{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,4]],"date-time":"2026-07-04T02:22:01Z","timestamp":1783131721310,"version":"3.54.6"},"reference-count":61,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"funder":[{"DOI":"10.13039\/501100012226","name":"Fundamental Research Funds for the Central Universities","doi-asserted-by":"publisher","award":["DUT24YG129"],"award-info":[{"award-number":["DUT24YG129"]}],"id":[{"id":"10.13039\/501100012226","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61972440"],"award-info":[{"award-number":["61972440"]}],"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":["62372079"],"award-info":[{"award-number":["62372079"]}],"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":["61572101"],"award-info":[{"award-number":["61572101"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2025YFE0116000"],"award-info":[{"award-number":["2025YFE0116000"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100005047","name":"Liaoning Provincial Natural Science Foundation","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100005047","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Applied Soft Computing"],"published-print":{"date-parts":[[2026,7]]},"DOI":"10.1016\/j.asoc.2026.115283","type":"journal-article","created":{"date-parts":[[2026,4,20]],"date-time":"2026-04-20T23:14:40Z","timestamp":1776726880000},"page":"115283","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"C","title":["MTGUSC-Net: A multi-task gastric ultrasound segmentation and classification network via multi-scale attention feature fusion and adaptive multi-scale pooling"],"prefix":"10.1016","volume":"198","author":[{"given":"Mustafain","family":"Rehman","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5533-3038","authenticated-orcid":false,"given":"Zhijun","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Miao","family":"Fan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5404-4808","authenticated-orcid":false,"given":"Ahsan","family":"Humayun","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"JinHao","family":"Yu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-0721-1291","authenticated-orcid":false,"given":"KeMeng","family":"Cui","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"ShiJun","family":"Su","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wei","family":"Yang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-9349-9395","authenticated-orcid":false,"given":"Zhi","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1072-6601","authenticated-orcid":false,"given":"Bin","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"issue":"1","key":"10.1016\/j.asoc.2026.115283_bib1","doi-asserted-by":"crossref","first-page":"335","DOI":"10.1053\/j.gastro.2020.02.068","article-title":"Global burden of 5 major types of gastrointestinal cancer","volume":"159","author":"Arnold","year":"2020","journal-title":"Gastroenterology"},{"issue":"3","key":"10.1016\/j.asoc.2026.115283_bib2","first-page":"209","article-title":"Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries","volume":"71","author":"Sung","year":"2021","journal-title":"CA Cancer J. Clin."},{"issue":"3","key":"10.1016\/j.asoc.2026.115283_bib3","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."},{"issue":"4","key":"10.1016\/j.asoc.2026.115283_bib4","doi-asserted-by":"crossref","first-page":"1535","DOI":"10.1016\/j.bbe.2020.09.008","article-title":"Gastric histopathology image segmentation using a hierarchical conditional random field","volume":"40","author":"Sun","year":"2020","journal-title":"Biocybern. Biomed. Eng."},{"issue":"11","key":"10.1016\/j.asoc.2026.115283_bib5","doi-asserted-by":"crossref","first-page":"1493","DOI":"10.1016\/S1470-2045(19)30456-5","article-title":"Progress in cancer survival, mortality, and incidence in seven high-income countries 1995\u20132014 (ICBP SURVMARK-2): a population-based study","volume":"20","author":"Arnold","year":"2019","journal-title":"Lancet Oncol."},{"issue":"3","key":"10.1016\/j.asoc.2026.115283_bib6","first-page":"264","article-title":"Current treatment and recent progress in gastric cancer","volume":"71","author":"Joshi","year":"2021","journal-title":"CA Cancer J. Clin."},{"key":"10.1016\/j.asoc.2026.115283_bib7","article-title":"GCLDNet: gastric cancer lesion detection network combining level feature aggregation and attention feature fusion","volume":"12","author":"Shi","year":"2022","journal-title":"Front. Oncol."},{"key":"10.1016\/j.asoc.2026.115283_bib8","doi-asserted-by":"crossref","DOI":"10.1016\/j.cmpb.2023.107397","article-title":"Early gastric cancer segmentation in gastroscopic images using a co-spatial attention and channel attention based triple-branch ResUnet","volume":"231","author":"Du","year":"2023","journal-title":"Comput. Methods Prog. Biomed."},{"issue":"8","key":"10.1016\/j.asoc.2026.115283_bib9","doi-asserted-by":"crossref","DOI":"10.1001\/jamanetworkopen.2021.21403","article-title":"Estimated cost-effectiveness of endoscopic screening for upper gastrointestinal tract cancer in high-risk areas in China","volume":"4","author":"Xia","year":"2021","journal-title":"JAMA Netw. Open"},{"issue":"13","key":"10.1016\/j.asoc.2026.115283_bib10","doi-asserted-by":"crossref","first-page":"3026","DOI":"10.1016\/j.cgh.2020.07.031","article-title":"Endoscopy for gastric cancer screening is cost effective for Asian Americans in the United States","volume":"18","author":"Shah","year":"2020","journal-title":"Clin. Gastroenterol. Hepatol."},{"issue":"2","key":"10.1016\/j.asoc.2026.115283_bib11","doi-asserted-by":"crossref","first-page":"70","DOI":"10.1111\/1751-2980.13173","article-title":"Clinical guideline on magnetically controlled capsule gastroscopy (2021 edition)","volume":"24","author":"Jiang","year":"2023","journal-title":"J. Dig. Dis."},{"issue":"5","key":"10.1016\/j.asoc.2026.115283_bib12","doi-asserted-by":"crossref","first-page":"745","DOI":"10.1016\/j.gie.2019.06.044","article-title":"Using a deep learning system in endoscopy for screening of early esophageal squamous cell carcinoma (with video)","volume":"90","author":"Cai","year":"2019","journal-title":"Gastrointest. Endosc."},{"issue":"8","key":"10.1016\/j.asoc.2026.115283_bib13","doi-asserted-by":"crossref","first-page":"741","DOI":"10.1007\/s00535-023-02001-x","article-title":"Computer-aided demarcation of early gastric cancer: a pilot comparative study with endoscopists","volume":"58","author":"Takemoto","year":"2023","journal-title":"J. Gastroenterol."},{"issue":"05","key":"10.1016\/j.asoc.2026.115283_bib14","doi-asserted-by":"crossref","first-page":"469","DOI":"10.1055\/a-1229-0920","article-title":"A deep learning-based system for identifying differentiation status and delineating the margins of early gastric cancer in magnifying narrow-band imaging endoscopy","volume":"53","author":"Ling","year":"2021","journal-title":"Endoscopy"},{"key":"10.1016\/j.asoc.2026.115283_bib15","doi-asserted-by":"crossref","first-page":"884","DOI":"10.1007\/s10120-020-01071-7","article-title":"A deep learning method for delineating early gastric cancer resection margin under chromoendoscopy and white light endoscopy","volume":"23","author":"An","year":"2020","journal-title":"Gastric Cancer"},{"issue":"1","key":"10.1016\/j.asoc.2026.115283_bib16","doi-asserted-by":"crossref","DOI":"10.1136\/bmjgast-2021-000688","article-title":"Complications of diagnostic upper Gastrointestinal endoscopy: common and rare\u2013recognition, assessment and management","volume":"9","author":"Waddingham","year":"2022","journal-title":"BMJ Open Gastroenterol."},{"key":"10.1016\/j.asoc.2026.115283_bib17","first-page":"1","article-title":"Adaptive box-level supervision with superpixel shape guidance for ultrasound image segmentation","author":"Chi","year":"2025","journal-title":"Vis. Comput."},{"key":"10.1016\/j.asoc.2026.115283_bib18","doi-asserted-by":"crossref","DOI":"10.3389\/fonc.2021.627556","article-title":"Automatic detection of gastric wall structure based on oral contrast-enhanced ultrasound and its application on tumor screening","volume":"11","author":"Sui","year":"2021","journal-title":"Front. Oncol."},{"key":"10.1016\/j.asoc.2026.115283_bib19","series-title":"Medical Image Computing and Computer-Assisted Intervention\u2013MICCAI 2015: 18th International Conference, Munich, Germany, October 5-9, 2015, Proceedings","first-page":"234","article-title":"U-net: Convolutional networks for biomedical image segmentation","author":"Ronneberger","year":"2015"},{"key":"10.1016\/j.asoc.2026.115283_bib20","unstructured":"O. Oktay et al., \u201cAttention u-net: Learning where to look for the pancreas,\u201d arXiv Prepr. arXiv1804.03999, 2018."},{"key":"10.1016\/j.asoc.2026.115283_bib21","unstructured":"J. Chen et al., \u201cTransunet: Transformers make strong encoders for medical image segmentation,\u201d arXiv Prepr. arXiv2102.04306, 2021."},{"key":"10.1016\/j.asoc.2026.115283_bib22","series-title":"International MICCAI brainlesion workshop","first-page":"272","article-title":"Swin unetr: Swin transformers for semantic segmentation of brain tumors in mri images","author":"Hatamizadeh","year":"2021"},{"key":"10.1016\/j.asoc.2026.115283_bib23","doi-asserted-by":"crossref","first-page":"40496","DOI":"10.1109\/ACCESS.2021.3063716","article-title":"Real-time polyp detection, localization and segmentation in colonoscopy using deep learning","volume":"9","author":"Jha","year":"2021","journal-title":"IEEE Access"},{"key":"10.1016\/j.asoc.2026.115283_bib24","doi-asserted-by":"crossref","DOI":"10.1016\/j.compbiomed.2023.106626","article-title":"DCSAU-Net: a deeper and more compact split-attention U-Net for medical image segmentation","volume":"154","author":"Xu","year":"2023","journal-title":"Comput. Biol. Med."},{"issue":"1","key":"10.1016\/j.asoc.2026.115283_bib25","doi-asserted-by":"crossref","first-page":"241","DOI":"10.1186\/s12880-024-01401-6","article-title":"Medical image analysis using improved SAM-Med2D: segmentation and classification perspectives","volume":"24","author":"Sun","year":"2024","journal-title":"BMC Med. Imaging"},{"key":"10.1016\/j.asoc.2026.115283_bib26","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"},{"issue":"6","key":"10.1016\/j.asoc.2026.115283_bib27","doi-asserted-by":"crossref","first-page":"833","DOI":"10.1016\/j.acra.2018.11.006","article-title":"Perceptual and interpretive error in diagnostic radiology\u2014causes and potential solutions","volume":"26","author":"Degnan","year":"2019","journal-title":"Acad. Radio."},{"issue":"11","key":"10.1016\/j.asoc.2026.115283_bib28","doi-asserted-by":"crossref","first-page":"8135","DOI":"10.1109\/TNNLS.2022.3152527","article-title":"Learning from noisy labels with deep neural networks: A survey","volume":"34","author":"Song","year":"2022","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"issue":"4","key":"10.1016\/j.asoc.2026.115283_bib29","doi-asserted-by":"crossref","first-page":"1133","DOI":"10.1109\/TCE.2023.3293841","article-title":"Dynamic label smoothing and semantic transport for unsupervised domain adaptation on object recognition","volume":"69","author":"Ding","year":"2023","journal-title":"IEEE Trans. Consum. Electron"},{"issue":"1","key":"10.1016\/j.asoc.2026.115283_bib30","doi-asserted-by":"crossref","first-page":"1473","DOI":"10.1109\/TCE.2024.3474037","article-title":"Learning from AI-generated annotations for medical image segmentation","volume":"71","author":"Song","year":"2024","journal-title":"IEEE Trans. Consum. Electron"},{"key":"10.1016\/j.asoc.2026.115283_bib31","doi-asserted-by":"crossref","DOI":"10.1016\/j.patcog.2023.109400","article-title":"Learning from multiple annotators for medical image segmentation","volume":"138","author":"Zhang","year":"2023","journal-title":"Pattern Recognit."},{"issue":"1","key":"10.1016\/j.asoc.2026.115283_bib32","doi-asserted-by":"crossref","first-page":"16875","DOI":"10.1038\/s41598-023-43864-7","article-title":"\u201cStochastic co-teaching for training neural networks with unknown levels of label noise,\u201d","volume":"13","author":"de Vos","year":"2023","journal-title":"Sci. Rep."},{"issue":"6","key":"10.1016\/j.asoc.2026.115283_bib33","doi-asserted-by":"crossref","first-page":"8011","DOI":"10.1007\/s40747-024-01574-1","article-title":"A teacher-guided early-learning method for medical image segmentation from noisy labels","volume":"10","author":"Liu","year":"2024","journal-title":"Complex Intell. Syst."},{"key":"10.1016\/j.asoc.2026.115283_bib34","doi-asserted-by":"crossref","DOI":"10.1016\/j.compbiomed.2023.107866","article-title":"SaB-Net: self-attention backward network for gastric tumor segmentation in CT images","volume":"169","author":"He","year":"2024","journal-title":"Comput. Biol. Med."},{"issue":"1","key":"10.1016\/j.asoc.2026.115283_bib35","doi-asserted-by":"crossref","first-page":"63","DOI":"10.1186\/s40644-024-00711-w","article-title":"HCA-DAN: hierarchical class-aware domain adaptive network for gastric tumor segmentation in 3D CT images","volume":"24","author":"Yuan","year":"2024","journal-title":"Cancer Imaging"},{"key":"10.1016\/j.asoc.2026.115283_bib36","doi-asserted-by":"crossref","DOI":"10.1016\/j.bspc.2024.106500","article-title":"ALIEN: Attention-guided cross-resolution collaborative network for 3D gastric cancer segmentation in CT images","volume":"96","author":"Chen","year":"2024","journal-title":"Biomed. Signal Process. Control"},{"issue":"1","key":"10.1016\/j.asoc.2026.115283_bib37","doi-asserted-by":"crossref","DOI":"10.1002\/acm2.14233","article-title":"Medical image segmentation of gastric adenocarcinoma based on dense connection of residuals","volume":"25","author":"Hu","year":"2024","journal-title":"J. Appl. Clin. Med. Phys."},{"issue":"3","key":"10.1016\/j.asoc.2026.115283_bib38","doi-asserted-by":"crossref","first-page":"915","DOI":"10.1007\/s00371-021-02374-1","article-title":"Multi-scale boundary neural network for gastric tumor segmentation","volume":"39","author":"Wang","year":"2023","journal-title":"Vis. Comput."},{"key":"10.1016\/j.asoc.2026.115283_bib39","doi-asserted-by":"crossref","DOI":"10.1016\/j.neucom.2024.128228","article-title":"A two-stage image enhancement and dynamic feature aggregation framework for gastroscopy image segmentation","volume":"601","author":"He","year":"2024","journal-title":"Neurocomputing"},{"issue":"1","key":"10.1016\/j.asoc.2026.115283_bib40","doi-asserted-by":"crossref","first-page":"7847","DOI":"10.1038\/s41598-024-58361-8","article-title":"Early gastric cancer detection and lesion segmentation based on deep learning and gastroscopic images","volume":"14","author":"Zhang","year":"2024","journal-title":"Sci. Rep."},{"issue":"1","key":"10.1016\/j.asoc.2026.115283_bib41","doi-asserted-by":"crossref","first-page":"6377","DOI":"10.1038\/s41598-023-33462-y","article-title":"Dual-branch hybrid network for lesion segmentation in gastric cancer images","volume":"13","author":"He","year":"2023","journal-title":"Sci. Rep."},{"key":"10.1016\/j.asoc.2026.115283_bib42","article-title":"Dual\u2011branch fully convolutional segment anything model for lesion segmentation in endoscopic images","author":"He","year":"2024","journal-title":"IEEE Access"},{"issue":"4","key":"10.1016\/j.asoc.2026.115283_bib43","doi-asserted-by":"crossref","first-page":"3041","DOI":"10.1007\/s40747-021-00328-7","article-title":"3D-semantic segmentation and classification of stomach infections using uncertainty aware deep neural networks","volume":"8","author":"Amin","year":"2022","journal-title":"Complex Intell. Syst."},{"issue":"2","key":"10.1016\/j.asoc.2026.115283_bib44","doi-asserted-by":"crossref","first-page":"258","DOI":"10.1016\/j.gie.2021.08.022","article-title":"Deep learning model for diagnosing gastric mucosal lesions using endoscopic images: development, validation, and method comparison","volume":"95","author":"Nam","year":"2022","journal-title":"Gastrointest. Endosc."},{"issue":"5","key":"10.1016\/j.asoc.2026.115283_bib45","doi-asserted-by":"crossref","first-page":"788","DOI":"10.1111\/den.13844","article-title":"Diagnosis of gastric lesions through a deep convolutional neural network","volume":"33","author":"Zhang","year":"2021","journal-title":"Dig. Endosc."},{"issue":"1","key":"10.1016\/j.asoc.2026.115283_bib46","doi-asserted-by":"crossref","first-page":"22533","DOI":"10.1038\/s41598-024-73823-9","article-title":"Enhanced gastric cancer classification and quantification interpretable framework using digital histopathology images","volume":"14","author":"Zubair","year":"2024","journal-title":"Sci. Rep."},{"key":"10.1016\/j.asoc.2026.115283_bib47","doi-asserted-by":"crossref","DOI":"10.1016\/j.cmpb.2021.106210","article-title":"Medical image recognition and segmentation of pathological slices of gastric cancer based on Deeplab v3+ neural network","volume":"207","author":"Wang","year":"2021","journal-title":"Comput. Methods Prog. Biomed."},{"key":"10.1016\/j.asoc.2026.115283_bib48","doi-asserted-by":"crossref","DOI":"10.1016\/j.patcog.2022.108827","article-title":"GasHis-transformer: a multi-scale visual transformer approach for gastric histopathological image detection","volume":"130","author":"Chen","year":"2022","journal-title":"Pattern Recognit."},{"issue":"22","key":"10.1016\/j.asoc.2026.115283_bib49","doi-asserted-by":"crossref","DOI":"10.1088\/1361-6560\/ae1881","article-title":"AIP-Net: an attention-integrated pyramid network for computer-aided diagnosis and segmentation of gastric lesion in ultrasound images","volume":"70","author":"Huang","year":"2025","journal-title":"Phys. Med. Biol."},{"issue":"8","key":"10.1016\/j.asoc.2026.115283_bib50","doi-asserted-by":"crossref","first-page":"6399","DOI":"10.1007\/s13369-020-04480-z","article-title":"RDA-UNET-WGAN: an accurate breast ultrasound lesion segmentation using wasserstein generative adversarial networks","volume":"45","author":"Negi","year":"2020","journal-title":"Arab. J. Sci. Eng."},{"key":"10.1016\/j.asoc.2026.115283_bib51","doi-asserted-by":"crossref","DOI":"10.1016\/j.bspc.2024.106472","article-title":"Enhanced-TransUNet for ultrasound segmentation of thyroid nodules","volume":"95","author":"Ozcan","year":"2024","journal-title":"Biomed. Signal Process. Control"},{"key":"10.1016\/j.asoc.2026.115283_bib52","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2022.119024","article-title":"CSwin-PNet: A CNN-Swin Transformer combined pyramid network for breast lesion segmentation in ultrasound images","volume":"213","author":"Yang","year":"2023","journal-title":"Expert Syst. Appl."},{"key":"10.1016\/j.asoc.2026.115283_bib53","series-title":"Chinese Conference on Pattern Recognition and Computer Vision (PRCV)","first-page":"42","article-title":"Levit-unet: Make faster encoders with transformer for medical image segmentation","author":"Xu","year":"2023"},{"key":"10.1016\/j.asoc.2026.115283_bib54","series-title":"Medical image computing and computer assisted intervention\u2013MICCAI 2021: 24th International Conference","first-page":"36","article-title":"Medical transformer: Gated axial-attention for medical image segmentation","author":"Valanarasu","year":"2021"},{"key":"10.1016\/j.asoc.2026.115283_bib55","doi-asserted-by":"crossref","DOI":"10.1016\/j.bspc.2021.102643","article-title":"Dilated MultiResUNet: dilated multiresidual blocks network based on U-Net for biomedical image segmentation","volume":"68","author":"Yang","year":"2021","journal-title":"Biomed. Signal Process. Control"},{"key":"10.1016\/j.asoc.2026.115283_bib56","doi-asserted-by":"crossref","DOI":"10.1016\/j.bspc.2022.104038","article-title":"OAU-net: Outlined Attention U-net for biomedical image segmentation","volume":"79","author":"Song","year":"2023","journal-title":"Biomed. Signal Process. Control"},{"key":"10.1016\/j.asoc.2026.115283_bib57","doi-asserted-by":"crossref","DOI":"10.1016\/j.compmedimag.2023.102311","article-title":"A lightweight multi-modality medical image semantic segmentation network base on the novel UNeXt and Wave-MLP","volume":"111","author":"He","year":"2024","journal-title":"Comput. Med. Imaging Graph"},{"key":"10.1016\/j.asoc.2026.115283_bib58","first-page":"205","article-title":"Swin-unet: Unet-like pure transformer for medical image segmentation","author":"Cao","year":"2022","journal-title":"Eur. Conf. Comput. Vis."},{"issue":"10","key":"10.1016\/j.asoc.2026.115283_bib59","doi-asserted-by":"crossref","first-page":"2281","DOI":"10.1109\/TMI.2019.2903562","article-title":"Ce-net: Context encoder network for 2D medical image segmentation","volume":"38","author":"Gu","year":"2019","journal-title":"IEEE Trans. Med. Imaging"},{"key":"10.1016\/j.asoc.2026.115283_bib60","series-title":"Proceedings of the IEEE\/CVF International Conference on Computer Vision","first-page":"4015","article-title":"Segment anything","author":"Kirillov","year":"2023"},{"key":"10.1016\/j.asoc.2026.115283_bib61","unstructured":"J. Ma et al., \u201cMedsam2: Segment anything in 3d medical images and videos,\u201d arXiv Prepr. arXiv2504.03600, 2025."}],"container-title":["Applied Soft Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1568494626007313?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1568494626007313?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,7,4]],"date-time":"2026-07-04T02:01:07Z","timestamp":1783130467000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S1568494626007313"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,7]]},"references-count":61,"alternative-id":["S1568494626007313"],"URL":"https:\/\/doi.org\/10.1016\/j.asoc.2026.115283","relation":{},"ISSN":["1568-4946"],"issn-type":[{"value":"1568-4946","type":"print"}],"subject":[],"published":{"date-parts":[[2026,7]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"MTGUSC-Net: A multi-task gastric ultrasound segmentation and classification network via multi-scale attention feature fusion and adaptive multi-scale pooling","name":"articletitle","label":"Article Title"},{"value":"Applied Soft Computing","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.asoc.2026.115283","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":"115283"}}