{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,24]],"date-time":"2026-08-24T18:32:57Z","timestamp":1787596377960,"version":"build-2736575974"},"reference-count":70,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,12,1]],"date-time":"2026-12-01T00:00:00Z","timestamp":1796083200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,12,1]],"date-time":"2026-12-01T00:00:00Z","timestamp":1796083200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,12,1]],"date-time":"2026-12-01T00:00:00Z","timestamp":1796083200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,12,1]],"date-time":"2026-12-01T00:00:00Z","timestamp":1796083200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,12,1]],"date-time":"2026-12-01T00:00:00Z","timestamp":1796083200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,12,1]],"date-time":"2026-12-01T00:00:00Z","timestamp":1796083200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,12,1]],"date-time":"2026-12-01T00:00:00Z","timestamp":1796083200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"funder":[{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2024YFA1802800"],"award-info":[{"award-number":["2024YFA1802800"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["32341012"],"award-info":[{"award-number":["32341012"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62372170"],"award-info":[{"award-number":["62372170"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Neurocomputing"],"published-print":{"date-parts":[[2026,12]]},"DOI":"10.1016\/j.neucom.2026.134807","type":"journal-article","created":{"date-parts":[[2026,8,13]],"date-time":"2026-08-13T16:09:00Z","timestamp":1786637340000},"page":"134807","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"C","title":["CSFMIL: Whole slide image classification with two-stage cross-scale fusion"],"prefix":"10.1016","volume":"704","author":[{"given":"Shijie","family":"Li","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yang","family":"Luo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhineng","family":"Chen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Feng-Jung","family":"Chen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kai","family":"Hu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xieping","family":"Gao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.neucom.2026.134807_bib0005","doi-asserted-by":"crossref","DOI":"10.1016\/j.media.2020.101813","article-title":"Deep neural network models for computational histopathology: a survey","volume":"67","author":"Srinidhi","year":"2021","journal-title":"Med. Image Anal."},{"key":"10.1016\/j.neucom.2026.134807_bib0010","doi-asserted-by":"crossref","first-page":"331","DOI":"10.1146\/annurev-pathol-011811-120902","article-title":"Digital imaging in pathology: whole-slide imaging and beyond","volume":"8","author":"Ghaznavi","year":"2013","journal-title":"Annu. Rev. Pathol. Mech. Dis."},{"key":"10.1016\/j.neucom.2026.134807_bib0015","doi-asserted-by":"crossref","first-page":"181","DOI":"10.1038\/s41586-024-07441-w","article-title":"A whole-slide foundation model for digital pathology from real-world data","volume":"630","author":"Xu","year":"2024","journal-title":"Nature"},{"key":"10.1016\/j.neucom.2026.134807_bib0020","doi-asserted-by":"crossref","first-page":"1301","DOI":"10.1038\/s41591-019-0508-1","article-title":"Clinical-grade computational pathology using weakly supervised deep learning on whole slide images","volume":"25","author":"Campanella","year":"2019","journal-title":"Nat. Med."},{"key":"10.1016\/j.neucom.2026.134807_bib0025","doi-asserted-by":"crossref","DOI":"10.1016\/j.neucom.2025.130797","article-title":"A novel lightweight deep attention network for automated nuclei segmentation in histopathology images","volume":"649","author":"Bagri","year":"2025","journal-title":"Neurocomputing"},{"key":"10.1016\/j.neucom.2026.134807_bib0030","doi-asserted-by":"crossref","DOI":"10.1016\/j.neucom.2025.132365","article-title":"Gene-guided multimodal data fusion for cancer patient survival analysis","volume":"668","author":"Xu","year":"2026","journal-title":"Neurocomputing"},{"key":"10.1016\/j.neucom.2026.134807_bib0035","doi-asserted-by":"crossref","first-page":"1559","DOI":"10.1038\/s41591-018-0177-5","article-title":"Classification and mutation prediction from non\u2013small cell lung cancer histopathology images using deep learning","volume":"24","author":"Coudray","year":"2018","journal-title":"Nat. Med."},{"key":"10.1016\/j.neucom.2026.134807_bib0040","doi-asserted-by":"crossref","first-page":"170","DOI":"10.1016\/j.media.2016.06.037","article-title":"Image analysis and machine learning in digital pathology: challenges and opportunities","volume":"33","author":"Madabhushi","year":"2016","journal-title":"Med. Image Anal."},{"key":"10.1016\/j.neucom.2026.134807_bib0045","series-title":"Advances in Neural Information Processing Systems","article-title":"A framework for multiple-instance learning","volume":"vol. 10","author":"Maron","year":"1997"},{"key":"10.1016\/j.neucom.2026.134807_bib0050","series-title":"Proceedings of the International Conference on Machine Learning","first-page":"2127","article-title":"Attention-based deep multiple instance learning","author":"Ilse","year":"2018"},{"key":"10.1016\/j.neucom.2026.134807_bib0055","series-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition","first-page":"14318","article-title":"Dual-stream multiple instance learning network for whole slide image classification with self-supervised contrastive learning","author":"Li","year":"2021"},{"key":"10.1016\/j.neucom.2026.134807_bib0060","series-title":"Advances in Neural Information Processing Systems","first-page":"2136","article-title":"TransMIL: transformer based correlated multiple instance learning for whole slide image classification","volume":"vol. 34","author":"Shao","year":"2021"},{"key":"10.1016\/j.neucom.2026.134807_bib0065","series-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition","first-page":"18802","article-title":"DTFD-MIL: double-tier feature distillation multiple instance learning for histopathology whole slide image classification","author":"Zhang","year":"2022"},{"key":"10.1016\/j.neucom.2026.134807_bib0070","doi-asserted-by":"crossref","DOI":"10.1016\/j.neucom.2023.126736","article-title":"Predicting cancer outcomes from whole slide images via hybrid supervision learning","volume":"557","author":"He","year":"2023","journal-title":"Neurocomputing"},{"key":"10.1016\/j.neucom.2026.134807_bib0075","doi-asserted-by":"crossref","first-page":"312","DOI":"10.1016\/j.neucom.2020.04.153","article-title":"Automatic whole slide pathology image diagnosis framework via unit stochastic selection and attention fusion","volume":"453","author":"Chen","year":"2021","journal-title":"Neurocomputing"},{"key":"10.1016\/j.neucom.2026.134807_bib0080","doi-asserted-by":"crossref","DOI":"10.1016\/j.bspc.2025.109289","article-title":"Whole slide image redundancy reduction for efficient pathological diagnosis","volume":"114","author":"Li","year":"2026","journal-title":"Biomed. Signal Process. Control"},{"key":"10.1016\/j.neucom.2026.134807_bib0085","series-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Findings","first-page":"5336","article-title":"AceMIL: ordinal-aware multiple instance learning for pathological progression analysis","author":"Li","year":"2026"},{"key":"10.1016\/j.neucom.2026.134807_bib0090","doi-asserted-by":"crossref","first-page":"2199","DOI":"10.1001\/jama.2017.14585","article-title":"Diagnostic assessment of deep learning algorithms for detection of lymph node metastases in women with breast cancer","volume":"318","author":"Bejnordi","year":"2017","journal-title":"JAMA"},{"key":"10.1016\/j.neucom.2026.134807_bib0095","series-title":"2021 IEEE International Conference on Image Processing (ICIP)","first-page":"76","article-title":"Unitopatho, a labeled histopathological dataset for colorectal polyps classification and adenoma dysplasia grading","author":"Barbano","year":"2021"},{"key":"10.1016\/j.neucom.2026.134807_bib0100","series-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition","first-page":"19798","article-title":"Hierarchical discriminative learning improves visual representations of biomedical microscopy","author":"Jiang","year":"2023"},{"key":"10.1016\/j.neucom.2026.134807_bib0105","series-title":"Proceedings of the International Conference on Medical Image Computing and Computer-Assisted Intervention","first-page":"186","article-title":"Transpath: transformer-based self-supervised learning for histopathological image classification","author":"Wang","year":"2021"},{"key":"10.1016\/j.neucom.2026.134807_bib0110","series-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition","first-page":"16144","article-title":"Scaling vision transformers to gigapixel images via hierarchical self-supervised learning","author":"Chen","year":"2022"},{"key":"10.1016\/j.neucom.2026.134807_bib0115","series-title":"Proceedings of the International Joint Conference on Artificial Intelligence","first-page":"1587","article-title":"Diagnose like a pathologist: transformer-enabled hierarchical attention-guided multiple instance learning for whole slide image classification","author":"Xiong","year":"2023"},{"key":"10.1016\/j.neucom.2026.134807_bib0120","series-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition","first-page":"9650","article-title":"Emerging properties in self-supervised vision transformers","author":"Caron","year":"2021"},{"key":"10.1016\/j.neucom.2026.134807_bib0125","doi-asserted-by":"crossref","first-page":"7206","DOI":"10.1109\/JBHI.2024.3439499","article-title":"Cross-attention-based saliency inference for predicting cancer metastasis on whole slide images","volume":"28","author":"Su","year":"2024","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"10.1016\/j.neucom.2026.134807_bib0130","doi-asserted-by":"crossref","first-page":"3700","DOI":"10.1109\/JBHI.2020.3040269","article-title":"A deep learning approach for colonoscopy pathology WSI analysis: accurate segmentation and classification","volume":"25","author":"Feng","year":"2021","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"10.1016\/j.neucom.2026.134807_bib0135","doi-asserted-by":"crossref","first-page":"396","DOI":"10.1109\/JBHI.2024.3429188","article-title":"Partial-label contrastive representation learning for fine-grained biomarkers prediction from histopathology whole slide images","volume":"29","author":"Zheng","year":"2025","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"10.1016\/j.neucom.2026.134807_bib0140","doi-asserted-by":"crossref","DOI":"10.1016\/j.neucom.2026.132745","article-title":"xMagNet: dynamic magnification-aware fusion with uncertainty quantification for robust breast cancer histopathology","author":"Iqbal","year":"2026","journal-title":"Neurocomputing"},{"key":"10.1016\/j.neucom.2026.134807_bib0145","doi-asserted-by":"crossref","DOI":"10.1016\/j.neucom.2024.128787","article-title":"Whole slide cervical cancer classification via graph attention networks and contrastive learning","volume":"613","author":"Fei","year":"2025","journal-title":"Neurocomputing"},{"key":"10.1016\/j.neucom.2026.134807_bib0150","series-title":"IEEE ICASSP","first-page":"947","article-title":"Deep convolutional activation features for large scale brain tumor histopathology image classification and segmentation","author":"Xu","year":"2015"},{"key":"10.1016\/j.neucom.2026.134807_bib0155","series-title":"Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition","first-page":"2424","article-title":"Patch-based convolutional neural network for whole slide tissue image classification","author":"Hou","year":"2016"},{"key":"10.1016\/j.neucom.2026.134807_bib0160","series-title":"Advances in Neural Information Processing Systems","first-page":"5998","article-title":"Attention is all you need","volume":"vol. 30","author":"Vaswani","year":"2017"},{"key":"10.1016\/j.neucom.2026.134807_bib0165","series-title":"Proceedings of the IEEE\/CVF International Conference on Computer Vision","first-page":"4078","article-title":"Multiple instance learning framework with masked hard instance mining for whole slide image classification","author":"Tang","year":"2023"},{"key":"10.1016\/j.neucom.2026.134807_bib0170","series-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition","first-page":"19830","article-title":"Interventional bag multi-instance learning on whole-slide pathological images","author":"Lin","year":"2023"},{"key":"10.1016\/j.neucom.2026.134807_bib0175","series-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition","first-page":"11566","article-title":"Morphological prototyping for unsupervised slide representation learning in computational pathology","author":"Song","year":"2024"},{"key":"10.1016\/j.neucom.2026.134807_bib0180","series-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition","first-page":"9729","article-title":"Momentum contrast for unsupervised visual representation learning","author":"He","year":"2020"},{"key":"10.1016\/j.neucom.2026.134807_bib0185","series-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition","first-page":"16000","article-title":"Masked autoencoders are scalable vision learners","author":"He","year":"2022"},{"key":"10.1016\/j.neucom.2026.134807_bib0190","author":"Xie"},{"key":"10.1016\/j.neucom.2026.134807_bib0195","series-title":"Proceedings of the International Conference on Medical Image Computing and Computer-Assisted Intervention","first-page":"47","article-title":"Self-supervised visual representation learning for histopathological images","author":"Yang","year":"2021"},{"key":"10.1016\/j.neucom.2026.134807_bib0200","doi-asserted-by":"crossref","DOI":"10.1016\/j.patcog.2024.110745","article-title":"Global contrast masked autoencoders are powerful pathological representation learners","volume":"156","author":"Quan","year":"2024","journal-title":"Pattern Recognit."},{"key":"10.1016\/j.neucom.2026.134807_bib0205","series-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition","first-page":"3344","article-title":"Benchmarking self-supervised learning on diverse pathology datasets","author":"Kang","year":"2023"},{"key":"10.1016\/j.neucom.2026.134807_bib0210","doi-asserted-by":"crossref","first-page":"863","DOI":"10.1038\/s41591-024-02856-4","article-title":"A visual-language foundation model for computational pathology","volume":"30","author":"Lu","year":"2024","journal-title":"Nat. Med."},{"key":"10.1016\/j.neucom.2026.134807_bib0215","series-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition","first-page":"9632","article-title":"Transcriptomics-guided slide representation learning in computational pathology","author":"Jaume","year":"2024"},{"key":"10.1016\/j.neucom.2026.134807_bib0220","author":"He"},{"key":"10.1016\/j.neucom.2026.134807_bib0225","author":"Xu"},{"key":"10.1016\/j.neucom.2026.134807_bib0230","doi-asserted-by":"crossref","first-page":"433","DOI":"10.1093\/biomet\/58.3.433","article-title":"Canonical analysis of several sets of variables","volume":"58","author":"Kettenring","year":"1971","journal-title":"Biometrika"},{"key":"10.1016\/j.neucom.2026.134807_bib0235","doi-asserted-by":"crossref","first-page":"1863","DOI":"10.1109\/TKDE.2018.2872063","article-title":"A survey of multi-view representation learning","volume":"31","author":"Li","year":"2019","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"10.1016\/j.neucom.2026.134807_bib0240","doi-asserted-by":"crossref","first-page":"4","DOI":"10.1016\/j.inffus.2013.12.002","article-title":"Medical image fusion: a survey of the state of the art","volume":"19","author":"James","year":"2014","journal-title":"Inf. Fusion"},{"key":"10.1016\/j.neucom.2026.134807_bib0245","series-title":"Proceedings of the IEEE\/CVF International Conference on Computer Vision","first-page":"2998","article-title":"Multi-scale vision longformer: a new vision transformer for high-resolution image encoding","author":"Zhang","year":"2021"},{"key":"10.1016\/j.neucom.2026.134807_bib0250","doi-asserted-by":"crossref","DOI":"10.1016\/j.compbiomed.2023.106959","article-title":"Deep learning methods for medical image fusion: a review","volume":"160","author":"Zhou","year":"2023","journal-title":"Comput. Biol. Med."},{"key":"10.1016\/j.neucom.2026.134807_bib0255","doi-asserted-by":"crossref","first-page":"323","DOI":"10.1016\/j.inffus.2021.06.008","article-title":"Image fusion meets deep learning: a survey and perspective","volume":"76","author":"Zhang","year":"2021","journal-title":"Inf. Fusion"},{"key":"10.1016\/j.neucom.2026.134807_bib0260","doi-asserted-by":"crossref","first-page":"444","DOI":"10.1109\/TMI.2013.2287121","article-title":"Hierarchical manifold learning for regional image analysis","volume":"33","author":"Bhatia","year":"2014","journal-title":"IEEE Trans. Med. Imaging"},{"key":"10.1016\/j.neucom.2026.134807_bib0265","series-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition","first-page":"3852","article-title":"Multi-scale domain-adversarial multiple-instance CNN for cancer subtype classification with unannotated histopathological images","author":"Hashimoto","year":"2020"},{"key":"10.1016\/j.neucom.2026.134807_bib0270","doi-asserted-by":"crossref","DOI":"10.1016\/j.compmedimag.2024.102337","article-title":"Multiple instance learning for digital pathology: a review of the state-of-the-art, limitations & future potential","volume":"112","author":"Gadermayr","year":"2024","journal-title":"Comput. Med. Imaging Graph."},{"key":"10.1016\/j.neucom.2026.134807_bib0275","author":"Wang"},{"key":"10.1016\/j.neucom.2026.134807_bib0280","doi-asserted-by":"crossref","DOI":"10.1016\/j.media.2024.103109","article-title":"LESS: label-efficient multi-scale learning for cytological whole slide image screening","volume":"94","author":"Zhao","year":"2024","journal-title":"Med. Image Anal."},{"key":"10.1016\/j.neucom.2026.134807_bib0285","doi-asserted-by":"crossref","DOI":"10.1016\/j.engappai.2024.109300","article-title":"Multi-scale multi-instance contrastive learning for whole slide image classification","volume":"138","author":"Zhang","year":"2024","journal-title":"Eng. Appl. Artif. Intell."},{"key":"10.1016\/j.neucom.2026.134807_bib0290","series-title":"Proceedings of the IEEE\/CVF International Conference on Computer Vision","first-page":"11248","article-title":"ViLa-MIL: dual-scale vision-language multiple instance learning for whole slide image classification","author":"Shi","year":"2024"},{"key":"10.1016\/j.neucom.2026.134807_bib0295","doi-asserted-by":"crossref","DOI":"10.1016\/j.media.2024.103124","article-title":"Cross-scale multi-instance learning for pathological image diagnosis","volume":"94","author":"Deng","year":"2024","journal-title":"Med. Image Anal."},{"key":"10.1016\/j.neucom.2026.134807_bib0300","author":"Oord"},{"key":"10.1016\/j.neucom.2026.134807_bib0305","series-title":"Proceedings of the International Conference on Learning Representations","article-title":"On mutual information maximization for representation learning","author":"Tschannen","year":"2020"},{"key":"10.1016\/j.neucom.2026.134807_bib0310","series-title":"Advances in Neural Information Processing Systems","first-page":"6827","article-title":"What makes for good views for contrastive learning?","volume":"vol. 33","author":"Tian","year":"2020"},{"key":"10.1016\/j.neucom.2026.134807_bib0315","series-title":"2021 IEEE\/CVF International Conference on Computer Vision (ICCV)","first-page":"9620","article-title":"An empirical study of training self-supervised vision transformers","author":"Chen","year":"2021"},{"key":"10.1016\/j.neucom.2026.134807_bib0320","series-title":"Advances in Neural Information Processing Systems","first-page":"21271","article-title":"Bootstrap your own latent-a new approach to self-supervised learning","volume":"vol. 33","author":"Grill","year":"2020"},{"key":"10.1016\/j.neucom.2026.134807_bib0325","series-title":"Advances in Neural Information Processing Systems","first-page":"9912","article-title":"Unsupervised learning of visual features by contrasting cluster assignments","volume":"vol. 33","author":"Caron","year":"2020"},{"key":"10.1016\/j.neucom.2026.134807_bib0330","series-title":"Proceedings of the International Conference on Learning Representations","article-title":"Decoupled weight decay regularization","author":"Loshchilov","year":"2019"},{"key":"10.1016\/j.neucom.2026.134807_bib0335","series-title":"Proceedings of the International Conference on Learning Representations","article-title":"SGDR: stochastic gradient descent with warm restarts","author":"Loshchilov","year":"2017"},{"key":"10.1016\/j.neucom.2026.134807_bib0340","article-title":"Visualizing data using t-SNE","volume":"9","author":"Van der Maaten","year":"2008","journal-title":"J. Mach. Learn. Res."},{"key":"10.1016\/j.neucom.2026.134807_bib0345","doi-asserted-by":"crossref","first-page":"850","DOI":"10.1038\/s41591-024-02857-3","article-title":"Towards a general-purpose foundation model for computational pathology","volume":"30","author":"Chen","year":"2024","journal-title":"Nat. Med."},{"key":"10.1016\/j.neucom.2026.134807_bib0350","first-page":"611","article-title":"Virchow: a million-slide digital pathology foundation model","volume":"30","author":"Vorontsov","year":"2024","journal-title":"Nat. Med."}],"container-title":["Neurocomputing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0925231226022058?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0925231226022058?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,8,24]],"date-time":"2026-08-24T18:12:38Z","timestamp":1787595158000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0925231226022058"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,12]]},"references-count":70,"alternative-id":["S0925231226022058"],"URL":"https:\/\/doi.org\/10.1016\/j.neucom.2026.134807","relation":{},"ISSN":["0925-2312"],"issn-type":[{"value":"0925-2312","type":"print"}],"subject":[],"published":{"date-parts":[[2026,12]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"CSFMIL: Whole slide image classification with two-stage cross-scale fusion","name":"articletitle","label":"Article Title"},{"value":"Neurocomputing","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.neucom.2026.134807","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":"134807"}}