision","first-page":"205","article-title":"Swin-unet: Unet-like pure transformer for medical image segmentation","author":"Cao","year":"2022"},{"key":"10.1016\/j.media.2026.104224_b6","doi-asserted-by":"crossref","DOI":"10.1016\/j.media.2021.102311","article-title":"An end-to-end approach to segmentation in medical images with CNN and posterior-CRF","volume":"76","author":"Chen","year":"2022","journal-title":"Med. Image Anal."},{"issue":"5","key":"10.1016\/j.media.2026.104224_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.media.2026.104224_b8","series-title":"Transunet: Transformers make strong encoders for medical image segmentation","author":"Chen","year":"2021"},{"issue":"1","key":"10.1016\/j.media.2026.104224_b9","doi-asserted-by":"crossref","first-page":"299","DOI":"10.1016\/j.patcog.2009.05.012","article-title":"Automated breast cancer detection and classification using ultrasound images: A survey","volume":"43","author":"Cheng","year":"2010","journal-title":"Pattern Recognit."},{"key":"10.1016\/j.media.2026.104224_b10","series-title":"SAM-Med2D","author":"Cheng","year":"2023"},{"issue":"2","key":"10.1016\/j.media.2026.104224_b11","doi-asserted-by":"crossref","first-page":"29","DOI":"10.3390\/jimaging4020029","article-title":"Estimating full regional skeletal muscle fibre orientation from B-mode ultrasound images using convolutional, residual, and deconvolutional neural networks","volume":"4","author":"Cunningham","year":"2018","journal-title":"J. Imaging"},{"key":"10.1016\/j.media.2026.104224_b12","series-title":"An image is worth 16x16 words: Transformers for image recognition at scale","author":"Dosovitskiy","year":"2020"},{"issue":"19","key":"10.1016\/j.media.2026.104224_b13","doi-asserted-by":"crossref","first-page":"27915","DOI":"10.1007\/s11042-019-07884-8","article-title":"A novel breast ultrasound image automated segmentation algorithm based on seeded region growing integrating gradual equipartition threshold","volume":"78","author":"Fan","year":"2019","journal-title":"Multimedia Tools Appl."},{"issue":"6","key":"10.1016\/j.media.2026.104224_b14","first-page":"524","article-title":"Breast cancer statistics, 2022","volume":"72","author":"Giaquinto","year":"2022","journal-title":"CA: Cancer J. Clin."},{"issue":"4","key":"10.1016\/j.media.2026.104224_b15","doi-asserted-by":"crossref","first-page":"3110","DOI":"10.1002\/mp.16812","article-title":"BUS-BRA: a breast ultrasound dataset for assessing computer-aided diagnosis systems","volume":"51","author":"G\u00f3mez-Flores","year":"2024","journal-title":"Med. Phys."},{"key":"10.1016\/j.media.2026.104224_b16","doi-asserted-by":"crossref","DOI":"10.1016\/j.compbiomed.2022.106389","article-title":"Thyroid region prior guided attention for ultrasound segmentation of thyroid nodules","volume":"155","author":"Gong","year":"2023","journal-title":"Comput. Biol. Med."},{"key":"10.1016\/j.media.2026.104224_b17","series-title":"Digital Image Processing","author":"Gonzalez","year":"2009"},{"key":"10.1016\/j.media.2026.104224_b18","doi-asserted-by":"crossref","DOI":"10.1016\/j.compbiomed.2023.106629","article-title":"HCTNet: A hybrid CNN-transformer network for breast ultrasound image segmentation","volume":"155","author":"He","year":"2023","journal-title":"Comput. Biol. Med."},{"key":"10.1016\/j.media.2026.104224_b19","doi-asserted-by":"crossref","DOI":"10.1016\/j.media.2020.101722","article-title":"Dense biased networks with deep priori anatomy and hard region adaptation: Semi-supervised learning for fine renal artery segmentation","volume":"63","author":"He","year":"2020","journal-title":"Med. Image Anal."},{"key":"10.1016\/j.media.2026.104224_b20","doi-asserted-by":"crossref","DOI":"10.1016\/j.media.2021.102055","article-title":"Meta grayscale adaptive network for 3D integrated renal structures segmentation","volume":"71","author":"He","year":"2021","journal-title":"Med. Image Anal."},{"key":"10.1016\/j.media.2026.104224_b21","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.media.2026.104224_b22","doi-asserted-by":"crossref","unstructured":"Heidari, M., Kazerouni, A., Soltany, M., Azad, R., Aghdam, E.K., Cohen-Adad, J., Merhof, D., 2023. Hiformer: Hierarchical multi-scale representations using transformers for medical image segmentation. In: Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision. pp. 6202\u20136212.","DOI":"10.1109\/WACV56688.2023.00614"},{"issue":"8","key":"10.1016\/j.media.2026.104224_b23","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pone.0200412","article-title":"Automated measurement of fetal head circumference using 2D ultrasound images","volume":"13","author":"van den Heuvel","year":"2018","journal-title":"PloS One"},{"issue":"3","key":"10.1016\/j.media.2026.104224_b24","doi-asserted-by":"crossref","first-page":"272","DOI":"10.1016\/S1076-6332(03)00719-0","article-title":"Performance of computer-aided diagnosis in the interpretation of lesions on breast sonography","volume":"11","author":"Horsch","year":"2004","journal-title":"Academic Radiol."},{"key":"10.1016\/j.media.2026.104224_b25","doi-asserted-by":"crossref","unstructured":"Howard, A., Sandler, M., Chu, G., Chen, L.-C., Chen, B., Tan, M., Wang, W., Zhu, Y., Pang, R., Vasudevan, V., et al., 2019. Searching for mobilenetv3. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision. pp. 1314\u20131324.","DOI":"10.1109\/ICCV.2019.00140"},{"issue":"5","key":"10.1016\/j.media.2026.104224_b26","doi-asserted-by":"crossref","first-page":"625","DOI":"10.1016\/j.ultrasmedbio.2003.12.001","article-title":"Watershed segmentation for breast tumor in 2-D sonography","volume":"30","author":"Huang","year":"2004","journal-title":"Ultrasound Med. Biol."},{"key":"10.1016\/j.media.2026.104224_b27","doi-asserted-by":"crossref","unstructured":"Huang, G., Liu, Z., Van Der Maaten, L., Weinberger, K.Q., 2017. Densely connected convolutional networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp. 4700\u20134708.","DOI":"10.1109\/CVPR.2017.243"},{"issue":"2","key":"10.1016\/j.media.2026.104224_b28","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"},{"key":"10.1016\/j.media.2026.104224_b29","doi-asserted-by":"crossref","DOI":"10.1016\/j.bspc.2023.105329","article-title":"A hybrid enhanced attention transformer network for medical ultrasound image segmentation","volume":"86","author":"Jiang","year":"2023","journal-title":"Biomed. Signal Process. Control."},{"key":"10.1016\/j.media.2026.104224_b30","doi-asserted-by":"crossref","DOI":"10.1016\/j.media.2025.103554","article-title":"MedScale-Former: Self-guided multiscale transformer for medical image segmentation","volume":"103","author":"Karimijafarbigloo","year":"2025","journal-title":"Med. Image Anal."},{"key":"10.1016\/j.media.2026.104224_b31","doi-asserted-by":"crossref","first-page":"88","DOI":"10.1016\/j.media.2019.02.009","article-title":"Constrained-CNN losses for weakly supervised segmentation","volume":"54","author":"Kervadec","year":"2019","journal-title":"Med. Image Anal."},{"issue":"9","key":"10.1016\/j.media.2026.104224_b32","doi-asserted-by":"crossref","first-page":"2198","DOI":"10.1109\/TMI.2019.2900516","article-title":"Deep learning for segmentation using an open large-scale dataset in 2D echocardiography","volume":"38","author":"Leclerc","year":"2019","journal-title":"IEEE Trans. Med. Imaging"},{"key":"10.1016\/j.media.2026.104224_b33","article-title":"DCSAU-Net: A deeper and more compact split-attention U-Net for medical image segmentation","volume":"154","author":"Lei","year":"2023","journal-title":"Comput. Biol. Med."},{"key":"10.1016\/j.media.2026.104224_b34","doi-asserted-by":"crossref","unstructured":"Li, Y., Chan, K., Sun, Y., Lam, C., Tong, T., Yu, Z., Fu, K., Liu, X., Tan, T., 2025. MoEdit: On Learning Quantity Perception for Multi-object Image Editing. In: Proceedings of the Computer Vision and Pattern Recognition Conference. pp. 2683\u20132693.","DOI":"10.1109\/CVPR52734.2025.00256"},{"issue":"2","key":"10.1016\/j.media.2026.104224_b35","doi-asserted-by":"crossref","first-page":"1178","DOI":"10.1002\/mp.16662","article-title":"MultiIB-TransUNet: Transformer with multiple information bottleneck blocks for CT and ultrasound image segmentation","volume":"51","author":"Li","year":"2024","journal-title":"Med. Phys."},{"key":"10.1016\/j.media.2026.104224_b36","doi-asserted-by":"crossref","unstructured":"Li, C., Liu, X., Li, W., Wang, C., Liu, H., Liu, Y., Chen, Z., Yuan, Y., 2025. U-kan makes strong backbone for medical image segmentation and generation. In: Proceedings of the AAAI Conference on Artificial Intelligence. Vol. 39, pp. 4652\u20134660.","DOI":"10.1609\/aaai.v39i5.32491"},{"key":"10.1016\/j.media.2026.104224_b37","doi-asserted-by":"crossref","DOI":"10.1016\/j.compbiomed.2022.106365","article-title":"Attransunet: An enhanced hybrid transformer architecture for ultrasound and histopathology image segmentation","volume":"152","author":"Li","year":"2023","journal-title":"Comput. Biol. Med."},{"issue":"8","key":"10.1016\/j.media.2026.104224_b38","doi-asserted-by":"crossref","first-page":"2325","DOI":"10.1109\/TMI.2023.3247814","article-title":"The lighter the better: rethinking transformers in medical image segmentation through adaptive pruning","volume":"42","author":"Lin","year":"2023","journal-title":"IEEE Trans. Med. Imaging"},{"key":"10.1016\/j.media.2026.104224_b39","doi-asserted-by":"crossref","unstructured":"Liu, Z., Mao, H., Wu, C.-Y., Feichtenhofer, C., Darrell, T., Xie, S., 2022. A convnet for the 2020s. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. pp. 11976\u201311986.","DOI":"10.1109\/CVPR52688.2022.01167"},{"issue":"2","key":"10.1016\/j.media.2026.104224_b40","doi-asserted-by":"crossref","first-page":"261","DOI":"10.1016\/j.eng.2018.11.020","article-title":"Deep learning in medical ultrasound analysis: a review","volume":"5","author":"Liu","year":"2019","journal-title":"Engineering"},{"key":"10.1016\/j.media.2026.104224_b41","series-title":"International Conference on Medical Image Computing and Computer-Assisted Intervention","first-page":"615","article-title":"Swin-umamba: Mamba-based unet with imagenet-based pretraining","author":"Liu","year":"2024"},{"key":"10.1016\/j.media.2026.104224_b42","doi-asserted-by":"crossref","unstructured":"Liu, Y., Zhu, H., Liu, M., Yu, H., Chen, Z., Gao, J., 2024. Rolling-unet: Revitalizing mlp\u2019s ability to efficiently extract long-distance dependencies for medical image segmentation. In: Proceedings of the AAAI Conference on Artificial Intelligence. Vol. 38, pp. 3819\u20133827.","DOI":"10.1609\/aaai.v38i4.28173"},{"key":"10.1016\/j.media.2026.104224_b43","series-title":"Sgdr: Stochastic gradient descent with warm restarts","author":"Loshchilov","year":"2016"},{"key":"10.1016\/j.media.2026.104224_b44","series-title":"Decoupled weight decay regularization","author":"Loshchilov","year":"2017"},{"issue":"1","key":"10.1016\/j.media.2026.104224_b45","doi-asserted-by":"crossref","first-page":"654","DOI":"10.1038\/s41467-024-44824-z","article-title":"Segment anything in medical images","volume":"15","author":"Ma","year":"2024","journal-title":"Nat. Commun."},{"key":"10.1016\/j.media.2026.104224_b46","doi-asserted-by":"crossref","unstructured":"Ma, N., Zhang, X., Zheng, H.-T., Sun, J., 2018. Shufflenet v2: Practical guidelines for efficient cnn architecture design. In: Proceedings of the European Conference on Computer Vision. ECCV, pp. 116\u2013131.","DOI":"10.1007\/978-3-030-01264-9_8"},{"key":"10.1016\/j.media.2026.104224_b47","series-title":"Common carotid artery ultrasound images","author":"Momot","year":"2022"},{"key":"10.1016\/j.media.2026.104224_b48","series-title":"Attention u-net: Learning where to look for the pancreas","author":"Oktay","year":"2018"},{"issue":"7802","key":"10.1016\/j.media.2026.104224_b49","doi-asserted-by":"crossref","first-page":"252","DOI":"10.1038\/s41586-020-2145-8","article-title":"Video-based AI for beat-to-beat assessment of cardiac function","volume":"580","author":"Ouyang","year":"2020","journal-title":"Nature"},{"key":"10.1016\/j.media.2026.104224_b50","doi-asserted-by":"crossref","DOI":"10.1109\/TCSVT.2025.3558496","article-title":"BLENet: a bio-inspired lightweight and efficient network for left ventricle segmentation in echocardiography","author":"Pang","year":"2025","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"10.1016\/j.media.2026.104224_b51","doi-asserted-by":"crossref","DOI":"10.1016\/j.bspc.2021.102925","article-title":"Breast DCE-MRI segmentation for lesion detection by multi-level thresholding using student psychological based optimization","volume":"69","author":"Patra","year":"2021","journal-title":"Biomed. Signal Process. Control."},{"key":"10.1016\/j.media.2026.104224_b52","series-title":"10th International Symposium on Medical Information Processing and Analysis","first-page":"188","article-title":"An open access thyroid ultrasound image database","volume":"Vol. 9287","author":"Pedraza","year":"2015"},{"key":"10.1016\/j.media.2026.104224_b53","series-title":"European Conference on Computer Vision","first-page":"78","article-title":"MobileNetV4: Universal models for the mobile ecosystem","author":"Qin","year":"2024"},{"key":"10.1016\/j.media.2026.104224_b54","doi-asserted-by":"crossref","unstructured":"Rahman, M.M., Munir, M., Marculescu, R., 2024. Emcad: Efficient multi-scale convolutional attention decoding for medical image segmentation. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. pp. 11769\u201311779.","DOI":"10.1109\/CVPR52733.2024.01118"},{"key":"10.1016\/j.media.2026.104224_b55","series-title":"International Conference on Medical Image Computing and Computer-Assisted Intervention","first-page":"234","article-title":"U-net: Convolutional networks for biomedical image segmentation","author":"Ronneberger","year":"2015"},{"key":"10.1016\/j.media.2026.104224_b56","series-title":"International Conference on Medical Image Computing and Computer-Assisted Intervention","first-page":"405","article-title":"MedNeXt: transformer-driven scaling of convnets for medical image segmentation","author":"Roy","year":"2023"},{"key":"10.1016\/j.media.2026.104224_b57","series-title":"Vm-unet: Vision mamba unet for medical image segmentation","author":"Ruan","year":"2024"},{"key":"10.1016\/j.media.2026.104224_b58","doi-asserted-by":"crossref","unstructured":"Sandler, M., Howard, A., Zhu, M., Zhmoginov, A., Chen, L.-C., 2018. Mobilenetv2: Inverted residuals and linear bottlenecks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp. 4510\u20134520.","DOI":"10.1109\/CVPR.2018.00474"},{"issue":"5","key":"10.1016\/j.media.2026.104224_b59","doi-asserted-by":"crossref","first-page":"849","DOI":"10.1016\/j.eururo.2010.11.037","article-title":"Laparoscopic partial nephrectomy with segmental renal artery clamping: technique and clinical outcomes","volume":"59","author":"Shao","year":"2011","journal-title":"Eur. Urol."},{"issue":"6","key":"10.1016\/j.media.2026.104224_b60","doi-asserted-by":"crossref","first-page":"1001","DOI":"10.1016\/j.eururo.2012.05.056","article-title":"Precise segmental renal artery clamping under the guidance of dual-source computed tomography angiography during laparoscopic partial nephrectomy","volume":"62","author":"Shao","year":"2012","journal-title":"Eur. Urol."},{"issue":"9","key":"10.1016\/j.media.2026.104224_b61","doi-asserted-by":"crossref","first-page":"4512","DOI":"10.21037\/qims-22-33","article-title":"Dilated transformer: residual axial attention for breast ultrasound image segmentation","volume":"12","author":"Shen","year":"2022","journal-title":"Quant. Imaging Med. Surg."},{"key":"10.1016\/j.media.2026.104224_b62","series-title":"The open kidney ultrasound data set, international workshop on advances in simplifying medical ultrasound","author":"Singla","year":"2023"},{"key":"10.1016\/j.media.2026.104224_b63","doi-asserted-by":"crossref","unstructured":"Su, Z., Liu, W., Yu, Z., Hu, D., Liao, Q., Tian, Q., Pietik\u00e4inen, M., Liu, L., 2021. Pixel difference networks for efficient edge detection. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision. pp. 5117\u20135127.","DOI":"10.1109\/ICCV48922.2021.00507"},{"key":"10.1016\/j.media.2026.104224_b64","series-title":"Mobile U-ViT: Revisiting large kernel and U-shaped ViT for efficient medical image segmentation","author":"Tang","year":"2025"},{"key":"10.1016\/j.media.2026.104224_b65","doi-asserted-by":"crossref","DOI":"10.1016\/j.media.2025.103569","article-title":"Lightweight multi-stage aggregation transformer for robust medical image segmentation","author":"Wang","year":"2025","journal-title":"Med. Image Anal."},{"key":"10.1016\/j.media.2026.104224_b66","series-title":"International Conference on Medical Image Computing and Computer-Assisted Intervention","first-page":"578","article-title":"Segmamba: Long-range sequential modeling mamba for 3d medical image segmentation","author":"Xing","year":"2024"},{"key":"10.1016\/j.media.2026.104224_b67","doi-asserted-by":"crossref","DOI":"10.1016\/j.compmedimag.2024.102370","article-title":"MEF-UNet: An end-to-end ultrasound image segmentation algorithm based on multi-scale feature extraction and fusion","volume":"114","author":"Xu","year":"2024","journal-title":"Comput. Med. Imaging Graph."},{"key":"10.1016\/j.media.2026.104224_b68","doi-asserted-by":"crossref","DOI":"10.1016\/j.bspc.2025.109406","article-title":"ANNet: Adaptive nonlinear network for medical image segmentation with brightness balancing and noise suppression","volume":"115","author":"Yang","year":"2026","journal-title":"Biomed. Signal Process. Control."},{"issue":"4","key":"10.1016\/j.media.2026.104224_b69","doi-asserted-by":"crossref","first-page":"1218","DOI":"10.1109\/JBHI.2017.2731873","article-title":"Automated breast ultrasound lesions detection using convolutional neural networks","volume":"22","author":"Yap","year":"2017","journal-title":"IEEE J. Biomed. Health Informatics"},{"key":"10.1016\/j.media.2026.104224_b70","doi-asserted-by":"crossref","DOI":"10.1016\/j.bspc.2023.105427","article-title":"HAU-Net: Hybrid CNN-transformer for breast ultrasound image segmentation","volume":"87","author":"Zhang","year":"2024","journal-title":"Biomed. Signal Process. Control."},{"key":"10.1016\/j.media.2026.104224_b71","series-title":"Healthcare","first-page":"729","article-title":"BUSIS: a benchmark for breast ultrasound image segmentation","volume":"Vol. 10","author":"Zhang","year":"2022"},{"key":"10.1016\/j.media.2026.104224_b72","doi-asserted-by":"crossref","unstructured":"Zhang, X., Zhou, X., Lin, M., Sun, J., 2018. Shufflenet: An extremely efficient convolutional neural network for mobile devices. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp. 6848\u20136856.","DOI":"10.1109\/CVPR.2018.00716"},{"key":"10.1016\/j.media.2026.104224_b73","doi-asserted-by":"crossref","first-page":"4036","DOI":"10.1109\/TIP.2023.3293771","article-title":"Nnformer: Volumetric medical image segmentation via a 3d transformer","volume":"32","author":"Zhou","year":"2023","journal-title":"IEEE Trans. Image Process."},{"key":"10.1016\/j.media.2026.104224_b74","series-title":"International Workshop on Deep Learning in Medical Image Analysis","first-page":"3","article-title":"Unet++: A nested u-net architecture for medical image segmentation","author":"Zhou","year":"2018"}],"container-title":["Medical Image Analysis"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1361841526002938?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1361841526002938?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,8,4]],"date-time":"2026-08-04T06:54:37Z","timestamp":1785826477000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S1361841526002938"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,9]]},"references-count":74,"alternative-id":["S1361841526002938"],"URL":"https:\/\/doi.org\/10.1016\/j.media.2026.104224","relation":{},"ISSN":["1361-8415"],"issn-type":[{"value":"1361-8415","type":"print"}],"subject":[],"published":{"date-parts":[[2026,9]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"SDENet: Shallow suppression and deep enhancement network for general ultrasound image segmentation","name":"articletitle","label":"Article Title"},{"value":"Medical Image Analysis","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.media.2026.104224","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 Elsevier B.V. All rights are reserved, including those for text and data mining, AI training, and similar technologies.","name":"copyright","label":"Copyright"}],"article-number":"104224"}}