{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,22]],"date-time":"2026-06-22T16:55:49Z","timestamp":1782147349355,"version":"3.54.5"},"reference-count":48,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"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":["Biomedical Signal Processing and Control"],"published-print":{"date-parts":[[2026,10]]},"DOI":"10.1016\/j.bspc.2026.110840","type":"journal-article","created":{"date-parts":[[2026,6,22]],"date-time":"2026-06-22T12:06:30Z","timestamp":1782129990000},"page":"110840","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"PA","title":["Discriminative representation learning and dual consistency mining for semi-supervised brain stroke diagnosis on CT images"],"prefix":"10.1016","volume":"126","author":[{"given":"Feng","family":"Li","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qingrun","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaopan","family":"Chen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"issue":"1","key":"10.1016\/j.bspc.2026.110840_b1","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1177\/17474930211065917","article-title":"World Stroke Organization (WSO): global stroke fact sheet 2022","volume":"17","author":"Feigin","year":"2022","journal-title":"Int. J. Stroke"},{"key":"10.1016\/j.bspc.2026.110840_b2","doi-asserted-by":"crossref","DOI":"10.1016\/j.bspc.2024.106193","article-title":"Classification of brain strokes using divergence-based convolutional neural networks","volume":"93","author":"Polat","year":"2024","journal-title":"Biomed. Signal Process. Control."},{"issue":"7","key":"10.1016\/j.bspc.2026.110840_b3","doi-asserted-by":"crossref","first-page":"2064","DOI":"10.1161\/STR.0b013e318296aeca","article-title":"An updated definition of stroke for the 21st century: a statement for healthcare professionals from the American heart association\/American stroke association","volume":"44","author":"Sacco","year":"2013","journal-title":"Stroke"},{"issue":"10193","key":"10.1016\/j.bspc.2026.110840_b4","doi-asserted-by":"crossref","first-page":"139","DOI":"10.1016\/S0140-6736(19)31053-0","article-title":"Extending thrombolysis to 4\u00b75-9 h and wake-up stroke using perfusion imaging: a systematic review and meta-analysis of individual patient data","volume":"394","author":"Campbell","year":"2019","journal-title":"Lancet"},{"key":"10.1016\/j.bspc.2026.110840_b5","doi-asserted-by":"crossref","first-page":"162","DOI":"10.1016\/j.ejrad.2017.08.014","article-title":"Brain ischemia: CT and MRI techniques in acute ischemic stroke","volume":"96","author":"Vilela","year":"2017","journal-title":"Eur. J. Radiol."},{"key":"10.1016\/j.bspc.2026.110840_b6","doi-asserted-by":"crossref","first-page":"46638","DOI":"10.1109\/ACCESS.2025.3550344","article-title":"A Dual-Stream Deep Learning Architecture With Adaptive Random Vector Functional Link for Multi-Center Ischemic Stroke Classification","volume":"13","author":"Anil Inamdar","year":"2025","journal-title":"IEEE Access"},{"key":"10.1016\/j.bspc.2026.110840_b7","doi-asserted-by":"crossref","DOI":"10.1016\/j.bspc.2020.102178","article-title":"Towards effective classification of brain hemorrhagic and ischemic stroke using CNN","volume":"63","author":"Gautam","year":"2021","journal-title":"Biomed. Signal Process. Control."},{"key":"10.1016\/j.bspc.2026.110840_b8","doi-asserted-by":"crossref","DOI":"10.1016\/j.engappai.2022.105772","article-title":"StrokeViT with AutoML for brain stroke classification","volume":"119","author":"Raj","year":"2023","journal-title":"Eng. Appl. Artif. Intell."},{"key":"10.1016\/j.bspc.2026.110840_b9","doi-asserted-by":"crossref","DOI":"10.1016\/j.compbiomed.2025.109711","article-title":"A novel hybrid ViT-LSTM model with explainable AI for brain stroke detection and classification in CT images: A case study of Rajshahi region","volume":"186","author":"Hossain","year":"2025","journal-title":"Comput. Biol. Med."},{"key":"10.1016\/j.bspc.2026.110840_b10","doi-asserted-by":"crossref","unstructured":"M. Chawla, S. Sharma, J. Sivaswamy, L.T. Kishore, A method for automatic detection and classification of stroke from brain CT images, in: International Conference of the IEEE Engineering in Medicine and Biology Society, 2009, pp. 3581\u20133584.","DOI":"10.1109\/IEMBS.2009.5335289"},{"key":"10.1016\/j.bspc.2026.110840_b11","doi-asserted-by":"crossref","unstructured":"M. Grewal, M.M. Srivastava, P. Kumar, S. Varadarajan, RADnet: Radiologist level accuracy using deep learning for hemorrhage detection in CT scans, in: IEEE International Symposium on Biomedical Imaging, ISBI, 2018, pp. 281\u2013284.","DOI":"10.1109\/ISBI.2018.8363574"},{"key":"10.1016\/j.bspc.2026.110840_b12","doi-asserted-by":"crossref","unstructured":"B.R. Gaidhani, R. R.Rajamenakshi, S. Sonavane, Brain Stroke Detection Using Convolutional Neural Network and Deep Learning Models, in: International Conference on Intelligent Communication and Computational Techniques, ICCT, 2019, pp. 242\u2013249.","DOI":"10.1109\/ICCT46177.2019.8969052"},{"issue":"2","key":"10.1016\/j.bspc.2026.110840_b13","first-page":"1","article-title":"Ischemic Stroke Classification Using VGG-16 Convolutional Neural Networks: A Study on Moroccan MRI Scans","volume":"20","author":"Abbaoui","year":"2024","journal-title":"Int. J. Online & Biomed. Eng."},{"key":"10.1016\/j.bspc.2026.110840_b14","doi-asserted-by":"crossref","DOI":"10.1016\/j.compeleceng.2023.109069","article-title":"Stroke classification based on deep reinforcement learning over stroke screening imbalanced data","volume":"114","author":"Zuo","year":"2024","journal-title":"Comput. Electr. Eng."},{"key":"10.1016\/j.bspc.2026.110840_b15","doi-asserted-by":"crossref","unstructured":"D.R. Audyna Sukma Negara, M. Rahardi, A. Aminuddin, F.F. Abdulloh, S. Raj Y, B.P. Asaddulloh, Comparison of Random Search and Grid Search for Optimizing SVM in Stroke Disease Classification, in: International Conference on Computer Sciences, Engineering, and Technology Innovation (ICoCSETI), 2025, pp. 272\u2013277.","DOI":"10.1109\/ICoCSETI63724.2025.11019879"},{"key":"10.1016\/j.bspc.2026.110840_b16","doi-asserted-by":"crossref","unstructured":"D. Wang, Y. Zhang, K. Zhang, L. Wang, FocalMix: Semi-Supervised Learning for 3D Medical Image Detection, in: IEEE Conference on Computer Vision and Pattern Recognition, CVPR, 2020.","DOI":"10.1109\/CVPR42600.2020.00401"},{"key":"10.1016\/j.bspc.2026.110840_b17","doi-asserted-by":"crossref","DOI":"10.1016\/j.media.2021.102117","article-title":"SSMD: Semi-supervised medical image detection with adaptive consistency and heterogeneous perturbation","volume":"72","author":"Zhou","year":"2021","journal-title":"Med. Image Anal."},{"key":"10.1016\/j.bspc.2026.110840_b18","series-title":"Medical Image Computing and Computer Assisted Intervention(MICCAI)","article-title":"ASDNet: Attention Based Semi-supervised Deep Networks for Medical Image Segmentation","author":"Nie","year":"2018"},{"key":"10.1016\/j.bspc.2026.110840_b19","doi-asserted-by":"crossref","unstructured":"W. Zhang, L. Zhu, J. Hallinan, S. Zhang, A. Makmur, Q. Cai, B.C. Ooi, BoostMIS: Boosting Medical Image Semi-Supervised Learning With Adaptive Pseudo Labeling and Informative Active Annotation, 2022.","DOI":"10.1109\/CVPR52688.2022.02001"},{"issue":"1","key":"10.1016\/j.bspc.2026.110840_b20","doi-asserted-by":"crossref","first-page":"7193","DOI":"10.1038\/s41598-018-24876-0","article-title":"A cluster-then-label semi-supervised learning approach for pathology image classification","volume":"8","author":"Peikari","year":"2018","journal-title":"Sci. Rep."},{"key":"10.1016\/j.bspc.2026.110840_b21","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/s41746-018-0065-x","article-title":"Deep echocardiography: data-efficient supervised and semi-supervised deep learning towards automated diagnosis of cardiac disease","volume":"1","author":"Madani","year":"2018","journal-title":"NPJ Digit. Med."},{"key":"10.1016\/j.bspc.2026.110840_b22","series-title":"Medical Image Computing and Computer Assisted Intervention (MICCAI)","first-page":"559","article-title":"Local and Global Consistency Regularized Mean Teacher for Semi-supervised Nuclei Classification","volume":"vol. 11764","author":"Su","year":"2019"},{"issue":"11","key":"10.1016\/j.bspc.2026.110840_b23","doi-asserted-by":"crossref","first-page":"3429","DOI":"10.1109\/TMI.2020.2995518","article-title":"Semi-supervised medical image classification with relation-driven self-ensembling model","volume":"39","author":"Liu","year":"2020","journal-title":"IEEE Trans. Med. Imaging"},{"issue":"9","key":"10.1016\/j.bspc.2026.110840_b24","doi-asserted-by":"crossref","first-page":"10162","DOI":"10.1007\/s10489-022-04012-2","article-title":"Semi-supervised medical image classification via increasing prediction diversity","volume":"53","author":"Liu","year":"2023","journal-title":"Appl. Intell."},{"key":"10.1016\/j.bspc.2026.110840_b25","doi-asserted-by":"crossref","DOI":"10.1016\/j.media.2024.103107","article-title":"Semi-supervised medical image classification via distance correlation minimization and graph attention regularization","volume":"94","author":"Berenguer","year":"2024","journal-title":"Med. Image Anal."},{"issue":"1","key":"10.1016\/j.bspc.2026.110840_b26","doi-asserted-by":"crossref","first-page":"87","DOI":"10.1186\/s12880-020-00485-0","article-title":"Deep semi-supervised learning for brain tumor classification","volume":"20","author":"Ge","year":"2020","journal-title":"BMC Med. Imaging"},{"issue":"Part","key":"10.1016\/j.bspc.2026.110840_b27","article-title":"Semi-supervised medical image classification with adaptive threshold pseudo-labeling and unreliable sample contrastive loss","volume":"79","author":"Peng","year":"2023","journal-title":"Biomed. Signal Process. Control."},{"key":"10.1016\/j.bspc.2026.110840_b28","doi-asserted-by":"crossref","DOI":"10.1016\/j.bspc.2022.103611","article-title":"Fine-grained interactive attention learning for semi-supervised white blood cell classification","volume":"75","author":"Ha","year":"2022","journal-title":"Biomed. Signal Process. Control."},{"key":"10.1016\/j.bspc.2026.110840_b29","doi-asserted-by":"crossref","DOI":"10.1016\/j.media.2021.102148","article-title":"Semi-supervised classification of radiology images with noteacher: A teacher that is not mean","volume":"73","author":"Unnikrishnan","year":"2021","journal-title":"Med. Image Anal."},{"issue":"7","key":"10.1016\/j.bspc.2026.110840_b30","doi-asserted-by":"crossref","first-page":"2983","DOI":"10.1109\/JBHI.2022.3162748","article-title":"Handling Imbalanced Data: Uncertainty-Guided Virtual Adversarial Training With Batch Nuclear-Norm Optimization for Semi-Supervised Medical Image Classification","volume":"26","author":"Liu","year":"2022","journal-title":"IEEE J. Biomed. Health Informatics"},{"issue":"3","key":"10.1016\/j.bspc.2026.110840_b31","doi-asserted-by":"crossref","first-page":"248","DOI":"10.5152\/eurasianjmed.2022.22096","article-title":"Artificial intelligence in healthcare competition (TEKNOFEST-2021): stroke data set","volume":"54","author":"Ko\u00e7","year":"2022","journal-title":"Eurasian J. Med."},{"issue":"12","key":"10.1016\/j.bspc.2026.110840_b32","doi-asserted-by":"crossref","first-page":"326","DOI":"10.1167\/16.12.326","article-title":"A neural algorithm of artistic style","volume":"16","author":"Gatys","year":"2016","journal-title":"J. Vis."},{"key":"10.1016\/j.bspc.2026.110840_b33","unstructured":"D. Berthelot, N. Carlini, E.D. Cubuk, A. Kurakin, K. Sohn, H. Zhang, C. Raffel, ReMixMatch: Semi-Supervised Learning with Distribution Matching and Augmentation Anchoring, in: International Conference on Learning Representations, 2020."},{"key":"10.1016\/j.bspc.2026.110840_b34","doi-asserted-by":"crossref","unstructured":"K. He, X. Zhang, S. Ren, J. Sun, Deep Residual Learning for Image Recognition, in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016, pp. 770\u2013778.","DOI":"10.1109\/CVPR.2016.90"},{"key":"10.1016\/j.bspc.2026.110840_b35","doi-asserted-by":"crossref","unstructured":"E.D. Cubuk, B. Zoph, J. Shlens, Q.V. Le, Randaugment: Practical automated data augmentation with a reduced search space, in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2020, pp. 3008\u20133017.","DOI":"10.1109\/CVPRW50498.2020.00359"},{"key":"10.1016\/j.bspc.2026.110840_b36","doi-asserted-by":"crossref","first-page":"387","DOI":"10.35234\/fumbd.1598597","article-title":"Stroke Classification in Brain Computed Tomography Images Using Vision Transformers and GAN-based Data Augmentation","author":"Yelken","year":"2025","journal-title":"F\u0131Rat \u00dcniversitesi M\u00fchendislik Bilim. Derg., Volume=37"},{"key":"10.1016\/j.bspc.2026.110840_b37","doi-asserted-by":"crossref","DOI":"10.1016\/j.bspc.2022.103720","article-title":"Stroke classification from computed tomography scans using 3D convolutional neural network","volume":"76","author":"Neethi","year":"2022","journal-title":"Biomed. Signal Process. Control."},{"issue":"1","key":"10.1016\/j.bspc.2026.110840_b38","article-title":"Classification of stroke disease using convolutional neural network","volume":"978","author":"Marbun","year":"2018","journal-title":"J. Phys.: Conf. Ser."},{"key":"10.1016\/j.bspc.2026.110840_b39","doi-asserted-by":"crossref","first-page":"797","DOI":"10.1007\/s10044-019-00838-8","article-title":"Local gradient of gradient pattern: a robust image descriptor for the classification of brain strokes from computed tomography images","volume":"23","author":"Gautam","year":"2020","journal-title":"Pattern Anal. Appl."},{"key":"10.1016\/j.bspc.2026.110840_b40","series-title":"Applications of Artificial Intelligence in Medical Imaging","first-page":"207","article-title":"Chapter 7 - Brain stroke detection from computed tomography images using deep learning algorithms","author":"Diker","year":"2023"},{"key":"10.1016\/j.bspc.2026.110840_b41","doi-asserted-by":"crossref","unstructured":"X. Wang, Y. Peng, L. Lu, Z. Lu, M. Bagheri, R.M. Summers, ChestX-Ray8: Hospital-Scale Chest X-Ray Database and Benchmarks on Weakly-Supervised Classification and Localization of Common Thorax Diseases, in: IEEE Conference on Computer Vision and Pattern Recognition, CVPR, 2017, pp. 3462\u20133471.","DOI":"10.1109\/CVPR.2017.369"},{"key":"10.1016\/j.bspc.2026.110840_b42","series-title":"Skin lesion analysis toward melanoma detection 2018: A challenge hosted by the international skin imaging collaboration (ISIC)","author":"Codella","year":"2019"},{"issue":"1","key":"10.1016\/j.bspc.2026.110840_b43","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/sdata.2018.161","article-title":"The HAM10000 dataset, a large collection of multi-source dermatoscopic images of common pigmented skin lesions","volume":"5","author":"Tschandl","year":"2018","journal-title":"Sci. Data"},{"key":"10.1016\/j.bspc.2026.110840_b44","unstructured":"A. Tarvainen, H. Valpola, Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results, in: International Conference on Neural Information Processing Systems, NIPS, 2017, pp. 1195\u20131204."},{"key":"10.1016\/j.bspc.2026.110840_b45","series-title":"Advances in Neural Information Processing Systems(NeurIPS)","first-page":"596","article-title":"FixMatch: Simplifying semi-supervised learning with consistency and confidence","volume":"33","author":"Sohn","year":"2020"},{"key":"10.1016\/j.bspc.2026.110840_b46","doi-asserted-by":"crossref","unstructured":"F. Liu, Y. Tian, Y. Chen, Y. Liu, V. Belagiannis, G. Carneiro, ACPL: Anti-curriculum Pseudo-labelling for Semi-supervised Medical Image Classification, in: IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2022, pp. 20665\u201320674.","DOI":"10.1109\/CVPR52688.2022.02004"},{"key":"10.1016\/j.bspc.2026.110840_b47","doi-asserted-by":"crossref","unstructured":"R.R. Selvaraju, M. Cogswell, A. Das, R. Vedantam, D. Parikh, D. Batra, Grad-CAM: Visual Explanations from Deep Networks via Gradient-Based Localization, in: IEEE International Conference on Computer Vision, ICCV, 2017, pp. 618\u2013626.","DOI":"10.1109\/ICCV.2017.74"},{"key":"10.1016\/j.bspc.2026.110840_b48","series-title":"PyTorch library for CAM methods","author":"Gildenblat","year":"2025"}],"container-title":["Biomedical Signal Processing and Control"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1746809426013947?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1746809426013947?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,6,22]],"date-time":"2026-06-22T16:40:59Z","timestamp":1782146459000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S1746809426013947"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,10]]},"references-count":48,"alternative-id":["S1746809426013947"],"URL":"https:\/\/doi.org\/10.1016\/j.bspc.2026.110840","relation":{},"ISSN":["1746-8094"],"issn-type":[{"value":"1746-8094","type":"print"}],"subject":[],"published":{"date-parts":[[2026,10]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Discriminative representation learning and dual consistency mining for semi-supervised brain stroke diagnosis on CT images","name":"articletitle","label":"Article Title"},{"value":"Biomedical Signal Processing and Control","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.bspc.2026.110840","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":"110840"}}