{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,19]],"date-time":"2026-03-19T15:36:44Z","timestamp":1773934604998,"version":"3.50.1"},"reference-count":71,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2025,12,20]],"date-time":"2025-12-20T00:00:00Z","timestamp":1766188800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100015825","name":"Korea University Anam Hospital","doi-asserted-by":"publisher","award":["O2514391"],"award-info":[{"award-number":["O2514391"]}],"id":[{"id":"10.13039\/501100015825","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100002642","name":"Korea University","doi-asserted-by":"publisher","award":["K2319661"],"award-info":[{"award-number":["K2319661"]}],"id":[{"id":"10.13039\/501100002642","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100014188","name":"Ministry of Science and ICT, South Korea","doi-asserted-by":"publisher","award":["RS2024-00441029"],"award-info":[{"award-number":["RS2024-00441029"]}],"id":[{"id":"10.13039\/501100014188","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100014188","name":"Ministry of Science and ICT, South Korea","doi-asserted-by":"publisher","award":["RQT-25-090105"],"award-info":[{"award-number":["RQT-25-090105"]}],"id":[{"id":"10.13039\/501100014188","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100014188","name":"Ministry of Science and ICT, South Korea","doi-asserted-by":"publisher","award":["RS-2025-02215813"],"award-info":[{"award-number":["RS-2025-02215813"]}],"id":[{"id":"10.13039\/501100014188","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100014188","name":"Ministry of Science and ICT, South Korea","doi-asserted-by":"publisher","award":["RS-2022-NR070353"],"award-info":[{"award-number":["RS-2022-NR070353"]}],"id":[{"id":"10.13039\/501100014188","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100014188","name":"Ministry of Science and ICT, South Korea","doi-asserted-by":"publisher","award":["RS-2025-02634603"],"award-info":[{"award-number":["RS-2025-02634603"]}],"id":[{"id":"10.13039\/501100014188","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100002701","name":"Ministry of Education","doi-asserted-by":"publisher","award":["RS-2020-NR054068"],"award-info":[{"award-number":["RS-2020-NR054068"]}],"id":[{"id":"10.13039\/501100002701","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003625","name":"Ministry of Health and Welfare","doi-asserted-by":"publisher","award":["RS-2021-KH113146"],"award-info":[{"award-number":["RS-2021-KH113146"]}],"id":[{"id":"10.13039\/501100003625","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003725","name":"National Research Foundation of Korea","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100003725","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001321","name":"National Research Foundation","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100001321","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003710","name":"Korea Health Industry Development Institute","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100003710","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":[[2026,1]]},"DOI":"10.1016\/j.compbiomed.2025.111410","type":"journal-article","created":{"date-parts":[[2025,12,25]],"date-time":"2025-12-25T13:54:29Z","timestamp":1766670869000},"page":"111410","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"C","title":["Subtype classification of gastric spindle cell tumors in whole slide images"],"prefix":"10.1016","volume":"201","author":[{"given":"Hyeseong","family":"Lee","sequence":"first","affiliation":[]},{"given":"Yoo Jin","family":"Lee","sequence":"additional","affiliation":[]},{"given":"Eunsu","family":"Kim","sequence":"additional","affiliation":[]},{"given":"Jonghyun","family":"Lee","sequence":"additional","affiliation":[]},{"given":"Sangjeong","family":"Ahn","sequence":"additional","affiliation":[]},{"given":"Sung Hak","family":"Lee","sequence":"additional","affiliation":[]}],"member":"78","reference":[{"issue":"9","key":"10.1016\/j.compbiomed.2025.111410_bib1","doi-asserted-by":"crossref","first-page":"3950","DOI":"10.1109\/TCYB.2019.2935141","article-title":"Weakly supervised deep learning for whole slide lung cancer image analysis","volume":"50","author":"Wang","year":"2019","journal-title":"IEEE Trans. Cybern."},{"key":"10.1016\/j.compbiomed.2025.111410_bib2","doi-asserted-by":"crossref","first-page":"S39","DOI":"10.1016\/S0959-8049(02)80602-5","article-title":"Pathology and diagnostic criteria of gastrointestinal stromal tumors (GISTs): a review","volume":"38","author":"Miettinen","year":"2002","journal-title":"Eur. J. Cancer"},{"issue":"7","key":"10.1016\/j.compbiomed.2025.111410_bib3","doi-asserted-by":"crossref","first-page":"690","DOI":"10.1007\/s00535-002-1124-1","article-title":"Effect of a tyrosine kinase inhibitor STI571 in a patient with hepatic metastases from a duodenal gastrointestinal stromal tumor","volume":"38","author":"Sawaki","year":"2003","journal-title":"J. Gastroenterol."},{"issue":"7","key":"10.1016\/j.compbiomed.2025.111410_bib4","doi-asserted-by":"crossref","first-page":"899","DOI":"10.1016\/j.ajpath.2023.03.012","article-title":"A deep learning\u2013based system trained for gastrointestinal stromal tumor screening can identify multiple types of soft tissue tumors","volume":"193","author":"Meng","year":"2023","journal-title":"Am. J. Pathol."},{"key":"10.1016\/j.compbiomed.2025.111410_bib5","doi-asserted-by":"crossref","DOI":"10.1016\/j.media.2020.101859","article-title":"Reducing annotation effort in digital pathology: a Co-Representation learning framework for classification tasks","volume":"67","author":"Pati","year":"2021","journal-title":"Med. Image Anal."},{"key":"10.1016\/j.compbiomed.2025.111410_bib6","doi-asserted-by":"crossref","first-page":"60","DOI":"10.1016\/j.media.2017.07.005","article-title":"A survey on deep learning in medical image analysis","volume":"42","author":"Litjens","year":"2017","journal-title":"Med. Image Anal."},{"issue":"8033","key":"10.1016\/j.compbiomed.2025.111410_bib7","doi-asserted-by":"crossref","first-page":"466","DOI":"10.1038\/s41586-024-07618-3","article-title":"A multimodal generative AI copilot for human pathology","volume":"634","author":"Lu","year":"2024","journal-title":"Nature"},{"issue":"10","key":"10.1016\/j.compbiomed.2025.111410_bib8","doi-asserted-by":"crossref","first-page":"2924","DOI":"10.1038\/s41591-024-03141-0","article-title":"A foundation model for clinical-grade computational pathology and rare cancers detection","volume":"30","author":"Vorontsov","year":"2024","journal-title":"Nat. Med."},{"issue":"6","key":"10.1016\/j.compbiomed.2025.111410_bib9","doi-asserted-by":"crossref","first-page":"giy065","DOI":"10.1093\/gigascience\/giy065","article-title":"H&E-Stained sentinel lymph node sections of breast cancer patients: the CAMELYON dataset","volume":"7","author":"Litjens","year":"2018","journal-title":"GigaScience"},{"key":"10.1016\/j.compbiomed.2025.111410_bib10","doi-asserted-by":"crossref","first-page":"85760","DOI":"10.1109\/ACCESS.2023.3304242","article-title":"Deep learning-based multi-modal ensemble classification approach for human breast cancer prognosis","volume":"11","author":"Jadoon","year":"2023","journal-title":"IEEE Access"},{"issue":"1","key":"10.1016\/j.compbiomed.2025.111410_bib11","doi-asserted-by":"crossref","first-page":"137","DOI":"10.1186\/s13058-024-01895-6","article-title":"Deep learning applications in breast cancer histopathological imaging: diagnosis, treatment, and prognosis","volume":"26","author":"Jiang","year":"2024","journal-title":"Breast Cancer Res."},{"issue":"2","key":"10.1016\/j.compbiomed.2025.111410_bib12","doi-asserted-by":"crossref","first-page":"222","DOI":"10.1016\/S1470-2045(19)30738-7","article-title":"Artificial intelligence for diagnosis and grading of prostate cancer in biopsies: a population-based, diagnostic study","volume":"21","author":"Str\u00f6m","year":"2020","journal-title":"Lancet Oncol."},{"issue":"1","key":"10.1016\/j.compbiomed.2025.111410_bib13","doi-asserted-by":"crossref","first-page":"154","DOI":"10.1038\/s41591-021-01620-2","article-title":"Artificial intelligence for diagnosis and Gleason grading of prostate cancer: the PANDA challenge","volume":"28","author":"Bulten","year":"2022","journal-title":"Nat. Med."},{"issue":"1","key":"10.1016\/j.compbiomed.2025.111410_bib14","doi-asserted-by":"crossref","first-page":"133","DOI":"10.1111\/bju.16464","article-title":"External validation of an artificial intelligence model for Gleason grading of prostate cancer on prostatectomy specimens","volume":"135","author":"Schmidt","year":"2025","journal-title":"BJU Int."},{"issue":"1","key":"10.1016\/j.compbiomed.2025.111410_bib15","doi-asserted-by":"crossref","DOI":"10.1038\/s41598-019-46718-3","article-title":"Pan-Renal Cell Carcinoma classification and survival prediction from histopathology images using deep learning","volume":"9","author":"Tabibu","year":"2019","journal-title":"Sci. Rep."},{"issue":"1","key":"10.1016\/j.compbiomed.2025.111410_bib16","first-page":"47","article-title":"Artificial intelligence in pathomics and genomics of renal cell carcinoma","volume":"51","author":"Knudsen","year":"2024","journal-title":"Urologic Clinics"},{"key":"10.1016\/j.compbiomed.2025.111410_bib17","article-title":"Digital pathology and artificial intelligence in renal cell carcinoma focusing on feature extraction: a literature review","volume":"15","author":"Li","year":"2025","journal-title":"Front. Oncol."},{"key":"10.1016\/j.compbiomed.2025.111410_bib18","series-title":"Scaling Laws for Neural Language Models","author":"Kaplan","year":"2020"},{"key":"10.1016\/j.compbiomed.2025.111410_bib19","article-title":"Scaling laws for autoregressive generative modeling","author":"Henighan","year":"2020","journal-title":"arXiv preprint arXiv:2010.14701"},{"key":"10.1016\/j.compbiomed.2025.111410_bib20","first-page":"84839","article-title":"Visual autoregressive modeling: scalable image generation via next-scale prediction","volume":"37","author":"Tian","year":"2024","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"10.1016\/j.compbiomed.2025.111410_bib21","article-title":"Attention is all you need","volume":"30","author":"Vaswani","year":"2017","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"10.1016\/j.compbiomed.2025.111410_bib22","series-title":"International Conference on Learning Representations","article-title":"An image is worth 16x16 words: Transformers for image recognition at scale","author":"Dosovitskiy","year":"2020"},{"key":"10.1016\/j.compbiomed.2025.111410_bib23","series-title":"Proceedings of the IEEE\/CVF International Conference on Computer Vision","article-title":"Swin transformer: hierarchical vision transformer using shifted windows","author":"Liu","year":"2021"},{"key":"10.1016\/j.compbiomed.2025.111410_bib24","series-title":"International Conference on Machine Learning","article-title":"Learning transferable visual models from natural language supervision","author":"Radford","year":"2021"},{"key":"10.1016\/j.compbiomed.2025.111410_bib25","series-title":"International Conference on Machine Learning","article-title":"Zero-shot text-to-image generation","author":"Ramesh","year":"2021"},{"issue":"3","key":"10.1016\/j.compbiomed.2025.111410_bib26","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."},{"issue":"3","key":"10.1016\/j.compbiomed.2025.111410_bib27","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."},{"issue":"8015","key":"10.1016\/j.compbiomed.2025.111410_bib28","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.compbiomed.2025.111410_bib29","series-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition","article-title":"Masked autoencoders are scalable vision learners","author":"He","year":"2022"},{"key":"10.1016\/j.compbiomed.2025.111410_bib30","series-title":"International Conference on Learning Representations","article-title":"BEiT: BERT pre-training of image transformers","author":"Bao","year":"2022"},{"key":"10.1016\/j.compbiomed.2025.111410_bib31","series-title":"Proceedings of the IEEE\/CVF International Conference on Computer Vision","article-title":"Emerging properties in self-supervised vision transformers","author":"Caron","year":"2021"},{"key":"10.1016\/j.compbiomed.2025.111410_bib32","article-title":"DINOv2: learning robust visual features without supervision","author":"Oquab","year":"2024","journal-title":"Trans. Mach. Learn. Res."},{"key":"10.1016\/j.compbiomed.2025.111410_bib33","series-title":"Dinov3. Arxiv Preprint arXiv:2508.10104","author":"Sim\u00e9oni","year":"2025"},{"issue":"1","key":"10.1016\/j.compbiomed.2025.111410_bib34","doi-asserted-by":"crossref","first-page":"6609","DOI":"10.1038\/s41598-024-56701-2","article-title":"The development of a prediction model based on deep learning for prognosis prediction of gastrointestinal stromal tumor: a SEER-based study","volume":"14","author":"Zeng","year":"2024","journal-title":"Sci. Rep."},{"issue":"1","key":"10.1016\/j.compbiomed.2025.111410_bib35","doi-asserted-by":"crossref","first-page":"71","DOI":"10.1038\/s41698-023-00421-9","article-title":"Deep learning predicts patients outcome and mutations from digitized histology slides in gastrointestinal stromal tumor","volume":"7","author":"Fu","year":"2023","journal-title":"npj Precis. Oncol."},{"key":"10.1016\/j.compbiomed.2025.111410_bib36","doi-asserted-by":"crossref","first-page":"1115","DOI":"10.1016\/S0046-8177(96)90301-9","article-title":"Discordance among expert pathologists in diagnosis of melanocytic neoplasms","volume":"27","author":"Ackerman","year":"1996","journal-title":"Hum. Pathol."},{"issue":"1","key":"10.1016\/j.compbiomed.2025.111410_bib37","article-title":"A framework of rebalancing imbalanced healthcare data for rare events' classification: a case of look\u2010alike sound\u2010alike mix\u2010up incident detection","volume":"2018","author":"Zhao","year":"2018","journal-title":"Journal of healthcare engineering"},{"issue":"5","key":"10.1016\/j.compbiomed.2025.111410_bib38","doi-asserted-by":"crossref","first-page":"509","DOI":"10.1016\/j.compbiomed.2010.03.005","article-title":"A learning method for the class imbalance problem with medical data sets","volume":"40","author":"Li","year":"2010","journal-title":"Comput. Biol. Med."},{"issue":"1","key":"10.1016\/j.compbiomed.2025.111410_bib39","doi-asserted-by":"crossref","first-page":"109","DOI":"10.1186\/s13000-023-01375-z","article-title":"Artificial intelligence in diagnostic pathology","volume":"18","author":"Shafi","year":"2023","journal-title":"Diagn. Pathol."},{"issue":"9","key":"10.1016\/j.compbiomed.2025.111410_bib40","doi-asserted-by":"crossref","first-page":"1525","DOI":"10.1093\/jamia\/ocac093","article-title":"The harm of class imbalance corrections for risk prediction models: illustration and simulation using logistic regression","volume":"29","author":"Van den Goorbergh","year":"2022","journal-title":"J. Am. Med. Inf. Assoc."},{"key":"10.1016\/j.compbiomed.2025.111410_bib41","series-title":"International Conference on Medical Image Computing and Computer-Assisted Intervention","article-title":"Fediic: towards robust federated learning for class-imbalanced medical image classification","author":"Wu","year":"2023"},{"key":"10.1016\/j.compbiomed.2025.111410_bib42","series-title":"International Conference on Machine Learning","article-title":"On calibration of modern neural networks","author":"Guo","year":"2017"},{"issue":"3\u20134","key":"10.1016\/j.compbiomed.2025.111410_bib43","article-title":"The harms of class imbalance corrections for machine learning based prediction models: a simulation study","volume":"44","author":"Carriero","year":"2025","journal-title":"Stat. Med."},{"key":"10.1016\/j.compbiomed.2025.111410_bib44","series-title":"International Conference on Machine Learning","article-title":"Attention-based deep multiple instance learning","author":"Ilse","year":"2018"},{"issue":"6","key":"10.1016\/j.compbiomed.2025.111410_bib45","doi-asserted-by":"crossref","first-page":"555","DOI":"10.1038\/s41551-020-00682-w","article-title":"Data-efficient and weakly supervised computational pathology on whole-slide images","volume":"5","author":"Lu","year":"2021","journal-title":"Nat. Biomed. Eng."},{"key":"10.1016\/j.compbiomed.2025.111410_bib46","series-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition","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.compbiomed.2025.111410_bib47","first-page":"2136","article-title":"Transmil: transformer based correlated multiple instance learning for whole slide image classification","volume":"34","author":"Shao","year":"2021","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"10.1016\/j.compbiomed.2025.111410_bib48","doi-asserted-by":"crossref","first-page":"20689","DOI":"10.52202\/068431-1504","article-title":"Additive mil: intrinsically interpretable multiple instance learning for pathology","volume":"35","author":"Javed","year":"2022","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"10.1016\/j.compbiomed.2025.111410_bib49","first-page":"8300","article-title":"XMIL: insightful explanations for multiple instance learning in histopathology","volume":"37","author":"Hense","year":"2024","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"10.1016\/j.compbiomed.2025.111410_bib50","series-title":"Proceedings of the IEEE\/CVF International Conference on Computer Vision","article-title":"Cutmix: regularization strategy to train strong classifiers with localizable features","author":"Yun","year":"2019"},{"key":"10.1016\/j.compbiomed.2025.111410_bib51","series-title":"Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition","article-title":"Deep residual learning for image recognition","author":"He","year":"2016"},{"key":"10.1016\/j.compbiomed.2025.111410_bib52","series-title":"Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition","article-title":"Squeeze-and-excitation networks","author":"Hu","year":"2018"},{"key":"10.1016\/j.compbiomed.2025.111410_bib53","doi-asserted-by":"crossref","first-page":"861","DOI":"10.21105\/joss.00861","article-title":"UMAP: uniform manifold approximation and projection for dimension reduction","volume":"3.29","author":"McInnes","year":"2018","journal-title":"J. Open Source Softw."},{"key":"10.1016\/j.compbiomed.2025.111410_bib54","series-title":"International Conference on Learning Representations","article-title":"Mixup: beyond empirical risk minimization","author":"Zhang","year":"2017"},{"issue":"1","key":"10.1016\/j.compbiomed.2025.111410_bib55","doi-asserted-by":"crossref","DOI":"10.1038\/ncomms12474","article-title":"Predicting non-small cell lung cancer prognosis by fully automated microscopic pathology image features","volume":"7","author":"Yu","year":"2016","journal-title":"Nat. Commun."},{"issue":"10","key":"10.1016\/j.compbiomed.2025.111410_bib56","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."},{"issue":"1","key":"10.1016\/j.compbiomed.2025.111410_bib57","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s40537-019-0197-0","article-title":"A survey on image data augmentation for deep learning","volume":"6","author":"Shorten","year":"2019","journal-title":"Journal of big data"},{"issue":"8","key":"10.1016\/j.compbiomed.2025.111410_bib58","doi-asserted-by":"crossref","first-page":"139","DOI":"10.1007\/s10916-018-1003-9","article-title":"A survey of data mining and deep learning in bioinformatics","volume":"42","author":"Lan","year":"2018","journal-title":"J. Med. Syst."},{"issue":"1","key":"10.1016\/j.compbiomed.2025.111410_bib59","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s40537-019-0192-5","article-title":"Survey on deep learning with class imbalance","volume":"6","author":"Johnson","year":"2019","journal-title":"Journal of big data"},{"issue":"1","key":"10.1016\/j.compbiomed.2025.111410_bib60","first-page":"1929","article-title":"Dropout: a simple way to prevent neural networks from overfitting","volume":"15","author":"Srivastava","year":"2014","journal-title":"J. Mach. Learn. Res."},{"key":"10.1016\/j.compbiomed.2025.111410_bib61","series-title":"International Conference on Machine Learning","article-title":"Batch normalization: accelerating deep network training by reducing internal covariate shift","author":"Ioffe","year":"2015"},{"key":"10.1016\/j.compbiomed.2025.111410_bib62","series-title":"2018 IEEE Conference on Multimedia Information Processing and Retrieval (MIPR)","article-title":"Dynamic sampling in convolutional neural networks for imbalanced data classification","author":"Pouyanfar","year":"2018"},{"key":"10.1016\/j.compbiomed.2025.111410_bib63","series-title":"Fourteenth International Conference on Quality Control by Artificial Vision","article-title":"Data augmentation for intra-class imbalance with generative adversarial network","author":"Hase","year":"2019"},{"key":"10.1016\/j.compbiomed.2025.111410_bib64","doi-asserted-by":"crossref","first-page":"321","DOI":"10.1613\/jair.953","article-title":"SMOTE: synthetic minority over-sampling technique","volume":"16","author":"Chawla","year":"2002","journal-title":"J. Artif. Intell. Res."},{"key":"10.1016\/j.compbiomed.2025.111410_bib65","series-title":"Proceedings of the IEEE International Conference on Computer Vision","article-title":"Focal loss for dense object detection","author":"Lin","year":"2017"},{"key":"10.1016\/j.compbiomed.2025.111410_bib66","series-title":"Neural Information Processing Systems","article-title":"Generative adversarial nets","author":"Goodfellow","year":"2014"},{"key":"10.1016\/j.compbiomed.2025.111410_bib67","series-title":"2019 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR)","first-page":"4396","article-title":"A style-based generator Architecture for generative adversarial networks","author":"Karras","year":"2018"},{"key":"10.1016\/j.compbiomed.2025.111410_bib68","first-page":"6840","article-title":"Denoising diffusion probabilistic models","volume":"33","author":"Ho","year":"2020","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"10.1016\/j.compbiomed.2025.111410_bib69","series-title":"2022 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR)","first-page":"10674","article-title":"High-Resolution image synthesis with latent diffusion models","author":"Rombach","year":"2021"},{"key":"10.1016\/j.compbiomed.2025.111410_bib70","doi-asserted-by":"crossref","DOI":"10.1016\/j.inffus.2023.101997","article-title":"Stain normalization methods for histopathology image analysis: a comprehensive review and experimental comparison","volume":"102","author":"Hoque","year":"2024","journal-title":"Inf. Fusion"},{"key":"10.1016\/j.compbiomed.2025.111410_bib71","doi-asserted-by":"crossref","DOI":"10.1016\/j.media.2019.101544","article-title":"Quantifying the effects of data augmentation and stain color normalization in convolutional neural networks for computational pathology","volume":"58","author":"Tellez","year":"2019","journal-title":"Med. Image Anal."}],"container-title":["Computers in Biology and Medicine"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0010482525017640?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0010482525017640?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,3,19]],"date-time":"2026-03-19T13:23:38Z","timestamp":1773926618000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0010482525017640"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,1]]},"references-count":71,"alternative-id":["S0010482525017640"],"URL":"https:\/\/doi.org\/10.1016\/j.compbiomed.2025.111410","relation":{},"ISSN":["0010-4825"],"issn-type":[{"value":"0010-4825","type":"print"}],"subject":[],"published":{"date-parts":[[2026,1]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Subtype classification of gastric spindle cell tumors in whole slide images","name":"articletitle","label":"Article Title"},{"value":"Computers in Biology and Medicine","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.compbiomed.2025.111410","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2025 The Authors. Published by Elsevier Ltd.","name":"copyright","label":"Copyright"}],"article-number":"111410"}}