{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,21]],"date-time":"2026-03-21T19:57:47Z","timestamp":1774123067129,"version":"3.50.1"},"reference-count":44,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T00:00:00Z","timestamp":1777593600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T00:00:00Z","timestamp":1777593600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T00:00:00Z","timestamp":1777593600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T00:00:00Z","timestamp":1777593600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T00:00:00Z","timestamp":1777593600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T00:00:00Z","timestamp":1777593600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T00:00:00Z","timestamp":1777593600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Pathology - Research and Practice"],"published-print":{"date-parts":[[2026,5]]},"DOI":"10.1016\/j.prp.2026.156403","type":"journal-article","created":{"date-parts":[[2026,2,14]],"date-time":"2026-02-14T15:53:12Z","timestamp":1771084392000},"page":"156403","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"C","title":["H-DCA Net: Hierarchical dual-branch coordinate attention framework for multi-scale gastric histopathology diagnosis"],"prefix":"10.1016","volume":"281","author":[{"given":"Changyu","family":"Li","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wei","family":"Liu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"78","reference":[{"key":"10.1016\/j.prp.2026.156403_bib1","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":"11","key":"10.1016\/j.prp.2026.156403_bib2","doi-asserted-by":"crossref","first-page":"303","DOI":"10.4251\/wjgo.v7.i11.303","article-title":"Gastric cancer: the times they are a-changin\u2019","volume":"7","author":"Satolli","year":"2015","journal-title":"World J. Gastrointest. Oncol."},{"issue":"10494","key":"10.1016\/j.prp.2026.156403_bib3","doi-asserted-by":"crossref","first-page":"2087","DOI":"10.1016\/S0140-6736(25)00052-2","article-title":"\"Gastric cancer","volume":"405","author":"Sundar","year":"2025","journal-title":"Lancet"},{"key":"10.1016\/j.prp.2026.156403_bib4","first-page":"1","article-title":"A state-of-the-art review for gastric histopathology image analysis approaches and future development","author":"Ai","year":"2021","journal-title":"BioMed. Res. Int."},{"key":"10.1016\/j.prp.2026.156403_bib5","doi-asserted-by":"crossref","first-page":"76321","DOI":"10.1109\/ACCESS.2023.3297441","article-title":"Computer aided diagnosis for gastrointestinal cancer classification using hybrid rice optimization with deep learning","volume":"11","author":"Mirza","year":"2023","journal-title":"IEEE Access"},{"key":"10.1016\/j.prp.2026.156403_bib6","doi-asserted-by":"crossref","unstructured":"Liu, B., Zhang, M., Guo, T. and Cheng, Y. (2018) Classification of Gastric Slices Based on Deep Learning and Sparse Representation. 2018 Chinese Control and Decision Conference (CCDC), Shenyang, 9-11 June 2018, 1825-1829.","DOI":"10.1109\/CCDC.2018.8407423"},{"issue":"1","key":"10.1016\/j.prp.2026.156403_bib7","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s12065-020-00540-3","article-title":"Convolutional neural networks in medical image understanding: a survey","volume":"15","author":"Sarvamangala","year":"2022","journal-title":"Evol. Intel."},{"key":"10.1016\/j.prp.2026.156403_bib8","doi-asserted-by":"crossref","unstructured":"Cascianelli, S., et al.: Dimensionality reduction strategies for CNN-based classification of histopathological images. In: Intelligent Interactive Multimedia Systems and Services 2017, pp. 21\u201330.","DOI":"10.1007\/978-3-319-59480-4_3"},{"key":"10.1016\/j.prp.2026.156403_bib9","doi-asserted-by":"crossref","first-page":"4426","DOI":"10.3390\/s22124426","article-title":"Performance analysis of state-of-the-art CNN architectures for LUNA16","volume":"22","author":"Naseer","year":"2022","journal-title":"Sensors"},{"issue":"1","key":"10.1016\/j.prp.2026.156403_bib10","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pone.0262128","article-title":"Multi-channel convolutional neural network architectures for thyroid cancer detection","volume":"17","author":"Zhang","year":"2022","journal-title":"PLoS ONE"},{"key":"10.1016\/j.prp.2026.156403_bib11","doi-asserted-by":"crossref","first-page":"1337","DOI":"10.1007\/s12065-020-00367-y","article-title":"Improved convolutional neural network based histopathological image classification","volume":"14","author":"Rachapudi","year":"2021","journal-title":"Evol. Intel."},{"key":"10.1016\/j.prp.2026.156403_bib12","doi-asserted-by":"crossref","first-page":"9717","DOI":"10.1007\/s10489-021-02886-2","article-title":"A hierarchical conditional random field-based attention mechanism approach for gastric histopathology image classification","volume":"52","author":"Li","year":"2022","journal-title":"Appl. Intell."},{"key":"10.1016\/j.prp.2026.156403_bib13","article-title":"HCCANet: histopathological image grading of colorectal cancer using CNN based on multichannel fusion attention mechanism","volume":"12","author":"Zhou","year":"2022","journal-title":"Sci. Rep."},{"key":"10.1016\/j.prp.2026.156403_bib14","doi-asserted-by":"crossref","first-page":"176","DOI":"10.1186\/s12911-022-01919-1","article-title":"Classification of multi-differentiated liver cancer pathological images based on deep learning attention mechanism","volume":"22","author":"Chen","year":"2022","journal-title":"BMC Med Inf. Decis. Mak."},{"issue":"1","key":"10.1016\/j.prp.2026.156403_bib15","doi-asserted-by":"crossref","first-page":"297","DOI":"10.1109\/JBHI.2023.3322387","article-title":"\"Cross-scale fusion transformer for histopathological image classification","volume":"28","author":"Huang","year":"2024","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"10.1016\/j.prp.2026.156403_bib16","doi-asserted-by":"crossref","unstructured":"Y. Zou, S. Chen, Q. Sun, B. Liu and J. Zhang, \"DCET-Net: Dual-Stream Convolution Expanded Transformer for Breast Cancer Histopathological Image Classification,\" 2021 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Houston, TX, USA, 2021, pp. 1235-1240, doi: 10.1109\/BIBM52615.2021.9669903.","DOI":"10.1109\/BIBM52615.2021.9669903"},{"key":"10.1016\/j.prp.2026.156403_bib17","doi-asserted-by":"crossref","DOI":"10.1016\/j.bspc.2024.106998","article-title":"SAFP-Net: a spatially augmented feature pyramid network for automatic detection of microaneurysms","volume":"100","author":"Xu","year":"2025","journal-title":"Biomed. Signal Process. Control"},{"key":"10.1016\/j.prp.2026.156403_bib18","doi-asserted-by":"crossref","DOI":"10.1016\/j.bspc.2025.107949","article-title":"\"ADCFormer: a hybrid model of adaptive depth-wise convolution and transformer for retinal macular edema segmentation in OCT images","volume":"108","author":"Xu","year":"2025","journal-title":"Biomed. Signal Process. Control"},{"key":"10.1016\/j.prp.2026.156403_bib19","doi-asserted-by":"crossref","DOI":"10.1016\/j.bspc.2025.109063","article-title":"SSR-Mam2MIL: spatial sequence reordering Mamba2 based multiple instance learning for computational pathology","volume":"113","author":"Zhang","year":"2026","journal-title":"Biomed. Signal Process. Control"},{"key":"10.1016\/j.prp.2026.156403_bib20","doi-asserted-by":"crossref","DOI":"10.1016\/j.prp.2023.154694","article-title":"\"Optimized detection and segmentation of nuclei in gastric cancer images using stain normalization and blurred artifact removal","volume":"248","author":"Martos","year":"2023","journal-title":"Pathol. Res. Pract."},{"key":"10.1016\/j.prp.2026.156403_bib21","doi-asserted-by":"crossref","DOI":"10.1016\/j.prp.2025.156246","article-title":"\"An interpretable machine learning framework for automated mitosis detection in gastrointestinal stromal tumors","author":"Dong","year":"2025","journal-title":"Pathol. Res. Pract."},{"key":"10.1016\/j.prp.2026.156403_bib22","doi-asserted-by":"crossref","DOI":"10.1016\/j.compbiomed.2021.105207","article-title":"GasHisSDB: a new gastric histopathology image dataset for computer aided diagnosis of gastric cancer","volume":"142","author":"Hu","year":"2022","journal-title":"Comput. Biol. Med."},{"issue":"10","key":"10.1016\/j.prp.2026.156403_bib23","first-page":"5","article-title":"Gastric cancer detection with ensemble learning on digital pathology: use case of gastric cancer on GasHisSDB dataset","volume":"13","author":"Mudavadkar","year":"2023","journal-title":"J. ]. Diagn."},{"key":"10.1016\/j.prp.2026.156403_bib24","first-page":"10096","article-title":"Efficientnetv2: smaller models and faster training[C]\/\/International conference on machine learning","author":"Tan","year":"2021","journal-title":"PMLR"},{"issue":"01","key":"10.1016\/j.prp.2026.156403_bib25","first-page":"8368","article-title":"Depthwise convolution is all you need for learning multiple visual domains[C]","volume":"33","author":"Guo","year":"2019","journal-title":"Proc. AAAI Conf. Artif. Intell."},{"key":"10.1016\/j.prp.2026.156403_bib26","doi-asserted-by":"crossref","unstructured":"Hu, J., Shen, L., Sun, G.: Squeeze-and-excitation networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 7132\u20137141 (2018).","DOI":"10.1109\/CVPR.2018.00745"},{"issue":"8","key":"10.1016\/j.prp.2026.156403_bib27","doi-asserted-by":"crossref","first-page":"a026583","DOI":"10.1101\/cshperspect.a026583","article-title":"Spatial heterogeneity in the tumor microenvironment[J]","volume":"6","author":"Yuan","year":"2016","journal-title":"Cold Spring Harb. Perspect. Med."},{"key":"10.1016\/j.prp.2026.156403_bib28","first-page":"125","article-title":"Histopathological gastric cancer detection using transfer learning","author":"Yong","year":"2023","journal-title":"202311th Int. Conf. Bioinforma. Comput. Biol. (ICBCB)"},{"issue":"6","key":"10.1016\/j.prp.2026.156403_bib29","article-title":"Performance evaluation in non-intrusive load monitoring: datasets, metrics, and tools\u2014A review","volume":"8","author":"Pereira","year":"2018","journal-title":"J. ]. Wiley Interdiscip. Rev. data Min. Knowl. Discov."},{"key":"10.1016\/j.prp.2026.156403_bib30","unstructured":"Yurdakul, M., & Tasdemir, S. (2025). CoAtNeXt: An Attention-Enhanced ConvNeXtV2-Transformer Hybrid Model for Gastric Tissue Classification. arXiv preprint arXiv:2509.09242."},{"key":"10.1016\/j.prp.2026.156403_bib31","unstructured":"K. Simonyan, A. Zisserman, Very deep convolutional networks for large-scale image recognition, 2014, arXiv preprint arXiv:1409.1556."},{"key":"10.1016\/j.prp.2026.156403_bib32","first-page":"2818","article-title":"Rethinking the inception architecture for computer vision, in: proceedings of","author":"Szegedy","year":"2016","journal-title":"IEEE Conf. Comput. Vis. Pattern Recognit."},{"key":"10.1016\/j.prp.2026.156403_bib33","unstructured":"Dosovitskiy, A. (2020). An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929."},{"key":"10.1016\/j.prp.2026.156403_bib34","doi-asserted-by":"crossref","unstructured":"Howard, M. Sandler, G. Chu, L.-C. Chen, B. Chen, M. Tan, W. Wang, Y. Zhu, R. Pang, V. Vasudevan, et al., Searching for mobilenetv3, in: Proceedings of the IEEE\/CVF International Conference on Computer Vision, 2019, pp. 1314\u20131324.","DOI":"10.1109\/ICCV.2019.00140"},{"key":"10.1016\/j.prp.2026.156403_bib35","series-title":"in: International Conference on Machine Learning","first-page":"10096","article-title":"Efficientnetv2: smaller models and faster training","author":"Tan","year":"2021"},{"key":"10.1016\/j.prp.2026.156403_bib36","first-page":"10012","article-title":"Swin transformer: hierarchical vision transformer using shifted windows[C]","author":"Liu","year":"2021","journal-title":"Proc. IEEE\/CVF Int. Conf. Comput. Vis."},{"key":"10.1016\/j.prp.2026.156403_bib37","first-page":"3965","article-title":"Coatnet: marrying convolution and attention for all data sizes[J]","volume":"34","author":"Dai","year":"2021","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"10.1016\/j.prp.2026.156403_bib38","doi-asserted-by":"crossref","unstructured":"Yong M.P., Hum Y.C., Lai K.W., et al. Histopathological gastric cancer detection using transfer learning[C]\/\/2023 11th International Conference on Bioinformatics and Computational Biology (ICBCB). IEEE, 2023: 123-129.","DOI":"10.1109\/ICBCB57893.2023.10246524"},{"key":"10.1016\/j.prp.2026.156403_bib39","doi-asserted-by":"crossref","DOI":"10.1016\/j.bspc.2022.104319","article-title":"MCLNet: an multidimensional convolutional lightweight network for gastric histopathology image classification[J]","volume":"80","author":"Fu","year":"2023","journal-title":"Biomed. Signal Process. Control"},{"key":"10.1016\/j.prp.2026.156403_bib40","doi-asserted-by":"crossref","unstructured":"Liu W., Yu W. Deep Feature Adaptive Fusion Algorithm for Breast Cancer Histopathology Image Classification[C]\/\/2024 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE, 2024: 5507-5514.","DOI":"10.1109\/BIBM62325.2024.10822468"},{"issue":"10","key":"10.1016\/j.prp.2026.156403_bib41","doi-asserted-by":"crossref","first-page":"1793","DOI":"10.3390\/diagnostics13101793","article-title":"Histopathological gastric cancer detection on GasHisSDB dataset using deep ensemble learning[J]","volume":"13","author":"Yong","year":"2023","journal-title":"Diagnostics"},{"key":"10.1016\/j.prp.2026.156403_bib42","doi-asserted-by":"crossref","unstructured":"Selvaraju, R.R.; Cogswell, M.; Das, A.; Vedantam, R.; Parikh, D.; Batra, D. Grad-cam: Visual explanations from deep networks via gradient-based localization. In Proceedings of the IEEE International Conference on Computer Vision, Venice, Italy, 22\u201329 October 2017; pp. 618\u2013626.","DOI":"10.1109\/ICCV.2017.74"},{"issue":"S1","key":"10.1016\/j.prp.2026.156403_bib43","doi-asserted-by":"crossref","first-page":"1857","DOI":"10.53730\/ijhs.v6nS1.4952","article-title":"Transfer learning based classification of MSI and MSS gastrointestinal cancer","volume":"6","author":"Khan","year":"2022","journal-title":"Int. J. Health Sci."},{"key":"10.1016\/j.prp.2026.156403_bib44","doi-asserted-by":"crossref","unstructured":"Sai Venkatesh, C.H., et al. \"Modified resnetmodel for msi and mss classification of gastrointestinal cancer.\" High Performance Computing and Networking: Select Proceedings of CHSN 2021. Singapore: Springer Singapore, 2022. 273-282.","DOI":"10.1007\/978-981-16-9885-9_23"}],"container-title":["Pathology - Research and Practice"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0344033826000543?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0344033826000543?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,3,21]],"date-time":"2026-03-21T19:23:50Z","timestamp":1774121030000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0344033826000543"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,5]]},"references-count":44,"alternative-id":["S0344033826000543"],"URL":"https:\/\/doi.org\/10.1016\/j.prp.2026.156403","relation":{},"ISSN":["0344-0338"],"issn-type":[{"value":"0344-0338","type":"print"}],"subject":[],"published":{"date-parts":[[2026,5]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"H-DCA Net: Hierarchical dual-branch coordinate attention framework for multi-scale gastric histopathology diagnosis","name":"articletitle","label":"Article Title"},{"value":"Pathology - Research and Practice","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.prp.2026.156403","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 Elsevier GmbH. All rights are reserved, including those for text and data mining, AI training, and similar technologies.","name":"copyright","label":"Copyright"}],"article-number":"156403"}}