{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,4]],"date-time":"2026-06-04T23:02:33Z","timestamp":1780614153950,"version":"3.54.1"},"reference-count":62,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,11,1]],"date-time":"2026-11-01T00:00:00Z","timestamp":1793491200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,11,1]],"date-time":"2026-11-01T00:00:00Z","timestamp":1793491200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,11,1]],"date-time":"2026-11-01T00:00:00Z","timestamp":1793491200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,11,1]],"date-time":"2026-11-01T00:00:00Z","timestamp":1793491200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,11,1]],"date-time":"2026-11-01T00:00:00Z","timestamp":1793491200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,11,1]],"date-time":"2026-11-01T00:00:00Z","timestamp":1793491200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,11,1]],"date-time":"2026-11-01T00:00:00Z","timestamp":1793491200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"funder":[{"DOI":"10.13039\/501100015401","name":"Shaanxi Province Key Research and Development Projects","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100015401","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100002858","name":"China Postdoctoral Science Foundation","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100002858","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Neural Networks"],"published-print":{"date-parts":[[2026,11]]},"DOI":"10.1016\/j.neunet.2026.109202","type":"journal-article","created":{"date-parts":[[2026,5,30]],"date-time":"2026-05-30T15:31:54Z","timestamp":1780155114000},"page":"109202","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"C","title":["Revisiting weakly supervised tabular anomaly detection from a cell-level perspective"],"prefix":"10.1016","volume":"203","author":[{"given":"Jiahui","family":"Wang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhen","family":"Peng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xujing","family":"Jia","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qika","family":"Lin","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lan","family":"Ma","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bin","family":"Shi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"issue":"4","key":"10.1016\/j.neunet.2026.109202_bib0001","doi-asserted-by":"crossref","first-page":"433","DOI":"10.1002\/wics.101","article-title":"Principal component analysis","volume":"2","author":"Abdi","year":"2010","journal-title":"Wiley Interdisciplinary Reviews: Computational Statistics"},{"key":"10.1016\/j.neunet.2026.109202_bib0002","series-title":"ICML","article-title":"Scaling out-of-distribution detection for real-world settings","author":"Basart","year":"2022"},{"key":"10.1016\/j.neunet.2026.109202_bib0003","unstructured":"Bengio, Y., L\u00e9onard, N., & Courville, A. (2013). Estimating or propagating gradients through stochastic neurons for conditional computation. arXiv preprint arXiv: 1308.3432,."},{"key":"10.1016\/j.neunet.2026.109202_bib0004","series-title":"Sigmod","first-page":"93","article-title":"Lof: Identifying density-based local outliers","author":"Breunig","year":"2000"},{"key":"10.1016\/j.neunet.2026.109202_bib0005","doi-asserted-by":"crossref","DOI":"10.1016\/j.media.2025.103500","article-title":"Medianomaly: A comparative study of anomaly detection in medical images","author":"Cai","year":"2025","journal-title":"Medical Image Analysis"},{"key":"10.1016\/j.neunet.2026.109202_bib0006","doi-asserted-by":"crossref","first-page":"329","DOI":"10.1016\/j.patcog.2017.10.009","article-title":"Multiple instance learning: A survey of problem characteristics and applications","volume":"77","author":"Carbonneau","year":"2018","journal-title":"Pattern Recognition"},{"key":"10.1016\/j.neunet.2026.109202_bib0007","series-title":"KDD","first-page":"190","article-title":"Data-efficient and interpretable tabular anomaly detection","author":"Chang","year":"2023"},{"key":"10.1016\/j.neunet.2026.109202_bib0008","series-title":"AAAI","first-page":"11553","article-title":"Unsupervised anomaly detection for tabular data using deep noise evaluation","volume":"vol. 39","author":"Dai","year":"2025"},{"issue":"8","key":"10.1016\/j.neunet.2026.109202_bib0009","doi-asserted-by":"crossref","first-page":"3784","DOI":"10.1109\/TNNLS.2017.2736643","article-title":"Credit card fraud detection: A realistic modeling and a novel learning strategy","volume":"29","author":"Dal Pozzolo","year":"2017","journal-title":"IEEE Transactions on Neural Networks and Learning Systems"},{"key":"10.1016\/j.neunet.2026.109202_bib0010","series-title":"SDM","first-page":"594","article-title":"Deep anomaly detection on attributed networks","author":"Ding","year":"2019"},{"key":"10.1016\/j.neunet.2026.109202_bib0011","first-page":"1229","article-title":"Asymmetric shapley values: Incorporating causal knowledge into model-agnostic explainability","volume":"33","author":"Frye","year":"2020","journal-title":"Advances in Neural Information Processing Systems"},{"key":"10.1016\/j.neunet.2026.109202_bib0012","series-title":"Www","first-page":"2111","article-title":"Semi-supervised anomaly detection through denoising-aware contrastive distance learning","author":"Gao","year":"2025"},{"issue":"1\u20132","key":"10.1016\/j.neunet.2026.109202_bib0013","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1016\/j.cose.2008.08.003","article-title":"Anomaly-based network intrusion detection: Techniques, systems and challenges","volume":"28","author":"Garcia-Teodoro","year":"2009","journal-title":"Computers & Security"},{"key":"10.1016\/j.neunet.2026.109202_bib0014","series-title":"AAAI","first-page":"6737","article-title":"Lunar: Unifying local outlier detection methods via graph neural networks","volume":"vol. 36","author":"Goodge","year":"2022"},{"key":"10.1016\/j.neunet.2026.109202_bib0015","first-page":"18932","article-title":"Revisiting deep learning models for tabular data","volume":"34","author":"Gorishniy","year":"2021","journal-title":"Advances in Neural Information Processing Systems"},{"key":"10.1016\/j.neunet.2026.109202_bib0016","series-title":"ICCV","first-page":"1026","article-title":"Delving deep into rectifiers: Surpassing human-level performance on imagenet classification","author":"He","year":"2015"},{"issue":"11","key":"10.1016\/j.neunet.2026.109202_bib0017","doi-asserted-by":"crossref","first-page":"3287","DOI":"10.1109\/TNNLS.2018.2890663","article-title":"Utilizing unlabeled data to detect electricity fraud in AMI: A semisupervised deep learning approach","volume":"30","author":"Hu","year":"2019","journal-title":"IEEE Transactions on Neural Networks and Learning Systems"},{"key":"10.1016\/j.neunet.2026.109202_bib0018","first-page":"677","article-title":"On the importance of gradients for detecting distributional shifts in the wild","volume":"34","author":"Huang","year":"2021","journal-title":"Advances in Neural Information Processing Systems"},{"key":"10.1016\/j.neunet.2026.109202_bib0019","unstructured":"Jiang, J.-P., Liu, S.-Y., Cai, H.-R., Zhou, Q., & Ye, H.-J. (2025). Representation learning for tabular data: A comprehensive survey. arXiv preprint arXiv: 2504.16109."},{"key":"10.1016\/j.neunet.2026.109202_bib0020","series-title":"ICLR","article-title":"Negative label guided OOD detection with pretrained vision-language models","author":"Jiang","year":"2024"},{"key":"10.1016\/j.neunet.2026.109202_bib0021","series-title":"ICLR","article-title":"Adam: A method for stochastic optimization","author":"Kingma","year":"2015"},{"key":"10.1016\/j.neunet.2026.109202_bib0022","unstructured":"Kipf, T. N. (2016). Semi-supervised classification with graph convolutional networks. arXiv preprint arXiv: 1609.02907."},{"key":"10.1016\/j.neunet.2026.109202_bib0023","series-title":"IJCAI","first-page":"2801","article-title":"The dangers of post-hoc interpretability: Unjustified counterfactual explanations","author":"Laugel","year":"2019"},{"key":"10.1016\/j.neunet.2026.109202_bib0024","series-title":"CVPR","first-page":"460","article-title":"AdaSTE: An adaptive straight-through estimator to train binary neural networks","author":"Le","year":"2022"},{"key":"10.1016\/j.neunet.2026.109202_bib0025","series-title":"ICLR","article-title":"Training confidence-calibrated classifiers for detecting out-of-distribution samples","author":"Lee","year":"2018"},{"issue":"1","key":"10.1016\/j.neunet.2026.109202_bib0026","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3744918","article-title":"Graph neural networks for tabular data learning: A survey with taxonomy and directions","volume":"58","author":"Li","year":"2025","journal-title":"ACM Computing Surveys"},{"key":"10.1016\/j.neunet.2026.109202_bib0027","first-page":"21464","article-title":"Energy-based out-of-distribution detection","volume":"33","author":"Liu","year":"2020","journal-title":"Advances in Neural Information Processing Systems"},{"key":"10.1016\/j.neunet.2026.109202_bib0028","series-title":"ICLR","article-title":"Learning with mixture of prototypes for out-of-distribution detection","author":"Lu","year":"2024"},{"key":"10.1016\/j.neunet.2026.109202_bib0029","series-title":"NeurIPS","first-page":"4768","article-title":"A unified approach to interpreting model predictions","author":"Lundberg","year":"2017"},{"key":"10.1016\/j.neunet.2026.109202_bib0030","series-title":"EMNLP","first-page":"19722","article-title":"Dsmoe: Matrix-partitioned experts with dynamic routing for computation-efficient dense llms","author":"Lv","year":"2025"},{"key":"10.1016\/j.neunet.2026.109202_bib0031","first-page":"35087","article-title":"Delving into out-of-distribution detection with vision-language representations","volume":"35","author":"Ming","year":"2022","journal-title":"Advances in Neural Information Processing Systems"},{"key":"10.1016\/j.neunet.2026.109202_bib0032","series-title":"ICLR","article-title":"How to exploit hyperspherical embeddings for out-of-distribution detection?","author":"Ming","year":"2023"},{"key":"10.1016\/j.neunet.2026.109202_bib0033","doi-asserted-by":"crossref","DOI":"10.1016\/j.patcog.2024.111149","article-title":"Corruption-based anomaly detection and interpretation in tabular data","volume":"159","author":"Mok","year":"2025","journal-title":"Pattern Recognition"},{"key":"10.1016\/j.neunet.2026.109202_bib0034","series-title":"KDD","first-page":"2041","article-title":"Learning representations of ultrahigh-dimensional data for random distance-based outlier detection","author":"Pang","year":"2018"},{"key":"10.1016\/j.neunet.2026.109202_bib0035","series-title":"KDD","first-page":"1795","article-title":"Deep weakly-supervised anomaly detection","author":"Pang","year":"2023"},{"key":"10.1016\/j.neunet.2026.109202_bib0036","series-title":"KDD","first-page":"353","article-title":"Deep anomaly detection with deviation networks","author":"Pang","year":"2019"},{"key":"10.1016\/j.neunet.2026.109202_bib0037","series-title":"NeurIPS","article-title":"An information-theoretical framework for understanding out-of-distribution detection with pretrained vision-language models","author":"Peng","year":"2025"},{"key":"10.1016\/j.neunet.2026.109202_bib0038","series-title":"KDD","first-page":"1104","article-title":"Distributional prototype learning for out-of-distribution detection","author":"Peng","year":"2025"},{"key":"10.1016\/j.neunet.2026.109202_bib0039","series-title":"ICLR","article-title":"Conjnorm: Tractable density estimation for out-of-distribution detection","author":"Peng","year":"2024"},{"issue":"2","key":"10.1016\/j.neunet.2026.109202_bib0040","doi-asserted-by":"crossref","first-page":"88","DOI":"10.1080\/00031305.1994.10476030","article-title":"The three sigma rule","volume":"48","author":"Pukelsheim","year":"1994","journal-title":"The American Statistician"},{"key":"10.1016\/j.neunet.2026.109202_bib0041","series-title":"KDD","first-page":"1135","article-title":"\u201cWhy should I trust you?\u201d Explaining the predictions of any classifier","author":"Ribeiro","year":"2016"},{"key":"10.1016\/j.neunet.2026.109202_bib0042","series-title":"ICML","first-page":"4393","article-title":"Deep one-class classification","author":"Ruff","year":"2018"},{"key":"10.1016\/j.neunet.2026.109202_bib0043","series-title":"ICLR","article-title":"Deep semi-supervised anomaly detection","author":"Ruff","year":"2020"},{"key":"10.1016\/j.neunet.2026.109202_bib0044","series-title":"ICLR","article-title":"Anomaly detection for tabular data with internal contrastive learning","author":"Shenkar","year":"2022"},{"issue":"6","key":"10.1016\/j.neunet.2026.109202_bib0045","doi-asserted-by":"crossref","first-page":"3537","DOI":"10.1109\/TSMC.2026.3660284","article-title":"Pseudo-label refinement for multimodal unsupervised domain adaptation","volume":"56","author":"Shi","year":"2026","journal-title":"IEEE Transactions on Systems, Man, and Cybernetics: Systems"},{"key":"10.1016\/j.neunet.2026.109202_bib0046","series-title":"ICML","first-page":"3145","article-title":"Learning important features through propagating activation differences","author":"Shrikumar","year":"2017"},{"key":"10.1016\/j.neunet.2026.109202_bib0047","series-title":"NeurIPS 2022 workshop TRL","article-title":"Saint: Improved neural networks for tabular data via row attention and contrastive pre-training","author":"Somepalli","year":"2022"},{"key":"10.1016\/j.neunet.2026.109202_bib0048","doi-asserted-by":"crossref","first-page":"249","DOI":"10.1016\/j.neunet.2020.06.005","article-title":"Missing data imputation with adversarially-trained graph convolutional networks","volume":"129","author":"Spinelli","year":"2020","journal-title":"Neural Networks"},{"key":"10.1016\/j.neunet.2026.109202_bib0049","series-title":"IJCAI","first-page":"2370","article-title":"Semanticmask: A contrastive view design for anomaly detection in tabular data","author":"Tao","year":"2024"},{"key":"10.1016\/j.neunet.2026.109202_bib0050","doi-asserted-by":"crossref","DOI":"10.1016\/j.neunet.2024.106180","article-title":"Graph neural network contextual embedding for deep learning on tabular data","volume":"173","author":"Villaiz\u00e1n-Vallelado","year":"2024","journal-title":"Neural Networks"},{"key":"10.1016\/j.neunet.2026.109202_bib0051","series-title":"KDD","first-page":"3122","article-title":"Graph evidential learning for anomaly detection","author":"Wei","year":"2025"},{"issue":"1","key":"10.1016\/j.neunet.2026.109202_bib0052","doi-asserted-by":"crossref","first-page":"4","DOI":"10.1109\/TNNLS.2020.2978386","article-title":"A comprehensive survey on graph neural networks","volume":"32","author":"Wu","year":"2020","journal-title":"IEEE Transactions on Neural Networks and Learning Systems"},{"key":"10.1016\/j.neunet.2026.109202_bib0053","series-title":"AAAI","first-page":"3110","article-title":"Learning semantic context from normal samples for unsupervised anomaly detection","volume":"vol. 35","author":"Yan","year":"2021"},{"key":"10.1016\/j.neunet.2026.109202_bib0054","series-title":"AAAI","first-page":"13061","article-title":"Disentangling tabular data towards better one-class anomaly detection","volume":"vol. 39","author":"Ye","year":"2025"},{"key":"10.1016\/j.neunet.2026.109202_bib0055","series-title":"ICLR","article-title":"MCM: Masked cell modeling for anomaly detection in tabular data","author":"Yin","year":"2024"},{"key":"10.1016\/j.neunet.2026.109202_bib0056","first-page":"11033","article-title":"Vime: Extending the success of self-and semi-supervised learning to tabular domain","volume":"33","author":"Yoon","year":"2020","journal-title":"Advances in Neural Information Processing Systems"},{"key":"10.1016\/j.neunet.2026.109202_bib0057","series-title":"CVPR","first-page":"15701","article-title":"Block selection method for using feature norm in out-of-distribution detection","author":"Yu","year":"2023"},{"key":"10.1016\/j.neunet.2026.109202_bib0058","first-page":"49384","article-title":"Learning to shape in-distribution feature space for out-of-distribution detection","volume":"37","author":"Zhang","year":"2024","journal-title":"Advances in Neural Information Processing Systems"},{"issue":"9","key":"10.1016\/j.neunet.2026.109202_bib0059","doi-asserted-by":"crossref","first-page":"9715","DOI":"10.1109\/TKDE.2023.3249186","article-title":"Deep tabular data modeling with dual-route structure-adaptive graph networks","volume":"35","author":"Zheng","year":"2023","journal-title":"IEEE Transactions on Knowledge and Data Engineering"},{"key":"10.1016\/j.neunet.2026.109202_bib0060","series-title":"IJCAI","first-page":"2420","article-title":"Table2Graph: Transforming tabular data to unified weighted graph","author":"Zhou","year":"2022"},{"issue":"6","key":"10.1016\/j.neunet.2026.109202_bib0061","doi-asserted-by":"crossref","first-page":"2454","DOI":"10.1109\/TNNLS.2021.3086137","article-title":"Feature encoding with autoencoders for weakly supervised anomaly detection","volume":"33","author":"Zhou","year":"2021","journal-title":"IEEE Transactions on Neural Networks and Learning Systems"},{"issue":"1","key":"10.1016\/j.neunet.2026.109202_bib0062","doi-asserted-by":"crossref","first-page":"44","DOI":"10.1093\/nsr\/nwx106","article-title":"A brief introduction to weakly supervised learning","volume":"5","author":"Zhou","year":"2018","journal-title":"National Science Review"}],"container-title":["Neural Networks"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0893608026006635?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0893608026006635?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,6,4]],"date-time":"2026-06-04T22:08:59Z","timestamp":1780610939000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0893608026006635"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,11]]},"references-count":62,"alternative-id":["S0893608026006635"],"URL":"https:\/\/doi.org\/10.1016\/j.neunet.2026.109202","relation":{},"ISSN":["0893-6080"],"issn-type":[{"value":"0893-6080","type":"print"}],"subject":[],"published":{"date-parts":[[2026,11]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Revisiting weakly supervised tabular anomaly detection from a cell-level perspective","name":"articletitle","label":"Article Title"},{"value":"Neural Networks","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.neunet.2026.109202","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":"109202"}}