{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,14]],"date-time":"2026-04-14T09:23:25Z","timestamp":1776158605921,"version":"3.50.1"},"reference-count":63,"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":["Knowledge-Based Systems"],"published-print":{"date-parts":[[2026,5]]},"DOI":"10.1016\/j.knosys.2026.115828","type":"journal-article","created":{"date-parts":[[2026,3,21]],"date-time":"2026-03-21T16:26:57Z","timestamp":1774110417000},"page":"115828","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"C","title":["MedAugment: Universal automatic data augmentation plug-in for medical image analysis"],"prefix":"10.1016","volume":"341","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3187-4881","authenticated-orcid":false,"given":"Zhaoshan","family":"Liu","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4979-7906","authenticated-orcid":false,"given":"Qiujie","family":"Lv","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6538-6819","authenticated-orcid":false,"given":"Yifan","family":"Li","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8195-2526","authenticated-orcid":false,"given":"Ziduo","family":"Yang","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6198-5753","authenticated-orcid":false,"given":"Lei","family":"Shen","sequence":"additional","affiliation":[]}],"member":"78","reference":[{"issue":"3s","key":"10.1016\/j.knosys.2026.115828_bib0001","first-page":"1","article-title":"Precise no-reference image quality evaluation based on distortion identification","volume":"17","author":"Yan","year":"2021","journal-title":"ACM Trans. Multimed. Comput., Commun. and Appl. (TOMM)"},{"key":"10.1016\/j.knosys.2026.115828_bib0002","article-title":"Imagenet classification with deep convolutional neural networks","volume":"25","author":"Krizhevsky","year":"2012","journal-title":"Adv. Neural Inf. Process. Syst."},{"issue":"1s","key":"10.1016\/j.knosys.2026.115828_bib0003","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3472810","article-title":"Age-invariant face recognition by multi-feature fusionand decomposition with self-attention","volume":"18","author":"Yan","year":"2022","journal-title":"ACM Trans. Multimed. Comput., Commun. Appl. (TOMM)"},{"key":"10.1016\/j.knosys.2026.115828_sbref0004","doi-asserted-by":"crossref","DOI":"10.1016\/j.compbiomed.2023.106541","article-title":"3D-MedTranCSGAN: 3D medical image transformation using CSGAN","author":"Poonkodi","year":"2023","journal-title":"Comput. Biol. Med."},{"key":"10.1016\/j.knosys.2026.115828_sbref0005","doi-asserted-by":"crossref","DOI":"10.1016\/j.compbiomed.2022.106034","article-title":"Uncertainty teacher with dense focal loss for semi-supervised medical image segmentation","volume":"149","author":"Chen","year":"2022","journal-title":"Comput. Biol. Med."},{"key":"10.1016\/j.knosys.2026.115828_sbref0006","article-title":"Transforming medical imaging with transformers? a comparative review of key properties, current progresses, and future perspectives","author":"Li","year":"2023","journal-title":"Med. Image Anal."},{"issue":"9","key":"10.1016\/j.knosys.2026.115828_bib0007","doi-asserted-by":"crossref","DOI":"10.1016\/j.heliyon.2023.e19585","article-title":"GSDA: Generative adversarial network-based semi-supervised data augmentation for ultrasound image classification","volume":"9","author":"Liu","year":"2023","journal-title":"Heliyon"},{"key":"10.1016\/j.knosys.2026.115828_bib0008","doi-asserted-by":"crossref","unstructured":"M. Eisenmann, A. Reinke, V. Weru, M.D. Tizabi, F. Isensee, T.J. Adler, S. Ali, V. Andrearczyk, M. Aubreville, U. Baid, et al., Why is the winner the best?, arXiv preprint (2023). https:\/\/doi.org\/10.48550\/arXiv.2303.17719.","DOI":"10.1109\/CVPR52729.2023.01911"},{"issue":"4","key":"10.1016\/j.knosys.2026.115828_bib0009","doi-asserted-by":"crossref","first-page":"1095","DOI":"10.1109\/TMI.2022.3224067","article-title":"Causality-inspired single-source domain generalization for medical image segmentation","volume":"42","author":"Ouyang","year":"2022","journal-title":"IEEE Trans. Med. Imaging"},{"key":"10.1016\/j.knosys.2026.115828_sbref0010","doi-asserted-by":"crossref","first-page":"21","DOI":"10.1016\/j.media.2018.10.004","article-title":"Fully convolutional multi-scale residual denseNets for cardiac segmentation and automated cardiac diagnosis using ensemble of classifiers","volume":"51","author":"Khened","year":"2019","journal-title":"Med. Image Anal."},{"key":"10.1016\/j.knosys.2026.115828_bib0011","doi-asserted-by":"crossref","first-page":"108276","DOI":"10.1109\/ACCESS.2021.3101142","article-title":"Diabetic retinopathy diagnosis from fundus images using stacked generalization of deep models","volume":"9","author":"Kaushik","year":"2021","journal-title":"IEEE Access"},{"issue":"2","key":"10.1016\/j.knosys.2026.115828_bib0012","doi-asserted-by":"crossref","first-page":"203","DOI":"10.1038\/s41592-020-01008-z","article-title":"NnU-Net: a self-configuring method for deep learning-based biomedical image segmentation","volume":"18","author":"Isensee","year":"2021","journal-title":"Nat. Methods"},{"key":"10.1016\/j.knosys.2026.115828_bib0013","doi-asserted-by":"crossref","DOI":"10.1016\/j.compbiomed.2022.105985","article-title":"Synthetic augmentation for semantic segmentation of class imbalanced biomedical images: a data pair generative adversarial network approach","volume":"150","author":"Chai","year":"2022","journal-title":"Comput. Biol. Med."},{"key":"10.1016\/j.knosys.2026.115828_bib0014","series-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition","first-page":"8300","article-title":"Semantic segmentation with generative models: semi-supervised learning and strong out-of-domain generalization","author":"Li","year":"2021"},{"key":"10.1016\/j.knosys.2026.115828_bib0015","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2023.119718","article-title":"UNet: A semi-supervised method for segmentation of breast tumor images using a U-shaped pyramid-dilated network","volume":"221","author":"Iqbal","year":"2023","journal-title":"Expert Syst. Appl."},{"key":"10.1016\/j.knosys.2026.115828_sbref0016","doi-asserted-by":"crossref","DOI":"10.1016\/j.cmpb.2021.106018","article-title":"Semi-supervised GAN-based radiomics model for data augmentation in breast ultrasound mass classification","volume":"203","author":"Pang","year":"2021","journal-title":"Comput. Meth. Programs Biomed."},{"key":"10.1016\/j.knosys.2026.115828_bib0017","unstructured":"A. Beers, J. Brown, K. Chang, J.P. Campbell, S. Ostmo, M.F. Chiang, J. Kalpathy-Cramer, High-resolution medical image synthesis using progressively grown generative adversarial networks, arXiv preprint arXiv: 1805.03144(2018)."},{"key":"10.1016\/j.knosys.2026.115828_bib0018","series-title":"Proceedings of the IEEE\/CVF International Conference on Computer Vision","first-page":"6701","article-title":"When do gans replicate? on the choice of dataset size","author":"Feng","year":"2021"},{"key":"10.1016\/j.knosys.2026.115828_bib0019","series-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition","first-page":"4401","article-title":"A style-based generator architecture for generative adversarial networks","author":"Karras","year":"2019"},{"key":"10.1016\/j.knosys.2026.115828_bib0020","series-title":"Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision","first-page":"2000","article-title":"A morphology focused diffusion probabilistic model for synthesis of histopathology images","author":"Moghadam","year":"2023"},{"key":"10.1016\/j.knosys.2026.115828_bib0021","series-title":"MICCAI Workshop on Deep Generative Models","first-page":"117","article-title":"Brain imaging generation with latent diffusion models","author":"Pinaya","year":"2022"},{"issue":"1","key":"10.1016\/j.knosys.2026.115828_bib0022","doi-asserted-by":"crossref","first-page":"7303","DOI":"10.1038\/s41598-023-34341-2","article-title":"Denoising diffusion probabilistic models for 3D medical image generation","volume":"13","author":"Khader","year":"2023","journal-title":"Sci. Rep."},{"key":"10.1016\/j.knosys.2026.115828_bib0023","unstructured":"F. Tang, J. Ding, L. Wang, M. Xian, C. Ning, Multi-Level Global Context Cross Consistency Model for Semi-Supervised Ultrasound Image Segmentation with Diffusion Model, (2023). arXiv preprint arXiv:2305.09447."},{"key":"10.1016\/j.knosys.2026.115828_bib0024","series-title":"ICASSP 2020-2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","first-page":"1419","article-title":"Automatic data augmentation via deep reinforcement learning for effective kidney tumor segmentation","author":"Qin","year":"2020"},{"key":"10.1016\/j.knosys.2026.115828_bib0025","series-title":"Medical Image Computing and Computer Assisted Intervention\u2013MICCAI 2020: 23rd International Conference, Lima, Peru, October 4\u20138, 2020, Proceedings, Part I 23","first-page":"378","article-title":"Automatic data augmentation for 3D medical image segmentation","author":"Xu","year":"2020"},{"issue":"12","key":"10.1016\/j.knosys.2026.115828_bib0026","doi-asserted-by":"crossref","first-page":"3699","DOI":"10.1109\/TMI.2022.3193146","article-title":"Aadg: automatic augmentation for domain generalization on retinal image segmentation","volume":"41","author":"Lyu","year":"2022","journal-title":"IEEE Trans. Med. Imaging"},{"key":"10.1016\/j.knosys.2026.115828_bib0027","series-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition","first-page":"113","article-title":"Autoaugment: learning augmentation strategies from data","author":"Cubuk","year":"2019"},{"key":"10.1016\/j.knosys.2026.115828_bib0028","series-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition Workshops","first-page":"702","article-title":"Randaugment: practical automated data augmentation with a reduced search space","author":"Cubuk","year":"2020"},{"key":"10.1016\/j.knosys.2026.115828_bib0029","series-title":"Proceedings of the IEEE\/CVF International Conference on Computer Vision","first-page":"774","article-title":"Trivialaugment: tuning-free yet state-of-the-art data augmentation","author":"M\u00fcller","year":"2021"},{"key":"10.1016\/j.knosys.2026.115828_bib0030","unstructured":"T.C. LingChen, A. Khonsari, A. Lashkari, M.R. Nazari, J.S. Sambee, M.A. Nascimento, Uniformaugment: A search-free probabilistic data augmentation approach, arXiv preprint (2020). https:\/\/doi.org\/10.48550\/arXiv.2003.14348."},{"key":"10.1016\/j.knosys.2026.115828_bib0031","article-title":"Recent progress in transformer-based medical image analysis","author":"Liu","year":"2023","journal-title":"Comput. Biol. Med."},{"issue":"7","key":"10.1016\/j.knosys.2026.115828_sbref0032","doi-asserted-by":"crossref","first-page":"1837","DOI":"10.1109\/TMI.2022.3150682","article-title":"Self-supervised learning for few-shot medical image segmentation","volume":"41","author":"Ouyang","year":"2022","journal-title":"IEEE Trans. Med. Imaging"},{"key":"10.1016\/j.knosys.2026.115828_bib0033","article-title":"Fast autoaugment","volume":"32","author":"Lim","year":"2019","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"10.1016\/j.knosys.2026.115828_bib0034","article-title":"A bayesian data augmentation approach for learning deep models","volume":"30","author":"Tran","year":"2017","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"10.1016\/j.knosys.2026.115828_bib0035","series-title":"International Conference on Machine Learning","first-page":"2731","article-title":"Population based augmentation: efficient learning of augmentation policy schedules","author":"Ho","year":"2019"},{"key":"10.1016\/j.knosys.2026.115828_bib0036","unstructured":"X. Zhang, Q. Wang, J. Zhang, Z. Zhong, Adversarial autoaugment, (2019). arXiv preprint arXiv:1912.11188."},{"key":"10.1016\/j.knosys.2026.115828_bib0037","series-title":"European Conference on Computer Vision","first-page":"580","article-title":"Differentiable automatic data augmentation","author":"Li","year":"2020"},{"key":"10.1016\/j.knosys.2026.115828_bib0038","unstructured":"D. Hendrycks, N. Mu, E.D. Cubuk, B. Zoph, J. Gilmer, B. Lakshminarayanan, Augmix: A simple data processing method to improve robustness and uncertainty, arXiv preprint (2019). https:\/\/doi.org\/10.48550\/arXiv.1912.02781."},{"issue":"7","key":"10.1016\/j.knosys.2026.115828_bib0039","doi-asserted-by":"crossref","first-page":"2531","DOI":"10.1109\/TMI.2020.2973595","article-title":"Generalizing deep learning for medical image segmentation to unseen domains via deep stacked transformation","volume":"39","author":"Zhang","year":"2020","journal-title":"IEEE Trans. Med. Imaging"},{"key":"10.1016\/j.knosys.2026.115828_bib0040","doi-asserted-by":"crossref","DOI":"10.1016\/j.media.2022.102597","article-title":"Enhancing MR image segmentation with realistic adversarial data augmentation","volume":"82","author":"Chen","year":"2022","journal-title":"Med. Image Anal."},{"key":"10.1016\/j.knosys.2026.115828_bib0041","unstructured":"T. Karras, T. Aila, S. Laine, J. Lehtinen, Progressive growing of gans for improved quality, stability, and variation, (2017). arXiv preprint arXiv:1710.10196."},{"key":"10.1016\/j.knosys.2026.115828_bib0042","doi-asserted-by":"crossref","DOI":"10.1016\/j.media.2020.101934","article-title":"Semi-supervised task-driven data augmentation for medical image segmentation","volume":"68","author":"Chaitanya","year":"2021","journal-title":"Med. Image Anal."},{"key":"10.1016\/j.knosys.2026.115828_bib0043","series-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition","first-page":"10684","article-title":"High-resolution image synthesis with latent diffusion models","author":"Rombach","year":"2022"},{"key":"10.1016\/j.knosys.2026.115828_bib0044","series-title":"International Conference on Machine Learning","first-page":"1995","article-title":"Dueling network architectures for deep reinforcement learning","author":"Wang","year":"2016"},{"key":"10.1016\/j.knosys.2026.115828_bib0045","series-title":"Medical Image Computing and Computer Assisted Intervention\u2013MICCAI 2019: 22nd International Conference, Shenzhen, China, October 13\u201317, 2019, Proceedings, Part II 22","first-page":"3","article-title":"Searching learning strategy with reinforcement learning for 3D medical image segmentation","author":"Yang","year":"2019"},{"issue":"2","key":"10.1016\/j.knosys.2026.115828_sbref0046","doi-asserted-by":"crossref","first-page":"125","DOI":"10.3390\/info11020125","article-title":"Albumentations: fast and flexible image augmentations","volume":"11","author":"Buslaev","year":"2020","journal-title":"Information"},{"key":"10.1016\/j.knosys.2026.115828_bib0047","doi-asserted-by":"crossref","DOI":"10.1016\/j.cmpb.2021.106236","article-title":"TorchIO: a python library for efficient loading, preprocessing, augmentation and patch-based sampling of medical images in deep learning","volume":"208","author":"P\u00e9rez-Garc\u00eda","year":"2021","journal-title":"Comput. Methods Programs. Biomed."},{"key":"10.1016\/j.knosys.2026.115828_bib0048","article-title":"Batchgenerators-a python framework for data augmentation","author":"Fabian","year":"2020","journal-title":"Division Med. Image Computing German Cancer Res. Center, Appl. Comput. Vis. Lab, Hamburg, Germany, Tech. Rep"},{"key":"10.1016\/j.knosys.2026.115828_sbref0049","doi-asserted-by":"crossref","DOI":"10.1016\/j.dib.2019.104863","article-title":"Dataset of breast ultrasound images","volume":"28","author":"Al-Dhabyani","year":"2020","journal-title":"Data Brief"},{"key":"10.1016\/j.knosys.2026.115828_bib0050","doi-asserted-by":"crossref","DOI":"10.1016\/j.knosys.2025.114615","article-title":"UNGT: Ultrasound nasogastric tube dataset for medical image analysis","author":"Liu","year":"2025","journal-title":"Knowl. Based. Syst."},{"key":"10.1016\/j.knosys.2026.115828_bib0051","doi-asserted-by":"crossref","DOI":"10.1016\/j.neunet.2024.106367","article-title":"Segmenting medical images with limited data","volume":"177","author":"Liu","year":"2024","journal-title":"Neural Networks"},{"key":"10.1016\/j.knosys.2026.115828_sbref0052","first-page":"1","article-title":"Deep learning for reliable classification of COVID-19, MERS, and SARS from chest X-ray images","author":"Tahir","year":"2022","journal-title":"Cognit. Comput."},{"key":"10.1016\/j.knosys.2026.115828_bib0053","unstructured":"Kaggle, Brain Tumor MRI Dataset, (2023)a, (https:\/\/www.kaggle.com\/datasets\/masoudnickparvar\/brain-tumor-mri-dataseta). Accessed 26 April 2023."},{"key":"10.1016\/j.knosys.2026.115828_bib0054","unstructured":"Kaggle, COVID-19 CT Scan Lesion Segmentation dataset, (2024)b, (https:\/\/www.kaggle.com\/datasets\/maedemaftouni\/covid19-ct-scan-lesion-segmentation-dataset). Accessed 1 July 2024."},{"key":"10.1016\/j.knosys.2026.115828_bib0055","doi-asserted-by":"crossref","unstructured":"S.P. Morozov, A.E. Andreychenko, N.A. Pavlov, A.V. Vladzymyrskyy, N.V. Ledikhova, V.A. Gombolevskiy, I.A. Blokhin, P.B. Gelezhe, A.V. Gonchar, V.Y. Chernina, Mosmeddata: Chest ct scans with covid-19 related findings dataset, arXiv preprint arXiv:2005.06465(2020).","DOI":"10.1101\/2020.05.20.20100362"},{"key":"10.1016\/j.knosys.2026.115828_bib0056","unstructured":"Kaggle, CVC-ClinicDB, (2023). (https:\/\/www.kaggle.com\/datasets\/balraj98\/cvcclinicdb). Accessed 3 May 2023."},{"key":"10.1016\/j.knosys.2026.115828_bib0057","unstructured":"Simula, Kvasir SEG, (2023). (https:\/\/datasets.simula.no\/kvasir-seg\/). Accessed 3 May 2023."},{"key":"10.1016\/j.knosys.2026.115828_bib0058","series-title":"Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition","first-page":"770","article-title":"Deep residual learning for image recognition","author":"He","year":"2016"},{"key":"10.1016\/j.knosys.2026.115828_bib0059","series-title":"Proceedings of the IEEE\/CVF International Conference on Computer Vision","first-page":"10012","article-title":"Swin transformer: hierarchical vision transformer using shifted windows","author":"Liu","year":"2021"},{"key":"10.1016\/j.knosys.2026.115828_bib0060","series-title":"Medical Image Computing and Computer-assisted Intervention\u2013MICCAI 2015: 18th International Conference, Munich, Germany, October 5-9, 2015, Proceedings, Part III 18","first-page":"234","article-title":"U-Net: convolutional networks for biomedical image segmentation","author":"Ronneberger","year":"2015"},{"key":"10.1016\/j.knosys.2026.115828_bib0061","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.knosys.2026.115828_bib0062","unstructured":"P. Iakubovskii, Segmentation Models Pytorch, 2019, (https:\/\/github.com\/qubvel\/segmentation_models.pytorch). Accessed 3 May 2023."},{"key":"10.1016\/j.knosys.2026.115828_bib0063","series-title":"Proceedings of the IEEE\/CVF International Conference on Computer Vision","first-page":"6023","article-title":"Cutmix: regularization strategy to train strong classifiers with localizable features","author":"Yun","year":"2019"}],"container-title":["Knowledge-Based Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S095070512600554X?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S095070512600554X?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,4,14]],"date-time":"2026-04-14T08:35:17Z","timestamp":1776155717000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S095070512600554X"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,5]]},"references-count":63,"alternative-id":["S095070512600554X"],"URL":"https:\/\/doi.org\/10.1016\/j.knosys.2026.115828","relation":{},"ISSN":["0950-7051"],"issn-type":[{"value":"0950-7051","type":"print"}],"subject":[],"published":{"date-parts":[[2026,5]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"MedAugment: Universal automatic data augmentation plug-in for medical image analysis","name":"articletitle","label":"Article Title"},{"value":"Knowledge-Based Systems","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.knosys.2026.115828","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":"115828"}}