{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,11]],"date-time":"2026-03-11T09:24:00Z","timestamp":1773221040084,"version":"3.50.1"},"reference-count":62,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2025,11,1]],"date-time":"2025-11-01T00:00:00Z","timestamp":1761955200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2025,11,1]],"date-time":"2025-11-01T00:00:00Z","timestamp":1761955200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2025,11,1]],"date-time":"2025-11-01T00:00:00Z","timestamp":1761955200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2025,11,1]],"date-time":"2025-11-01T00:00:00Z","timestamp":1761955200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2025,11,1]],"date-time":"2025-11-01T00:00:00Z","timestamp":1761955200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2025,11,1]],"date-time":"2025-11-01T00:00:00Z","timestamp":1761955200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2025,11,1]],"date-time":"2025-11-01T00:00:00Z","timestamp":1761955200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"funder":[{"DOI":"10.13039\/501100004921","name":"Shanghai Jiao Tong University","doi-asserted-by":"publisher","award":["YG2023ZD21"],"award-info":[{"award-number":["YG2023ZD21"]}],"id":[{"id":"10.13039\/501100004921","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003399","name":"Science and Technology Commission of Shanghai Municipality","doi-asserted-by":"publisher","award":["21ZR143630"],"award-info":[{"award-number":["21ZR143630"]}],"id":[{"id":"10.13039\/501100003399","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003399","name":"Science and Technology Commission of Shanghai Municipality","doi-asserted-by":"publisher","award":["23XD1401900"],"award-info":[{"award-number":["23XD1401900"]}],"id":[{"id":"10.13039\/501100003399","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003399","name":"Science and Technology Commission of Shanghai Municipality","doi-asserted-by":"publisher","award":["23DZ2290600"],"award-info":[{"award-number":["23DZ2290600"]}],"id":[{"id":"10.13039\/501100003399","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003399","name":"Science and Technology Commission of Shanghai Municipality","doi-asserted-by":"publisher","award":["24JS2810200"],"award-info":[{"award-number":["24JS2810200"]}],"id":[{"id":"10.13039\/501100003399","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["12171318"],"award-info":[{"award-number":["12171318"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["clinicalkey.com","clinicalkey.com.au","clinicalkey.es","clinicalkey.fr","clinicalkey.jp","elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Computers in Biology and Medicine"],"published-print":{"date-parts":[[2025,11]]},"DOI":"10.1016\/j.compbiomed.2025.111037","type":"journal-article","created":{"date-parts":[[2025,10,4]],"date-time":"2025-10-04T12:47:02Z","timestamp":1759582022000},"page":"111037","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"PA","title":["Spatially Aware GCNs for efficient, high-accuracy cancer grading: Mitigating oversmoothing via frequency analysis"],"prefix":"10.1016","volume":"198","author":[{"given":"Luke","family":"Johnston","sequence":"first","affiliation":[]},{"given":"Zhangsheng","family":"Yu","sequence":"additional","affiliation":[]}],"member":"78","reference":[{"issue":"3","key":"10.1016\/j.compbiomed.2025.111037_b1","doi-asserted-by":"crossref","first-page":"769","DOI":"10.1002\/ijc.32330","article-title":"Significant inter- and intra-laboratory variation in grading of invasive breast cancer: a nationwide study of 33,043 patients in the Netherlands","volume":"146","author":"Dooijeweert","year":"2019","journal-title":"Int. J. Cancer"},{"issue":"21","key":"10.1016\/j.compbiomed.2025.111037_b2","doi-asserted-by":"crossref","first-page":"5378","DOI":"10.3390\/cancers13215378","article-title":"Significant inter- and intralaboratory variation in Gleason grading of prostate cancer: a nationwide study of 35,258 patients in the Netherlands","volume":"13","author":"Flach","year":"2021","journal-title":"Cancers"},{"key":"10.1016\/j.compbiomed.2025.111037_b3","article-title":"Theory of the frequency principle for general deep neural networks","author":"Luo","year":"2021","journal-title":"Trans. Appl. Math."},{"key":"10.1016\/j.compbiomed.2025.111037_b4","unstructured":"Z.-Q.J. Xu, Y. Zhang, Y. Xiao, Training behavior of deep neural network in frequency domain, in: 26th International Conference on Neural Information Processing (ICONIP 2019), 2019."},{"key":"10.1016\/j.compbiomed.2025.111037_b5","article-title":"Frequency principle: Fourier analysis sheds light on deep neural networks","author":"Xu","year":"2020","journal-title":"Commun. Comput. Phys."},{"issue":"3","key":"10.1016\/j.compbiomed.2025.111037_b6","doi-asserted-by":"crossref","first-page":"827","DOI":"10.1007\/s42967-024-00398-7","article-title":"Overview frequency principle\/spectral bias in deep learning","volume":"7","author":"Xu","year":"2024","journal-title":"Commun. Appl. Math. Comput."},{"key":"10.1016\/j.compbiomed.2025.111037_b7","doi-asserted-by":"crossref","first-page":"203","DOI":"10.3389\/fphy.2020.00203","article-title":"Classification of cancer types using graph convolutional neural networks","volume":"8","author":"Ramirez","year":"2020","journal-title":"Front. Phys."},{"key":"10.1016\/j.compbiomed.2025.111037_b8","series-title":"Revisiting over-smoothing in deep GCNs","author":"Yang","year":"2020"},{"key":"10.1016\/j.compbiomed.2025.111037_b9","doi-asserted-by":"crossref","unstructured":"D. Chen, Y. Lin, W. Li, P. Li, J. Zhou, X. Sun, Measuring and relieving the over-smoothing problem for graph neural networks from the topological view, in: Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 34, 2020, pp. 3438\u20133445.","DOI":"10.1609\/aaai.v34i04.5747"},{"key":"10.1016\/j.compbiomed.2025.111037_b10","series-title":"Revisiting graph convolutional network on semi-supervised node classification from an optimization perspective","author":"Zhang","year":"2020"},{"key":"10.1016\/j.compbiomed.2025.111037_b11","doi-asserted-by":"crossref","first-page":"506","DOI":"10.1186\/s12859-022-05063-5","article-title":"A graph neural network framework for mapping histological topology in oral mucosal tissue","volume":"23","author":"Nair","year":"2022","journal-title":"BMC Bioinformatics"},{"issue":"23","key":"10.1016\/j.compbiomed.2025.111037_b12","doi-asserted-by":"crossref","first-page":"5961","DOI":"10.1021\/acs.jcim.2c01092","article-title":"Rgn: residue-based graph attention and convolutional network for protein\u2013protein interaction site prediction","volume":"62","author":"Wang","year":"2022","journal-title":"J. Chem. Inf. Model."},{"issue":"1","key":"10.1016\/j.compbiomed.2025.111037_b13","article-title":"Classification of breast cancer grades using physical parameters and K-nearest neighbor method","volume":"1334","author":"Gunawan","year":"2019","journal-title":"J. Phys.: Conf. Ser."},{"issue":"12","key":"10.1016\/j.compbiomed.2025.111037_b14","first-page":"367","article-title":"Quantitative analysis of benign and malignant tumors in histopathology: Predicting prostate cancer grading using SVM","volume":"43","author":"Bhattacharjee","year":"2019","journal-title":"J. Med. Syst."},{"key":"10.1016\/j.compbiomed.2025.111037_b15","doi-asserted-by":"crossref","DOI":"10.1016\/j.media.2023.102936","article-title":"Multi-cell type and multi-level graph aggregation network for cancer grading in pathology images","volume":"90","author":"Abbas","year":"2023","journal-title":"Med. Image Anal."},{"key":"10.1016\/j.compbiomed.2025.111037_b16","doi-asserted-by":"crossref","unstructured":"S. Paul, B. Yener, A.W. Lund, C2P-GCN: Cell-to-Patch Graph Convolutional Network for Colorectal Cancer Grading, in: 2024 46th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC, 2024.","DOI":"10.1109\/EMBC53108.2024.10782435"},{"key":"10.1016\/j.compbiomed.2025.111037_b17","doi-asserted-by":"crossref","first-page":"75343","DOI":"10.1007\/s11042-024-18608-y","article-title":"Enhancing cervical cancer diagnosis with graph convolution network: AI-Powered segmentation, feature analysis, and classification for early detection","volume":"83","author":"Fahad","year":"2024","journal-title":"Multimedia Tools Appl."},{"key":"10.1016\/j.compbiomed.2025.111037_b18","series-title":"Advances in Neural Information Processing Systems (NeurIPS)","first-page":"2224","article-title":"Convolutional networks on graphs for learning molecular fingerprints","volume":"Vol. 28","author":"Duvenaud","year":"2015"},{"issue":"7","key":"10.1016\/j.compbiomed.2025.111037_b19","doi-asserted-by":"crossref","first-page":"3163","DOI":"10.1109\/JBHI.2022.3153671","article-title":"A convolutional neural network and graph convolutional network based framework for classification of breast histopathological images","volume":"26","author":"Gao","year":"2022","journal-title":"IEEE J. Biomed. Heal. Inform."},{"key":"10.1016\/j.compbiomed.2025.111037_b20","doi-asserted-by":"crossref","unstructured":"J. Wang, R.J. Chen, M.Y. Lu, A. Baras, F. Mahmood, Weakly Supervised Prostate TMA Classification via Graph Convolutional Networks, in: Proceedings of the 2020 IEEE 17th International Symposium on Biomedical Imaging, ISBI, 2020, pp. 239\u2013243.","DOI":"10.1109\/ISBI45749.2020.9098534"},{"key":"10.1016\/j.compbiomed.2025.111037_b21","doi-asserted-by":"crossref","unstructured":"Y. Zhou, S. Graham, N.A. Koohbanani, M. Shaban, P.-A. Heng, N. Rajpoot, CGC-Net: Cell Graph Convolutional Network for Grading of Colorectal Cancer Histology Images, in: Proceedings of the IEEE\/CVF International Conference on Computer Vision Workshops, ICCVW, 2019, pp. 388\u2013398.","DOI":"10.1109\/ICCVW.2019.00050"},{"key":"10.1016\/j.compbiomed.2025.111037_b22","doi-asserted-by":"crossref","first-page":"126315","DOI":"10.1109\/ACCESS.2022.3226369","article-title":"Interest-aware contrastive-learning-based gcn for recommendation","volume":"10","author":"Lin","year":"2022","journal-title":"IEEE Access"},{"issue":"11","key":"10.1016\/j.compbiomed.2025.111037_b23","doi-asserted-by":"crossref","first-page":"7004","DOI":"10.1109\/TITS.2020.3000761","article-title":"Deep learning architecture for short-term passenger flow forecasting in urban rail transit","volume":"22","author":"Zhang","year":"2021","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"10.1016\/j.compbiomed.2025.111037_b24","series-title":"Proceedings of the AAAI Conference on Artificial Intelligence","first-page":"6524","article-title":"Meta propagation networks for graph few-shot semi-supervised learning","volume":"Vol. 36","author":"Ding","year":"2022"},{"issue":"4","key":"10.1016\/j.compbiomed.2025.111037_b25","doi-asserted-by":"crossref","first-page":"798","DOI":"10.3390\/sym14040798","article-title":"Dii-gcn: dropedge based deep graph convolutional networks","volume":"14","author":"Zhu","year":"2022","journal-title":"Symmetry"},{"key":"10.1016\/j.compbiomed.2025.111037_b26","series-title":"Proceedings of the AAAI Conference on Artificial Intelligence","first-page":"4371","article-title":"Multi-dimensional prediction of guild health in online games: a stability-aware multi-task learning approach","volume":"Vol. 36","author":"Zhao","year":"2022"},{"key":"10.1016\/j.compbiomed.2025.111037_b27","doi-asserted-by":"crossref","first-page":"39083","DOI":"10.1109\/ACCESS.2023.3268797","article-title":"A network science perspective of graph convolutional networks: a survey","volume":"11","author":"Jia","year":"2023","journal-title":"IEEE Access"},{"key":"10.1016\/j.compbiomed.2025.111037_b28","first-page":"1","article-title":"A dynamic community detection method for complex networks based on deep self-coding network","volume":"2022","author":"Zhang","year":"2022","journal-title":"Comput. Intell. Neurosci."},{"key":"10.1016\/j.compbiomed.2025.111037_b29","doi-asserted-by":"crossref","first-page":"144025","DOI":"10.1109\/ACCESS.2021.3121708","article-title":"Graphs, entities, and step mixture for enriching graph representation","volume":"9","author":"Shin","year":"2021","journal-title":"IEEE Access"},{"issue":"12","key":"10.1016\/j.compbiomed.2025.111037_b30","doi-asserted-by":"crossref","first-page":"3529","DOI":"10.1007\/s13042-021-01400-x","article-title":"Weakly-supervised learning for community detection based on graph convolution in attributed networks","volume":"12","author":"Li","year":"2021","journal-title":"Int. J. Mach. Learn. Cybern."},{"key":"10.1016\/j.compbiomed.2025.111037_b31","series-title":"Reducing oversmoothing in graph neural networks by changing the activation function","author":"Kelesis","year":"2023"},{"key":"10.1016\/j.compbiomed.2025.111037_b32","series-title":"On provable benefits of depth in training graph convolutional networks","author":"Cong","year":"2021"},{"key":"10.1016\/j.compbiomed.2025.111037_b33","unstructured":"Y. Rong, W. Huang, T. Xu, J. Huang, DropEdge: Towards Deep Graph Convolutional Networks on Node Classification, in: International Conference on Learning Representations, ICLR, 2020."},{"key":"10.1016\/j.compbiomed.2025.111037_b34","series-title":"Advances in Neural Information Processing Systems","article-title":"Inductive representation learning on large graphs","volume":"Vol. 30","author":"Hamilton","year":"2017"},{"key":"10.1016\/j.compbiomed.2025.111037_b35","unstructured":"P. Veli\u010dkovi\u0107, G. Cucurull, A. Casanova, A. Romero, P. Li\u00f2, Y. Bengio, Graph Attention Networks, in: International Conference on Learning Representations, ICLR, 2018."},{"key":"10.1016\/j.compbiomed.2025.111037_b36","series-title":"Tackling oversmoothing of GNNs with contrastive learning","author":"Zheng","year":"2021"},{"key":"10.1016\/j.compbiomed.2025.111037_b37","series-title":"Overcoming oversmoothness in graph convolutional networks via hybrid scattering networks","author":"Wenkel","year":"2022"},{"issue":"11","key":"10.1016\/j.compbiomed.2025.111037_b38","doi-asserted-by":"crossref","first-page":"1648","DOI":"10.3390\/math12111648","article-title":"Multi-view and multimodal graph convolutional neural network for autism spectrum disorder diagnosis","volume":"12","author":"Song","year":"2024","journal-title":"Mathematics"},{"key":"10.1016\/j.compbiomed.2025.111037_b39","doi-asserted-by":"crossref","first-page":"56083","DOI":"10.1109\/ACCESS.2023.3283029","article-title":"LEL-GNN: Learnable edge sampling and line based graph neural network for link prediction","volume":"11","author":"Morshed","year":"2023","journal-title":"IEEE Access"},{"key":"10.1016\/j.compbiomed.2025.111037_b40","series-title":"Proceedings of the AAAI Conference on Artificial Intelligence","first-page":"3950","article-title":"Beyond low-frequency information in graph convolutional networks","volume":"Vol. 35","author":"Bo","year":"2021"},{"key":"10.1016\/j.compbiomed.2025.111037_b41","doi-asserted-by":"crossref","unstructured":"Y. Dong, K. Ding, B. Jalaian, S. Ji, J. Li, AdaGNN: Graph Neural Networks with Adaptive Frequency Response Filter, in: Proceedings of the 30th ACM International Conference on Information & Knowledge Management, CIKM\u201921, 2021, pp. 392\u2013401.","DOI":"10.1145\/3459637.3482226"},{"issue":"7","key":"10.1016\/j.compbiomed.2025.111037_b42","doi-asserted-by":"crossref","first-page":"6687","DOI":"10.1109\/TKDE.2022.3186016","article-title":"Beyond low-pass filtering: Graph convolutional networks with automatic filtering","volume":"35","author":"Wu","year":"2023","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"10.1016\/j.compbiomed.2025.111037_b43","doi-asserted-by":"crossref","unstructured":"B. Xu, H. Shen, Q. Cao, K. Cen, X. Cheng, Graph convolutional networks using heat kernel for semi-supervised learning, in: Proceedings of IJCAI, 2019, pp. 1928\u20131934.","DOI":"10.24963\/ijcai.2019\/267"},{"key":"10.1016\/j.compbiomed.2025.111037_b44","article-title":"Adaptive graph encoder for attributed graph embedding","author":"Cui","year":"2020","journal-title":"ACM Trans."},{"key":"10.1016\/j.compbiomed.2025.111037_b45","series-title":"Message passing in graph convolution networks via adaptive filter banks","author":"Gao","year":"2021"},{"key":"10.1016\/j.compbiomed.2025.111037_b46","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"crossref","first-page":"288","DOI":"10.1007\/978-3-030-87237-3_28","article-title":"Cells are actors: social network analysis with classical ML for SOTA histology image classification","author":"Zamanitajeddin","year":"2021"},{"issue":"3","key":"10.1016\/j.compbiomed.2025.111037_b47","doi-asserted-by":"crossref","first-page":"140","DOI":"10.1002\/cncr.10621","article-title":"The effects of the current world health organization\/international society of urologic pathologists bladder neoplasm classification system on urine cytology results","volume":"96","author":"Curry","year":"2002","journal-title":"Cancer"},{"key":"10.1016\/j.compbiomed.2025.111037_b48","article-title":"Decoding visual fmri stimuli from human brain based on graph convolutional neural network","author":"Lu","year":"2022","journal-title":"Brain Sci."},{"key":"10.1016\/j.compbiomed.2025.111037_b49","series-title":"Proceedings of the IEEE\/CVF International Conference on Computer Vision","article-title":"PU-GCN: Point cloud upsampling using graph convolutional networks","author":"Qian","year":"2021"},{"key":"10.1016\/j.compbiomed.2025.111037_b50","series-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition","article-title":"Deepgcns: Making graph convolutional networks go as deep as CNNs","author":"Li","year":"2020"},{"key":"10.1016\/j.compbiomed.2025.111037_b51","series-title":"The t-digest: Efficient estimates of distributions","author":"Dunning","year":"2019"},{"issue":"1","key":"10.1016\/j.compbiomed.2025.111037_b52","doi-asserted-by":"crossref","first-page":"16852","DOI":"10.1038\/s41598-017-16516-w","article-title":"Glandular morphometrics for objective grading of colorectal adenocarcinoma histology images","volume":"7","author":"Awan","year":"2017","journal-title":"Sci. Rep."},{"key":"10.1016\/j.compbiomed.2025.111037_b53","series-title":"Medical Image Computing and Computer-Assisted Intervention\u2013MICCAI 2018","first-page":"802","article-title":"Improving whole slide segmentation through visual context - A systematic study","author":"Sirinukunwattana","year":"2018"},{"key":"10.1016\/j.compbiomed.2025.111037_b54","doi-asserted-by":"crossref","unstructured":"K. He, X. Zhang, S. Ren, J. Sun, Deep residual learning for image recognition, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2016, pp. 770\u2013778.","DOI":"10.1109\/CVPR.2016.90"},{"key":"10.1016\/j.compbiomed.2025.111037_b55","doi-asserted-by":"crossref","unstructured":"C. Szegedy, W. Liu, Y. Jia, et al., Going deeper with convolutions, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2015, pp. 1\u20139.","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"10.1016\/j.compbiomed.2025.111037_b56","doi-asserted-by":"crossref","unstructured":"F. Chollet, Xception: Deep learning with depthwise separable convolutions, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2017, pp. 1251\u20131258.","DOI":"10.1109\/CVPR.2017.195"},{"issue":"1","key":"10.1016\/j.compbiomed.2025.111037_b57","doi-asserted-by":"crossref","first-page":"91","DOI":"10.1007\/s00432-022-04435-x","article-title":"Computer-aided detection and prognosis of colorectal cancer on whole slide images using dual resolution deep learning","volume":"149","author":"Xu","year":"2023","journal-title":"J. Cancer Res. Clin. Oncol."},{"key":"10.1016\/j.compbiomed.2025.111037_b58","doi-asserted-by":"crossref","DOI":"10.1016\/j.cmpb.2022.107095","article-title":"PPsNet: An improved deep learning model for microsatellite instability high prediction in colorectal cancer from whole slide images","volume":"225","author":"Lou","year":"2022","journal-title":"Comput. Methods Programs Biomed."},{"issue":"7","key":"10.1016\/j.compbiomed.2025.111037_b59","doi-asserted-by":"crossref","first-page":"2395","DOI":"10.1109\/TMI.2020.2971006","article-title":"Context-Aware convolutional neural network for grading of colorectal cancer histology images","volume":"39","author":"Shaban","year":"2020","journal-title":"IEEE Trans. Med. Imaging"},{"key":"10.1016\/j.compbiomed.2025.111037_b60","unstructured":"Y. Su, W. Zhang, H. Li, X. Chen, J. Wang, HAT-Net: A Hierarchical Transformer Graph Neural Network for Grading of Colorectal Cancer Histology Images, in: Proceedings of the British Machine Vision Conference, BMVC, 2023."},{"issue":"4","key":"10.1016\/j.compbiomed.2025.111037_b61","doi-asserted-by":"crossref","first-page":"383","DOI":"10.1002\/path.5923","article-title":"High-throughput whole-slide scanning to enable large-scale data repository building","volume":"257","author":"Zarella","year":"2022","journal-title":"J. Pathol."},{"issue":"1","key":"10.1016\/j.compbiomed.2025.111037_b62","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1007\/s00428-025-04043-3","article-title":"An equivalency and efficiency study for one year digital pathology for clinical routine diagnostics in an accredited tertiary academic center","volume":"487","author":"Iwuajoku","year":"2025","journal-title":"Virchows Arch."}],"container-title":["Computers in Biology and Medicine"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0010482525013897?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0010482525013897?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,3,10]],"date-time":"2026-03-10T11:50:53Z","timestamp":1773143453000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0010482525013897"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,11]]},"references-count":62,"alternative-id":["S0010482525013897"],"URL":"https:\/\/doi.org\/10.1016\/j.compbiomed.2025.111037","relation":{},"ISSN":["0010-4825"],"issn-type":[{"value":"0010-4825","type":"print"}],"subject":[],"published":{"date-parts":[[2025,11]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Spatially Aware GCNs for efficient, high-accuracy cancer grading: Mitigating oversmoothing via frequency analysis","name":"articletitle","label":"Article Title"},{"value":"Computers in Biology and Medicine","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.compbiomed.2025.111037","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2025 Published by Elsevier Ltd.","name":"copyright","label":"Copyright"}],"article-number":"111037"}}