{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,4]],"date-time":"2026-05-04T07:25:36Z","timestamp":1777879536778,"version":"3.51.4"},"reference-count":80,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"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":[[2026,8]]},"DOI":"10.1016\/j.bspc.2026.110408","type":"journal-article","created":{"date-parts":[[2026,4,23]],"date-time":"2026-04-23T12:39:15Z","timestamp":1776947955000},"page":"110408","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"C","title":["DepViT-CAD: Deployable Vision Transformer-based cancer diagnosis in histopathology"],"prefix":"10.1016","volume":"121","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4039-8821","authenticated-orcid":false,"given":"Ashkan","family":"Shakarami","sequence":"first","affiliation":[]},{"given":"Lorenzo","family":"Nicol\u00e8","sequence":"additional","affiliation":[]},{"given":"Rocco","family":"Cappellesso","sequence":"additional","affiliation":[]},{"given":"Angelo Paolo","family":"Dei Tos","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3406-8719","authenticated-orcid":false,"given":"Stefano","family":"Ghidoni","sequence":"additional","affiliation":[]}],"member":"78","reference":[{"issue":"5","key":"10.1016\/j.bspc.2026.110408_b1","doi-asserted-by":"crossref","first-page":"403","DOI":"10.1038\/labinvest.3700551","article-title":"Why microscopy will remain a cornerstone of surgical pathology","volume":"87","author":"Rosai","year":"2007","journal-title":"Lab. Invest."},{"key":"10.1016\/j.bspc.2026.110408_b2","doi-asserted-by":"crossref","DOI":"10.1016\/j.critrevonc.2022.103776","article-title":"The evolving landscape of anatomic pathology","volume":"178","author":"Pisapia","year":"2022","journal-title":"Crit. Rev. Oncol. Hematol."},{"issue":"5","key":"10.1016\/j.bspc.2026.110408_b3","doi-asserted-by":"crossref","first-page":"e194337","DOI":"10.1001\/jamanetworkopen.2019.4337","article-title":"Trends in the US and Canadian pathologist workforces from 2007 to 2017","volume":"2","author":"Metter","year":"2019","journal-title":"JAMA Netw. Open"},{"key":"10.1016\/j.bspc.2026.110408_b4","doi-asserted-by":"crossref","first-page":"335","DOI":"10.1007\/s00428-020-02894-6","article-title":"Number of pathologists in Germany: Comparison with European countries, USA, and Canada","volume":"478","author":"M\u00e4rkl","year":"2021","journal-title":"Virchows Arch."},{"key":"10.1016\/j.bspc.2026.110408_b5","article-title":"Diagnostic digital pathology implementation: Learning from the digital health experience","volume":"7","author":"Betmouni","year":"2021","journal-title":"Digit. Health"},{"issue":"2","key":"10.1016\/j.bspc.2026.110408_b6","doi-asserted-by":"crossref","first-page":"152","DOI":"10.1038\/s41379-021-00929-0","article-title":"Integrating digital pathology into clinical practice","volume":"35","author":"Hanna","year":"2022","journal-title":"Mod. Pathol."},{"issue":"2","key":"10.1016\/j.bspc.2026.110408_b7","doi-asserted-by":"crossref","first-page":"86","DOI":"10.1016\/j.zemedi.2018.12.003","article-title":"A gentle introduction to deep learning in medical image processing","volume":"29","author":"Maier","year":"2019","journal-title":"Z. F\u00fcr Med. Phys."},{"issue":"4","key":"10.1016\/j.bspc.2026.110408_b8","doi-asserted-by":"crossref","first-page":"686","DOI":"10.1038\/s41416-020-01122-x","article-title":"Deep learning in cancer pathology: A new generation of clinical biomarkers","volume":"124","author":"Echle","year":"2021","journal-title":"Br. J. Cancer"},{"issue":"12","key":"10.1016\/j.bspc.2026.110408_b9","doi-asserted-by":"crossref","first-page":"930","DOI":"10.1038\/s44222-023-00096-8","article-title":"Artificial intelligence for digital and computational pathology","volume":"1","author":"Song","year":"2023","journal-title":"Nat. Rev. Bioeng."},{"issue":"1","key":"10.1016\/j.bspc.2026.110408_b10","doi-asserted-by":"crossref","first-page":"23","DOI":"10.1038\/s41379-021-00919-2","article-title":"Digital pathology and artificial intelligence in translational medicine and clinical practice","volume":"35","author":"Baxi","year":"2022","journal-title":"Mod. Pathol."},{"key":"10.1016\/j.bspc.2026.110408_b11","doi-asserted-by":"crossref","DOI":"10.1111\/his.15153","article-title":"Pros and cons of artificial intelligence implementation in diagnostic pathology","author":"van Diest","year":"2024","journal-title":"Histopathology"},{"key":"10.1016\/j.bspc.2026.110408_b12","series-title":"Transformative AI for Automating Histopathology Workflows","author":"Shakarami","year":"2025"},{"issue":"Supplement_1","key":"10.1016\/j.bspc.2026.110408_b13","doi-asserted-by":"crossref","first-page":"i443","DOI":"10.1093\/bioinformatics\/btab285","article-title":"Pathcnn: interpretable convolutional neural networks for survival prediction and pathway analysis applied to glioblastoma","volume":"37","author":"Oh","year":"2021","journal-title":"Bioinformatics"},{"key":"10.1016\/j.bspc.2026.110408_b14","series-title":"International Conference on Machine Learning","first-page":"6105","article-title":"Efficientnet: Rethinking model scaling for convolutional neural networks","author":"Tan","year":"2019"},{"key":"10.1016\/j.bspc.2026.110408_b15","doi-asserted-by":"crossref","DOI":"10.1016\/j.bspc.2023.104812","article-title":"TCNN: A transformer convolutional neural network for artifact classification in whole slide images","volume":"84","author":"Shakarami","year":"2023","journal-title":"Biomed. Signal Process. Control."},{"issue":"S1","key":"10.1016\/j.bspc.2026.110408_b16","first-page":"S397","article-title":"AI for advanced cancer diagnosis: a CAD system empowered by a novel vision transformer network for histopathology analysis","volume":"485","author":"Shakarami","year":"2024","journal-title":"Virchows Arch."},{"key":"10.1016\/j.bspc.2026.110408_b17","doi-asserted-by":"crossref","DOI":"10.1016\/j.ijleo.2020.164237","article-title":"A CAD system for diagnosing Alzheimer\u2019s disease using 2D slices and an improved AlexNet-SVM method","volume":"212","author":"Shakarami","year":"2020","journal-title":"Optik"},{"key":"10.1016\/j.bspc.2026.110408_b18","doi-asserted-by":"crossref","DOI":"10.1016\/j.ijleo.2021.167199","article-title":"Diagnosing COVID-19 disease using an efficient CAD system","volume":"241","author":"Shakarami","year":"2021","journal-title":"Optik"},{"key":"10.1016\/j.bspc.2026.110408_b19","doi-asserted-by":"crossref","DOI":"10.1016\/j.bspc.2021.102495","article-title":"A fast and yet efficient YOLOv3 for blood cell detection","volume":"66","author":"Shakarami","year":"2021","journal-title":"Biomed. Signal Process. Control."},{"issue":"6","key":"10.1016\/j.bspc.2026.110408_b20","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.bspc.2026.110408_b21","series-title":"An image is worth 16x16 words: Transformers for image recognition at scale","author":"Dosovitskiy","year":"2020"},{"key":"10.1016\/j.bspc.2026.110408_b22","series-title":"International Conference on Machine Learning","first-page":"10347","article-title":"Training data-efficient image transformers & distillation through attention","author":"Touvron","year":"2021"},{"key":"10.1016\/j.bspc.2026.110408_b23","doi-asserted-by":"crossref","unstructured":"Z. Liu, Y. Lin, Y. Cao, H. Hu, Y. Wei, Z. Zhang, S. Lin, B. Guo, Swin Transformer: Hierarchical vision transformer using shifted windows, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2021.","DOI":"10.1109\/ICCV48922.2021.00986"},{"key":"10.1016\/j.bspc.2026.110408_b24","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.bspc.2026.110408_b25","doi-asserted-by":"crossref","unstructured":"R.J. Chen, C. Chen, Y. Li, T.Y. Chen, A.D. Trister, R.G. Krishnan, F. Mahmood, Scaling vision transformers to gigapixel images via hierarchical self-supervised learning, in: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, 2022, pp. 16144\u201316155.","DOI":"10.1109\/CVPR52688.2022.01567"},{"key":"10.1016\/j.bspc.2026.110408_b26","doi-asserted-by":"crossref","DOI":"10.1016\/j.bspc.2022.104181","article-title":"Pathological image classification via embedded fusion mutual learning","volume":"79","author":"Li","year":"2023","journal-title":"Biomed. Signal Process. Control."},{"key":"10.1016\/j.bspc.2026.110408_b27","series-title":"A general-purpose self-supervised model for computational pathology","author":"Chen","year":"2023"},{"issue":"3","key":"10.1016\/j.bspc.2026.110408_b28","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":"Nature Med."},{"key":"10.1016\/j.bspc.2026.110408_b29","series-title":"A multimodal knowledge-enhanced whole-slide pathology foundation model","author":"Xu","year":"2024"},{"key":"10.1016\/j.bspc.2026.110408_b30","series-title":"International Conference on Machine Learning","first-page":"1597","article-title":"A simple framework for contrastive learning of visual representations","author":"Chen","year":"2020"},{"key":"10.1016\/j.bspc.2026.110408_b31","unstructured":"Kaiming He, Haoqi Fan, Yuxin Wu, Saining Xie, Ross Girshick, Momentum contrast for unsupervised visual representation learning, in: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, 2020, pp. 9729\u20139738."},{"issue":"4","key":"10.1016\/j.bspc.2026.110408_b32","doi-asserted-by":"crossref","first-page":"757","DOI":"10.1109\/TMI.2020.3021387","article-title":"Pathomic fusion: an integrated framework for fusing histopathology and genomic features for cancer diagnosis and prognosis","volume":"41","author":"Chen","year":"2022","journal-title":"IEEE Trans. Med. Imaging"},{"issue":"7861","key":"10.1016\/j.bspc.2026.110408_b33","doi-asserted-by":"crossref","first-page":"106","DOI":"10.1038\/s41586-021-03512-4","article-title":"AI-based pathology predicts origins for cancers of unknown primary","volume":"594","author":"Lu","year":"2021","journal-title":"Nature"},{"issue":"3","key":"10.1016\/j.bspc.2026.110408_b34","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":"Nature Med."},{"issue":"8","key":"10.1016\/j.bspc.2026.110408_b35","doi-asserted-by":"crossref","first-page":"865","DOI":"10.1016\/j.ccell.2022.07.004","article-title":"Pan-cancer integrative histology-genomic analysis via multimodal deep learning","volume":"40","author":"Chen","year":"2022","journal-title":"Cancer Cell"},{"key":"10.1016\/j.bspc.2026.110408_b36","doi-asserted-by":"crossref","DOI":"10.1016\/j.jpi.2023.100356","article-title":"SlideTiler: A dataset creator software for boosting deep learning on histological whole slide images","volume":"15","author":"Barcellona","year":"2024","journal-title":"J. Pathol. Informatics"},{"key":"10.1016\/j.bspc.2026.110408_b37","series-title":"Linformer: Self-attention with linear complexity","author":"Wang","year":"2020"},{"key":"10.1016\/j.bspc.2026.110408_b38","first-page":"1","article-title":"Learnable context in multiple instance learning for whole slide image classification and segmentation","volume":"1","author":"Huang","year":"2024","journal-title":"J. Imaging Informatics Med."},{"key":"10.1016\/j.bspc.2026.110408_b39","series-title":"Medical Image Computing and Computer Assisted Intervention\u2013MICCAI 2018: 21st International Conference, Granada, Spain, September 16-20, 2018, Proceedings, Part II","first-page":"254","article-title":"Multiple instance learning for heterogeneous images: Training a CNN for histopathology","volume":"vol. 11071","author":"Couture","year":"2018"},{"issue":"1","key":"10.1016\/j.bspc.2026.110408_b40","doi-asserted-by":"crossref","first-page":"55","DOI":"10.7326\/M14-0697","article-title":"Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): the TRIPOD statement","volume":"162","author":"Collins","year":"2015","journal-title":"Ann. Intern. Med."},{"issue":"21","key":"10.1016\/j.bspc.2026.110408_b41","doi-asserted-by":"crossref","first-page":"2714","DOI":"10.1002\/sim.8570","article-title":"Graphical calibration curves and the integrated calibration index (ICI) for survival models","volume":"39","author":"Austin","year":"2020","journal-title":"Stat. Med."},{"key":"10.1016\/j.bspc.2026.110408_b42","first-page":"14200","article-title":"Attention bottlenecks for multimodal fusion","volume":"vol. 34","author":"Nagrani","year":"2021"},{"issue":"4","key":"10.1016\/j.bspc.2026.110408_b43","doi-asserted-by":"crossref","first-page":"1231","DOI":"10.1002\/ima.22692","article-title":"A hybrid feature fusion strategy for early fusion and majority voting for late fusion towards melanocytic skin lesion detection","volume":"32","author":"Singh","year":"2022","journal-title":"Int. J. Imaging Syst. Technol."},{"issue":"9","key":"10.1016\/j.bspc.2026.110408_b44","doi-asserted-by":"crossref","first-page":"5521","DOI":"10.3390\/app13095521","article-title":"Comparing vision transformers and convolutional neural networks for image classification: A literature review","volume":"13","author":"Maur\u00edcio","year":"2023","journal-title":"Appl. Sci."},{"key":"10.1016\/j.bspc.2026.110408_b45","doi-asserted-by":"crossref","unstructured":"A. Howard, M. Sandler, G. Chu, L.C. Chen, B. Chen, M. Tan, H. Adam, Searching for mobilenetv3, in: Proceedings of the IEEE\/CVF International Conference on Computer Vision, 2019, pp. 1314\u20131324.","DOI":"10.1109\/ICCV.2019.00140"},{"key":"10.1016\/j.bspc.2026.110408_b46","doi-asserted-by":"crossref","unstructured":"K. He, X. Zhang, S. Ren, J. Sun, Deep residual learning for image recognition, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2016, pp. 770\u2013778.","DOI":"10.1109\/CVPR.2016.90"},{"key":"10.1016\/j.bspc.2026.110408_b47","unstructured":"Ming Y. Lu, Bowen Chen, et al., Visual Language Pretrained Multiple Instance Zero-Shot Transfer for Histopathology Images, in: CVPR, 2023."},{"key":"10.1016\/j.bspc.2026.110408_b48","series-title":"A knowledge-enhanced pathology vision-language foundation model for cancer diagnosis","author":"Zhou","year":"2024"},{"issue":"22","key":"10.1016\/j.bspc.2026.110408_b49","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.bspc.2026.110408_b50","first-page":"30","article-title":"Detection of lymph node metastases in breast cancer from whole-slide histopathology images using deep learning: participation in the CAMELYON17 challenge","volume":"56","author":"Bandi","year":"2019","journal-title":"Med Image Anal"},{"issue":"1","key":"10.1016\/j.bspc.2026.110408_b51","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pmed.1002730","article-title":"Predicting survival from colorectal cancer histology slides using deep learning: A retrospective multicenter study","volume":"16","author":"Kather","year":"2019","journal-title":"PLoS Med."},{"issue":"5","key":"10.1016\/j.bspc.2026.110408_b52","doi-asserted-by":"crossref","first-page":"775","DOI":"10.1038\/s41591-021-01343-4","article-title":"Deep learning in histopathology: the path to the clinic","volume":"27","author":"Van der Laak","year":"2021","journal-title":"Nature Med."},{"key":"10.1016\/j.bspc.2026.110408_b53","series-title":"Theory and Practice of Histological Techniques","author":"Bancroft","year":"2008"},{"key":"10.1016\/j.bspc.2026.110408_b54","article-title":"A high-performance system for robust stain normalization of whole-slide images in histopathology","volume":"193","author":"Anghel","year":"2019","journal-title":"Front. Med."},{"key":"10.1016\/j.bspc.2026.110408_b55","doi-asserted-by":"crossref","first-page":"58821","DOI":"10.1109\/ACCESS.2022.3176091","article-title":"The devil is in the details: Whole slide image acquisition and processing for artifacts detection, color variation, and data augmentation: A review","volume":"10","author":"Kanwal","year":"2022","journal-title":"IEEE Access"},{"issue":"1","key":"10.1016\/j.bspc.2026.110408_b56","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":"J. Big Data"},{"issue":"1","key":"10.1016\/j.bspc.2026.110408_b57","article-title":"Developing image analysis pipelines of whole-slide images: Pre-and post-processing","volume":"5","author":"Smith","year":"2021","journal-title":"J. Clin. Transl. Sci."},{"issue":"1","key":"10.1016\/j.bspc.2026.110408_b58","doi-asserted-by":"crossref","first-page":"15","DOI":"10.1002\/path.5797","article-title":"The utility of color normalization for AI-based diagnosis of hematoxylin and eosin-stained pathology images","volume":"256","author":"Boschman","year":"2022","journal-title":"J. Pathol."},{"key":"10.1016\/j.bspc.2026.110408_b59","article-title":"A closer look at domain shift for deep learning in histopathology","volume":"118","author":"Stacke","year":"2021","journal-title":"Pattern Recognit."},{"issue":"5","key":"10.1016\/j.bspc.2026.110408_b60","first-page":"1234","article-title":"Stain-invariant feature learning with vision transformers for robust histopathology image analysis","volume":"42","author":"Zheng","year":"2023","journal-title":"IEEE Trans. Med. Imaging"},{"key":"10.1016\/j.bspc.2026.110408_b61","series-title":"Stain-invariant self-supervised learning for histopathology image analysis","author":"Tiard","year":"2022"},{"key":"10.1016\/j.bspc.2026.110408_b62","article-title":"Self-supervised contrastive learning for digital histopathology","volume":"7","author":"Ciga","year":"2022","journal-title":"Mach. Learn. Appl."},{"key":"10.1016\/j.bspc.2026.110408_b63","doi-asserted-by":"crossref","first-page":"216","DOI":"10.1016\/j.patcog.2019.02.023","article-title":"The impact of class imbalance in classification performance metrics based on the binary confusion matrix","volume":"91","author":"Luque","year":"2019","journal-title":"Pattern Recognit."},{"key":"10.1016\/j.bspc.2026.110408_b64","doi-asserted-by":"crossref","unstructured":"M. Buda, A. Maki, H.R. Roth, A systematic study of the class imbalance problem in convolutional neural networks, in: Proceedings of the IEEE International Conference on Computer Vision, ICCV, 2018, pp. 981\u2013989.","DOI":"10.1016\/j.neunet.2018.07.011"},{"key":"10.1016\/j.bspc.2026.110408_b65","series-title":"Data Mining and Knowledge Discovery Handbook","first-page":"875","article-title":"Data mining for imbalanced datasets: An overview","author":"Chawla","year":"2010"},{"key":"10.1016\/j.bspc.2026.110408_b66","series-title":"An Introduction to Statistical Learning: with Applications in R","author":"James","year":"2013"},{"key":"10.1016\/j.bspc.2026.110408_b67","doi-asserted-by":"crossref","first-page":"34","DOI":"10.1016\/j.csbj.2018.01.001","article-title":"Machine learning methods for histopathological image analysis","volume":"16","author":"Komura","year":"2018","journal-title":"Comput. Struct. Biotechnol. J."},{"key":"10.1016\/j.bspc.2026.110408_b68","doi-asserted-by":"crossref","first-page":"147","DOI":"10.1109\/RBME.2009.2034865","article-title":"Histopathological image analysis: A review","volume":"2","author":"Gurcan","year":"2009","journal-title":"IEEE Rev. Biomed. Eng."},{"issue":"1","key":"10.1016\/j.bspc.2026.110408_b69","first-page":"52","article-title":"Automated histopathological image segmentation using deep learning: Challenges and opportunities","volume":"5","author":"Banerji","year":"2022","journal-title":"Npj Digit. Med."},{"issue":"4","key":"10.1016\/j.bspc.2026.110408_b70","doi-asserted-by":"crossref","first-page":"699","DOI":"10.3390\/diagnostics13040699","article-title":"Robustness fine-tuning deep learning model for cancers diagnosis based on histopathology image analysis","volume":"13","author":"El-Ghany","year":"2023","journal-title":"Diagnostics"},{"key":"10.1016\/j.bspc.2026.110408_b71","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":"Nature Med."},{"issue":"1","key":"10.1016\/j.bspc.2026.110408_b72","doi-asserted-by":"crossref","first-page":"27","DOI":"10.4103\/2153-3539.119005","article-title":"OpenSlide: A vendor-neutral software foundation for digital pathology","volume":"4","author":"Goode","year":"2013","journal-title":"J. Pathol. Informatics"},{"key":"10.1016\/j.bspc.2026.110408_b73","article-title":"GPU-accelerated histopathological image segmentation using deep learning","volume":"133","author":"Majurski","year":"2021","journal-title":"Comput. Biol. Med."},{"issue":"1","key":"10.1016\/j.bspc.2026.110408_b74","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/s41598-017-17204-5","article-title":"QuPath: Open source software for digital pathology image analysis","volume":"7","author":"Bankhead","year":"2017","journal-title":"Sci. Rep."},{"issue":"1","key":"10.1016\/j.bspc.2026.110408_b75","doi-asserted-by":"crossref","first-page":"3932","DOI":"10.1038\/s41598-024-54489-9","article-title":"Creating an atlas of normal tissue for pruning WSI patching through anomaly detection","volume":"14","author":"Nejat","year":"2024","journal-title":"Sci. Rep."},{"key":"10.1016\/j.bspc.2026.110408_b76","series-title":"Proceedings of the 2008 SIAM International Conference on Data Mining","first-page":"588","article-title":"On the dangers of cross-validation. An experimental evaluation","author":"Rao","year":"2008"},{"key":"10.1016\/j.bspc.2026.110408_b77","series-title":"Cross-validation","author":"Berrar","year":"2019"},{"issue":"3","key":"10.1016\/j.bspc.2026.110408_b78","doi-asserted-by":"crossref","first-page":"249","DOI":"10.1007\/s41664-018-0068-2","article-title":"On splitting training and validation set: a comparative study of cross-validation, bootstrap and systematic sampling for estimating the generalization performance of supervised learning","volume":"2","author":"Xu","year":"2018","journal-title":"J. Anal. Test."},{"issue":"5","key":"10.1016\/j.bspc.2026.110408_b79","doi-asserted-by":"crossref","first-page":"711","DOI":"10.1158\/2159-8290.CD-23-1199","article-title":"Artificial intelligence in oncology: Current landscape, challenges, and future directions","volume":"14","author":"Lotter","year":"2024","journal-title":"Cancer Discov."},{"issue":"546","key":"10.1016\/j.bspc.2026.110408_b80","doi-asserted-by":"crossref","first-page":"1434","DOI":"10.1080\/01621459.2023.2197686","article-title":"Cross-validation: what does it estimate and how well does it do it?","volume":"119","author":"Bates","year":"2024","journal-title":"J. Amer. Statist. Assoc."}],"container-title":["Biomedical Signal Processing and Control"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1746809426009626?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1746809426009626?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,4,30]],"date-time":"2026-04-30T23:46:40Z","timestamp":1777592800000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S1746809426009626"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,8]]},"references-count":80,"alternative-id":["S1746809426009626"],"URL":"https:\/\/doi.org\/10.1016\/j.bspc.2026.110408","relation":{},"ISSN":["1746-8094"],"issn-type":[{"value":"1746-8094","type":"print"}],"subject":[],"published":{"date-parts":[[2026,8]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"DepViT-CAD: Deployable Vision Transformer-based cancer diagnosis in histopathology","name":"articletitle","label":"Article Title"},{"value":"Biomedical Signal Processing and Control","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.bspc.2026.110408","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":"110408"}}