{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,4]],"date-time":"2026-07-04T13:20:45Z","timestamp":1783171245212,"version":"3.54.6"},"reference-count":64,"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\/501100003725","name":"National Research Foundation of Korea","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100003725","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100002701","name":"Ministry of Education","doi-asserted-by":"publisher","award":["RS-2026-25480755"],"award-info":[{"award-number":["RS-2026-25480755"]}],"id":[{"id":"10.13039\/501100002701","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Computers and Electrical Engineering"],"published-print":{"date-parts":[[2026,9]]},"DOI":"10.1016\/j.compeleceng.2026.111193","type":"journal-article","created":{"date-parts":[[2026,5,29]],"date-time":"2026-05-29T22:06:01Z","timestamp":1780092361000},"page":"111193","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"C","title":["A3FUS-Net: Towards automated Renal Cell Carcinoma grading system via advanced adaptive attention mechanism aided deep learning fusion"],"prefix":"10.1016","volume":"137","author":[{"ORCID":"https:\/\/orcid.org\/0009-0006-5916-4108","authenticated-orcid":false,"given":"Shreyan","family":"Kundu","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0652-1339","authenticated-orcid":false,"given":"Souradeep","family":"Mukhopadhyay","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Semanti","family":"Das","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5745-2460","authenticated-orcid":false,"given":"Biswadip Basu","family":"Mallik","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Daison","family":"Darlan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9071-1145","authenticated-orcid":false,"given":"Rammohan","family":"Mallipeddi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"issue":"1","key":"10.1016\/j.compeleceng.2026.111193_b1","first-page":"1","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":"16","key":"10.1016\/j.compeleceng.2026.111193_b2","first-page":"1","article-title":"Trends and projections of kidney cancer incidence at the global and national levels, 1990\u20132030: A Bayesian age-period-cohort modeling study","volume":"8","author":"Du","year":"2020","journal-title":"Biomark Res"},{"issue":"3","key":"10.1016\/j.compeleceng.2026.111193_b3","doi-asserted-by":"crossref","first-page":"409","DOI":"10.1111\/j.1464-410X.2010.09561.x","article-title":"Application of simplified fuhrman grading system in clear-cell renal cell carcinoma","volume":"107","author":"Hong","year":"2011","journal-title":"BJU Int"},{"issue":"3","key":"10.1016\/j.compeleceng.2026.111193_b4","doi-asserted-by":"crossref","first-page":"26","DOI":"10.15586\/jkcvhl.2014.11","article-title":"The ISUP system of staging, grading and classification of renal cell neoplasia","volume":"1","author":"Samaratunga","year":"2014","journal-title":"J Kidney Cancer VHL"},{"key":"10.1016\/j.compeleceng.2026.111193_b5","doi-asserted-by":"crossref","first-page":"4","DOI":"10.1111\/his.13735","article-title":"Grading of renal cell carcinoma","volume":"74","author":"Delahunt","year":"2019","journal-title":"Histopathology"},{"key":"10.1016\/j.compeleceng.2026.111193_b6","doi-asserted-by":"crossref","DOI":"10.3390\/healthcare11101493","article-title":"Automated uterine fibroids detection in ultrasound images using deep convolutional neural networks","volume":"11","author":"Shahzad","year":"2023","journal-title":"Healthcare"},{"key":"10.1016\/j.compeleceng.2026.111193_b7","doi-asserted-by":"crossref","DOI":"10.3390\/app13074255","article-title":"Enhancing ductal carcinoma classification using transfer learning with 3D U-net models in breast cancer imaging","author":"Khalil","year":"2023","journal-title":"Appl Sci"},{"key":"10.1016\/j.compeleceng.2026.111193_b8","doi-asserted-by":"crossref","first-page":"15","DOI":"10.1109\/TMI.2022.3202248","article-title":"A ViT-AMC network with adaptive model fusion and multiobjective optimization for interpretable laryngeal tumor grading from histopathological images","volume":"42","author":"Huang","year":"2022","journal-title":"IEEE Trans Med Imaging"},{"key":"10.1016\/j.compeleceng.2026.111193_b9","doi-asserted-by":"crossref","first-page":"1696","DOI":"10.1109\/JBHI.2021.3108999","article-title":"FABNet: Fusion attention block and transfer learning for laryngeal cancer tumor grading in P63 IHC histopathology images","volume":"26","author":"Huang","year":"2021","journal-title":"IEEE J Biomed Health Inform"},{"key":"10.1016\/j.compeleceng.2026.111193_b10","doi-asserted-by":"crossref","first-page":"666","DOI":"10.1007\/s12539-021-00452-5","article-title":"LPCANet: Classification of laryngeal cancer histopathological images using a CNN with position attention and channel attention mechanisms","volume":"13","author":"Zhou","year":"2021","journal-title":"Interdiscip Sci: Comput Life Sci"},{"key":"10.1016\/j.compeleceng.2026.111193_b11","doi-asserted-by":"crossref","DOI":"10.3390\/s21010122","article-title":"AF-SENet: Classification of cancer in cervical tissue pathological images based on fusing deep convolution features","volume":"21","author":"Huang","year":"2020","journal-title":"Sensors (Basel, Switzerland)"},{"key":"10.1016\/j.compeleceng.2026.111193_b12","doi-asserted-by":"crossref","first-page":"24219","DOI":"10.1109\/ACCESS.2020.2970121","article-title":"Classification of cervical biopsy images based on LASSO and EL-SVM","volume":"8","author":"Huang","year":"2020","journal-title":"IEEE Access"},{"key":"10.1016\/j.compeleceng.2026.111193_b13","first-page":"15","article-title":"ASI-DBNet: An adaptive sparse interactive ResNet-vision transformer dual-branch network for the grading of brain cancer histopathological images","volume":"15","author":"Zhou","year":"2022","journal-title":"Interdiscip Sci: Comput Life Sci"},{"key":"10.1016\/j.compeleceng.2026.111193_b14","doi-asserted-by":"crossref","first-page":"141705","DOI":"10.1109\/ACCESS.2020.3012967","article-title":"Computer-aided diagnosis and staging of pancreatic cancer based on CT images","volume":"8","author":"Li","year":"2020","journal-title":"IEEE Access"},{"key":"10.1016\/j.compeleceng.2026.111193_b15","article-title":"Interpretable laryngeal tumor grading of histopathological images via depth domain adaptive network with integration gradient CAM and priori experience-guided attention","volume":"154","author":"Huang","year":"2022","journal-title":"Comput Biol Med"},{"key":"10.1016\/j.compeleceng.2026.111193_b16","article-title":"E-TBNet: Light deep neural network for automatic detection of tuberculosis with X-ray DR imaging","volume":"22","author":"An","year":"2022","journal-title":"Sensors (Basel, Switzerland)"},{"key":"10.1016\/j.compeleceng.2026.111193_b17","doi-asserted-by":"crossref","first-page":"3557","DOI":"10.1109\/JBHI.2024.3373438","article-title":"LA-ViT: A network with transformers constrained by learned-parameter-free attention for interpretable grading in a new laryngeal histopathology image dataset","volume":"28","author":"Huang","year":"2024","journal-title":"IEEE J Biomed Health Inform"},{"key":"10.1016\/j.compeleceng.2026.111193_b18","doi-asserted-by":"crossref","DOI":"10.1016\/j.inffus.2024.102333","article-title":"MamlFormer: Priori-experience guiding transformer network via manifold adversarial multi-modal learning for laryngeal histopathological grading","volume":"108","author":"Huang","year":"2024","journal-title":"Inf Fusion"},{"key":"10.1016\/j.compeleceng.2026.111193_b19","first-page":"1","article-title":"The swin-transformer network based on focal loss is used to identify images of pathological subtypes of lung adenocarcinoma with high similarity and class imbalance","author":"Wang","year":"2023","journal-title":"J Cancer Res Clin Oncol"},{"key":"10.1016\/j.compeleceng.2026.111193_b20","first-page":"1","article-title":"DCA-DAFFNet: An end-to-end network with deformable fusion attention and deep adaptive feature fusion for laryngeal tumor grading from histopathology images","volume":"72","author":"Luo","year":"2023","journal-title":"IEEE Trans Instrum Meas"},{"key":"10.1016\/j.compeleceng.2026.111193_b21","doi-asserted-by":"crossref","DOI":"10.1016\/j.bspc.2024.107452","article-title":"CGAM: An end-to-end causality graph attention Mamba network for esophageal pathology grading","volume":"103","author":"Qu","year":"2025","journal-title":"Biomed Signal Process Control"},{"key":"10.1016\/j.compeleceng.2026.111193_b22","unstructured":"Bose S, Singha M, Jha A, Mukhopadhyay S, Banerjee B. Meta-learning to teach semantic prompts for open domain generalization in vision-language models."},{"key":"10.1016\/j.compeleceng.2026.111193_b23","doi-asserted-by":"crossref","DOI":"10.1093\/bib\/bbae570","article-title":"Towards molecular structure discovery from cryo-ET density volumes via modelling auxiliary semantic prototypes","volume":"26","author":"Nair","year":"2024","journal-title":"Brief Bioinform"},{"key":"10.1016\/j.compeleceng.2026.111193_b24","series-title":"GraphVL: Graph-enhanced semantic modeling via vision-language models for generalized class discovery","author":"Solanki","year":"2024"},{"key":"10.1016\/j.compeleceng.2026.111193_b25","doi-asserted-by":"crossref","first-page":"10509","DOI":"10.1038\/s41598-019-46718-3","article-title":"Pan-renal cell carcinoma classification and survival prediction from histopathology images using deep learning","volume":"9","author":"Tabibu","year":"2019","journal-title":"Sci Rep"},{"key":"10.1016\/j.compeleceng.2026.111193_b26","doi-asserted-by":"crossref","first-page":"7080","DOI":"10.1038\/s41598-021-86540-4","article-title":"Development and evaluation of a deep neural network for histologic classification of renal cell carcinoma on biopsy and surgical resection slides","volume":"11","author":"Zhu","year":"2021","journal-title":"Sci Rep"},{"key":"10.1016\/j.compeleceng.2026.111193_b27","doi-asserted-by":"crossref","unstructured":"Tian K, et al. Automated clear cell renal carcinoma grade classification with prognostic significance. PLoS One 14(3):2019.","DOI":"10.1371\/journal.pone.0222641"},{"key":"10.1016\/j.compeleceng.2026.111193_b28","doi-asserted-by":"crossref","unstructured":"He K, Zhang X, Ren S, Sun J. Deep residual learning for image recognition. In: IEEE conference on computer vision and pattern recognition. CVPR, Las Vegas, NV, USA; 2016, p. 770\u20138. http:\/\/dx.doi.org\/10.1109\/CVPR.2016.90.","DOI":"10.1109\/CVPR.2016.90"},{"issue":"13","key":"10.1016\/j.compeleceng.2026.111193_b29","doi-asserted-by":"crossref","first-page":"5479","DOI":"10.1007\/s00521-022-07895-x","article-title":"Deep feature selection using local search embedded social ski-driver optimization algorithm for breast cancer detection in mammograms","volume":"35","author":"Pramanik","year":"2023","journal-title":"Neural Comput Appl"},{"key":"10.1016\/j.compeleceng.2026.111193_b30","doi-asserted-by":"crossref","unstructured":"Huang G, Liu Z, van der Maaten L, Weinberger KQ. Densely connected convolutional networks. In: IEEE conference on computer vision and pattern recognition. CVPR, Honolulu, HI, USA; 2017, p. 2261\u20139. http:\/\/dx.doi.org\/10.1109\/CVPR.2017.243.","DOI":"10.1109\/CVPR.2017.243"},{"key":"10.1016\/j.compeleceng.2026.111193_b31","series-title":"Computer vision and pattern recognition","article-title":"Rethinking the inception architecture for computer vision","author":"Szegedy","year":"2015"},{"key":"10.1016\/j.compeleceng.2026.111193_b32","doi-asserted-by":"crossref","unstructured":"Szegedy C, Ioffe S, Vanhoucke V, Alemi A. Inception-v4, Inception-ResNet and the impact of residual connections on learning. In: Proceedings of the thirty-first AAAI conference on artificial intelligence, vol. 7. 2017, p. 4278\u201384.","DOI":"10.1609\/aaai.v31i1.11231"},{"key":"10.1016\/j.compeleceng.2026.111193_b33","doi-asserted-by":"crossref","first-page":"162432","DOI":"10.1109\/ACCESS.2020.3021557","article-title":"Deep learning applied for histological diagnosis of breast cancer","volume":"8","author":"Yari","year":"2020","journal-title":"IEEE Access"},{"key":"10.1016\/j.compeleceng.2026.111193_b34","first-page":"25","article-title":"Deep learning based analysis of histopathological images of breast cancer","volume":"80","author":"Juanying","year":"2019","journal-title":"Front Genet"},{"key":"10.1016\/j.compeleceng.2026.111193_b35","series-title":"2017 IEEE 2nd international conference on cloud computing and big data analysis","first-page":"348","article-title":"Deep learning model based breast cancer histopathological image classification","author":"Wei","year":"2017"},{"key":"10.1016\/j.compeleceng.2026.111193_b36","series-title":"Mathematics and its applications in new computer systems","article-title":"A deep feature selection method for tumor classification in breast ultrasound images","volume":"vol. 424","author":"Pramanik","year":"2022"},{"key":"10.1016\/j.compeleceng.2026.111193_b37","doi-asserted-by":"crossref","first-page":"46450","DOI":"10.1038\/srep46450","article-title":"Accurate and reproducible invasive breast cancer detection in whole slide images: A deep learning approach for quantifying tumor extent","volume":"7","author":"Cruz-Roa","year":"2018","journal-title":"Sci Rep"},{"key":"10.1016\/j.compeleceng.2026.111193_b38","doi-asserted-by":"crossref","first-page":"24273","DOI":"10.1109\/ACCESS.2021.3056516","article-title":"Breast cancer classification from histopathological images using patch-based deep learning modeling","volume":"9","author":"Hirra","year":"2021","journal-title":"IEEE Access"},{"issue":"3","key":"10.1016\/j.compeleceng.2026.111193_b39","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1371\/journal.pone.0214587","article-title":"Breast cancer histopathological image classification using convolutional neural networks with small SE-ResNet module","volume":"14","author":"Jiang","year":"2019","journal-title":"PLoS One"},{"key":"10.1016\/j.compeleceng.2026.111193_b40","doi-asserted-by":"crossref","DOI":"10.1016\/j.physa.2019.123592","article-title":"BreastNet: A novel convolutional neural network model through histopathological images for the diagnosis of breast cancer","volume":"545","author":"To\u011fa\u00e7ar","year":"2020","journal-title":"Phys A"},{"key":"10.1016\/j.compeleceng.2026.111193_b41","doi-asserted-by":"crossref","first-page":"1549","DOI":"10.1007\/s11548-021-02410-4","article-title":"LiverNet: efficient and robust deep learning model for automatic diagnosis of sub-types of liver hepatocellular carcinoma cancer from H&E stained liver histopathology images","volume":"16","author":"Aatresh","year":"2021","journal-title":"Int J Comput Assist Radiol Surg"},{"key":"10.1016\/j.compeleceng.2026.111193_b42","doi-asserted-by":"crossref","first-page":"15600","DOI":"10.1038\/s41598-022-19278-2","article-title":"Multiclass classification of breast cancer histopathology images using multilevel features of deep convolutional neural network","volume":"12","author":"Hameed","year":"2022","journal-title":"Sci Rep"},{"issue":"8","key":"10.1016\/j.compeleceng.2026.111193_b43","doi-asserted-by":"crossref","first-page":"2011","DOI":"10.1109\/TPAMI.2019.2913372","article-title":"Squeeze-and-excitation networks","volume":"42","author":"Hu","year":"2020","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"10.1016\/j.compeleceng.2026.111193_b44","doi-asserted-by":"crossref","unstructured":"Woo S, Park J, Lee J-Y, Kweon IS. CBAM: Convolutional Block Attention Module. In: Proceedings of the European conference on computer vision. ECCV, 2018, p. 3\u201319.","DOI":"10.1007\/978-3-030-01234-2_1"},{"key":"10.1016\/j.compeleceng.2026.111193_b45","doi-asserted-by":"crossref","unstructured":"Zhang X, Zhou X, Lin M, Sun J. ShuffleNet: An extremely efficient convolutional neural network for mobile devices. In: Proceedings of the IEEE conference on computer vision and pattern recognition. 2018, p. 6848\u201356.","DOI":"10.1109\/CVPR.2018.00716"},{"key":"10.1016\/j.compeleceng.2026.111193_b46","doi-asserted-by":"crossref","unstructured":"Ma N, Zhang X, Zheng H-T, Sun J. ShuffleNet V2: Practical guidelines for efficient CNN architecture design. In: Proceedings of the European conference on computer vision. ECCV, 2020, p. 122\u201338.","DOI":"10.1007\/978-3-030-01264-9_8"},{"key":"10.1016\/j.compeleceng.2026.111193_b47","series-title":"SA-Net: Shuffle attention for deep convolutional neural networks","author":"Zhang","year":"2021"},{"issue":"1","key":"10.1016\/j.compeleceng.2026.111193_b48","doi-asserted-by":"crossref","first-page":"917","DOI":"10.1016\/j.aej.2021.04.072","article-title":"Automatic polyp detection and segmentation using shuffle efficient channel attention network","volume":"61","author":"Yang","year":"2022","journal-title":"Alex Eng J"},{"issue":"12","key":"10.1016\/j.compeleceng.2026.111193_b49","doi-asserted-by":"crossref","first-page":"2956","DOI":"10.3390\/cancers14122956","article-title":"MVI-mind: A novel deep-learning strategy using computed tomography (CT)-based radiomics for end-to-end high-efficiency prediction of microvascular invasion in hepatocellular carcinoma","volume":"14","author":"Wang","year":"2022","journal-title":"Cancers"},{"issue":"3","key":"10.1016\/j.compeleceng.2026.111193_b50","doi-asserted-by":"crossref","first-page":"973","DOI":"10.1007\/s13042-022-01676-7","article-title":"CovidViT: A novel neural network with self-attention mechanism to detect Covid-19 through X-ray images","volume":"14","author":"Yang","year":"2022","journal-title":"Int J Mach Learn Cybern"},{"key":"10.1016\/j.compeleceng.2026.111193_b51","article-title":"Ensemble learning based on efficient features combination can predict the outcome of recurrence-free survival in patients with hepatocellular carcinoma within three years after surgery","volume":"12","author":"Wang","year":"2022","journal-title":"Front Oncol"},{"key":"10.1016\/j.compeleceng.2026.111193_b52","article-title":"An image is worth 16x16 words: Transformers for image recognition at scale","author":"Dosovitskiy","year":"2021","journal-title":"Comput Vis Pattern Recognit (CVPR)"},{"key":"10.1016\/j.compeleceng.2026.111193_b53","doi-asserted-by":"crossref","first-page":"5728","DOI":"10.1038\/s41598-023-31275-7","article-title":"A novel dataset and efficient deep learning framework for automated grading of renal cell carcinoma from kidney histopathology images","volume":"13","author":"Chanchal","year":"2023","journal-title":"Sci Rep"},{"key":"10.1016\/j.compeleceng.2026.111193_b54","doi-asserted-by":"crossref","unstructured":"Sandler M, Howard A, Zhu M, Zhmoginov A, Chen L-C. MobileNetV2: Inverted residuals and linear bottlenecks. In: Proceedings of the IEEE conference on computer vision and pattern recognition. CVPR, 2018, p. 4510\u201320.","DOI":"10.1109\/CVPR.2018.00474"},{"key":"10.1016\/j.compeleceng.2026.111193_b55","series-title":"RAF2Net: Automated grading of renal cell carcinoma utilizing attention-enhanced deep learning models through feature fusion","author":"Kundu","year":"2024"},{"key":"10.1016\/j.compeleceng.2026.111193_b56","doi-asserted-by":"crossref","unstructured":"Chollet Fran\u00e7ois. Xception: Deep Learning with Depthwise Separable Convolutions. In: Proceedings of the IEEE conference on computer vision and pattern recognition. CVPR, 2017, p. 1800\u20137.","DOI":"10.1109\/CVPR.2017.195"},{"key":"10.1016\/j.compeleceng.2026.111193_b57","doi-asserted-by":"crossref","unstructured":"Zoph Barret, Vasudevan Vijay, Shlens Jonathon, Le V. Learning Transferable Architectures for Scalable Image Recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition. CVPR, 2018, p. 8697\u2013710.","DOI":"10.1109\/CVPR.2018.00907"},{"key":"10.1016\/j.compeleceng.2026.111193_b58","series-title":"Very deep convolutional networks for large-scale image recognition","author":"Simonyan","year":"2014"},{"issue":"4","key":"10.1016\/j.compeleceng.2026.111193_b59","doi-asserted-by":"crossref","first-page":"605","DOI":"10.1007\/s10278-019-00182-7","article-title":"Breast cancer classification from histopathological images with inception recurrent residual convolutional neural network","volume":"32","author":"Alom","year":"2019","journal-title":"J Digit Imaging"},{"key":"10.1016\/j.compeleceng.2026.111193_b60","article-title":"Transformer in transformer","author":"Han","year":"2021","journal-title":"Neural Inf Process Syst"},{"key":"10.1016\/j.compeleceng.2026.111193_b61","first-page":"2022","article-title":"ConViT: improving vision transformers with soft convolutional inductive biases","author":"d\u2019Ascoli","year":"2021","journal-title":"J Stat Mech Theory Exp"},{"key":"10.1016\/j.compeleceng.2026.111193_b62","doi-asserted-by":"crossref","unstructured":"Liu Z, Lin Y, Cao Y, Hu H, Wei Y, Zhang Z, Lin S, Guo B. Swin Transformer: Hierarchical Vision Transformer using Shifted Windows. In: 2021 IEEE\/CVF international conference on computer vision. ICCV, 2021, p. 9992\u201310002.","DOI":"10.1109\/ICCV48922.2021.00986"},{"key":"10.1016\/j.compeleceng.2026.111193_b63","doi-asserted-by":"crossref","unstructured":"Heo B, Yun S, Han D, Chun S, Choe J, Oh S. Rethinking Spatial Dimensions of Vision Transformers. In: 2021 IEEE\/CVF international conference on computer vision. ICCV, 2021, p. 11916\u201325.","DOI":"10.1109\/ICCV48922.2021.01172"},{"key":"10.1016\/j.compeleceng.2026.111193_b64","doi-asserted-by":"crossref","first-page":"415","DOI":"10.1007\/s41095-022-0274-8","article-title":"PVT v2: Improved baselines with pyramid vision transformer","volume":"8","author":"Wang","year":"2021","journal-title":"Comput Vis Media"}],"container-title":["Computers and Electrical Engineering"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S004579062600265X?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S004579062600265X?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,7,4]],"date-time":"2026-07-04T12:51:30Z","timestamp":1783169490000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S004579062600265X"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,9]]},"references-count":64,"alternative-id":["S004579062600265X"],"URL":"https:\/\/doi.org\/10.1016\/j.compeleceng.2026.111193","relation":{},"ISSN":["0045-7906"],"issn-type":[{"value":"0045-7906","type":"print"}],"subject":[],"published":{"date-parts":[[2026,9]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"FUS-Net: Towards automated Renal Cell Carcinoma grading system via advanced adaptive attention mechanism aided deep learning fusion","name":"articletitle","label":"Article Title"},{"value":"Computers and Electrical Engineering","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.compeleceng.2026.111193","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":"111193"}}