{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,15]],"date-time":"2026-09-15T16:18:16Z","timestamp":1789489096520,"version":"build-2803163510"},"reference-count":54,"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":[[2026,6,2]],"date-time":"2026-06-02T00:00:00Z","timestamp":1780358400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100000925","name":"National Health and Medical Research Council","doi-asserted-by":"publisher","award":["GNT202035"],"award-info":[{"award-number":["GNT202035"]}],"id":[{"id":"10.13039\/501100000925","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Expert Systems with Applications"],"published-print":{"date-parts":[[2026,12]]},"DOI":"10.1016\/j.eswa.2026.133079","type":"journal-article","created":{"date-parts":[[2026,6,3]],"date-time":"2026-06-03T08:13:49Z","timestamp":1780474429000},"page":"133079","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"PA","title":["Slide-aware deep feature prompting for enhanced whole slide image classification"],"prefix":"10.1016","volume":"331","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8192-6731","authenticated-orcid":false,"given":"Cong","family":"Cong","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yang","family":"Song","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Antonio","family":"Di Ieva","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1781-1067","authenticated-orcid":false,"given":"Qiangguo","family":"Jin","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lei","family":"Fan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Angela","family":"Chou","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Anthony","family":"J Gill","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2371-0713","authenticated-orcid":false,"given":"Sidong","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.eswa.2026.133079_bib0001","unstructured":"Bahng, H., Jahanian, A., Sankaranarayanan, S., & Isola, P. (2022). Exploring visual prompts for adapting large-scale models. arXiv: 2203.17274."},{"key":"10.1016\/j.eswa.2026.133079_bib0002","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.eswa.2026.133079_bib0003","first-page":"463","article-title":"Rademacher and Gaussian complexities: Risk bounds and structural results","volume":"3","author":"Bartlett","year":"2002","journal-title":"Journal of Machine Learning Research"},{"issue":"22","key":"10.1016\/j.eswa.2026.133079_bib0004","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.eswa.2026.133079_bib0005","doi-asserted-by":"crossref","DOI":"10.1016\/j.media.2021.102004","article-title":"Residual cyclegan for robust domain transformation of histopathological tissue slides","volume":"70","author":"de Bel","year":"2021","journal-title":"Medical Image Analysis"},{"key":"10.1016\/j.eswa.2026.133079_bib0006","doi-asserted-by":"crossref","DOI":"10.1093\/database\/baac093","article-title":"BRACS: A dataset for BReAst carcinoma subtyping in H&E histology images","volume":"2022","author":"Brancati","year":"2022","journal-title":"Database"},{"key":"10.1016\/j.eswa.2026.133079_bib0007","first-page":"11285","article-title":"TinyTL: Reduce memory, not parameters for efficient on-device learning","volume":"33","author":"Cai","year":"2020","journal-title":"Advances in Neural Information Processing Systems"},{"key":"10.1016\/j.eswa.2026.133079_bib0008","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"},{"issue":"3","key":"10.1016\/j.eswa.2026.133079_bib0009","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 Medicine"},{"key":"10.1016\/j.eswa.2026.133079_bib0010","doi-asserted-by":"crossref","DOI":"10.1038\/s41746-026-02710-6","article-title":"Flexible and scalable federated learning with deep feature prompts for digital pathology","author":"Cong","year":"2026","journal-title":"npj Digital Medicine"},{"key":"10.1016\/j.eswa.2026.133079_bib0011","series-title":"International conference on medical image computing and computer-assisted intervention","first-page":"420","article-title":"Feature re-calibration based multiple instance learning for whole slide image classification","author":"Chikontwe","year":"2022"},{"key":"10.1016\/j.eswa.2026.133079_bib0012","doi-asserted-by":"crossref","DOI":"10.1016\/j.media.2022.102580","article-title":"Colour adaptive generative networks for stain normalisation of histopathology images","volume":"82","author":"Cong","year":"2022","journal-title":"Medical Image Analysis"},{"key":"10.1016\/j.eswa.2026.133079_bib0013","series-title":"2009\u202fIEEE conference on computer vision and pattern recognition","first-page":"248","article-title":"ImageNet: A large-scale hierarchical image database","author":"Deng","year":"2009"},{"key":"10.1016\/j.eswa.2026.133079_bib0014","unstructured":"Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S. et al. (2020). An image is worth 16x16 words: Transformers for image recognition at scale. arXiv: 2010.11929."},{"key":"10.1016\/j.eswa.2026.133079_bib0015","doi-asserted-by":"crossref","first-page":"118","DOI":"10.58496\/MJAIH\/2024\/014","article-title":"An optimized framework for kidney disease detection and prediction using DL techniques","volume":"2024","author":"El-Kenawy","year":"2024","journal-title":"Mesopotamian Journal of Artificial Intelligence in Healthcare"},{"issue":"5","key":"10.1016\/j.eswa.2026.133079_bib0016","doi-asserted-by":"crossref","first-page":"1401","DOI":"10.1109\/TMI.2022.3228275","article-title":"Cancer survival prediction from whole slide images with self-supervised learning and slide consistency","volume":"42","author":"Fan","year":"2023","journal-title":"IEEE Transactions on Medical Imaging"},{"key":"10.1016\/j.eswa.2026.133079_bib0017","unstructured":"Filiot, A., Jacob, P., Mac Kain, A., & Saillard, C. (2024). Phikon-v2, a large and public feature extractor for biomarker prediction. arXiv: 2409.09173."},{"key":"10.1016\/j.eswa.2026.133079_bib0018","unstructured":"Geuchen, P., Heindl, T., St\u00f6ger, D., & Voigtlaender, F. (2023). On the Lipschitz constant of random neural networks. arXiv: 2311.01356."},{"key":"10.1016\/j.eswa.2026.133079_bib0019","series-title":"Proceedings of the AAAI conference on artificial intelligence","first-page":"3158","article-title":"Queryable prototype multiple instance learning with vision-language models for incremental whole slide image classification","volume":"vol. 39","author":"Gou","year":"2025"},{"key":"10.1016\/j.eswa.2026.133079_bib0020","doi-asserted-by":"crossref","DOI":"10.1016\/j.media.2022.102685","article-title":"One model is all you need: Multi-task learning enables simultaneous histology image segmentation and classification","volume":"83","author":"Graham","year":"2023","journal-title":"Medical Image Analysis"},{"key":"10.1016\/j.eswa.2026.133079_bib0021","series-title":"CVPR","first-page":"770","article-title":"Deep residual learning for image recognition","author":"He","year":"2016"},{"key":"10.1016\/j.eswa.2026.133079_bib0022","series-title":"International conference on machine learning","first-page":"2790","article-title":"Parameter-efficient transfer learning for NLP","author":"Houlsby","year":"2019"},{"key":"10.1016\/j.eswa.2026.133079_bib0023","series-title":"Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition","first-page":"10878","article-title":"Diversity-aware meta visual prompting","author":"Huang","year":"2023"},{"key":"10.1016\/j.eswa.2026.133079_bib0024","series-title":"International conference on medical image computing and computer-assisted intervention","first-page":"120","article-title":"Fine-grained prompt tuning: A parameter and memory efficient transfer learning method for high-resolution medical image classification","author":"Huang","year":"2024"},{"issue":"9","key":"10.1016\/j.eswa.2026.133079_bib0025","doi-asserted-by":"crossref","first-page":"2307","DOI":"10.1038\/s41591-023-02504-3","article-title":"A visual\u2013language foundation model for pathology image analysis using medical Twitter","volume":"29","author":"Huang","year":"2023","journal-title":"Nature Medicine"},{"key":"10.1016\/j.eswa.2026.133079_bib0026","series-title":"International conference on machine learning","first-page":"2127","article-title":"Attention-based deep multiple instance learning","author":"Ilse","year":"2018"},{"key":"10.1016\/j.eswa.2026.133079_bib0027","series-title":"European conference on computer vision","first-page":"709","article-title":"Visual prompt tuning","author":"Jia","year":"2022"},{"key":"10.1016\/j.eswa.2026.133079_bib0028","series-title":"ECAI 2024","first-page":"202","article-title":"Convolutional bypasses are better vision transformer adapters","author":"Jie","year":"2024"},{"key":"10.1016\/j.eswa.2026.133079_bib0029","doi-asserted-by":"crossref","first-page":"72","DOI":"10.58496\/BJAI\/2025\/007","article-title":"The rise of transformers\u2013redefining the landscape of artificial intelligence","volume":"2025","author":"Ladu","year":"2025","journal-title":"Babylonian Journal of Artificial Intelligence"},{"key":"10.1016\/j.eswa.2026.133079_bib0030","unstructured":"Lee, J., Tang, R., & Lin, J. (2019). What would elsa do? Freezing layers during transformer fine-tuning. arXiv: 1911.03090."},{"key":"10.1016\/j.eswa.2026.133079_bib0031","doi-asserted-by":"crossref","unstructured":"Lester, B., Al-Rfou, R., & Constant, N. (2021). The power of scale for parameter-efficient prompt tuning. arXiv: 2104.08691.","DOI":"10.18653\/v1\/2021.emnlp-main.243"},{"key":"10.1016\/j.eswa.2026.133079_bib0032","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.eswa.2026.133079_bib0033","doi-asserted-by":"crossref","unstructured":"Li, D., Wan, G., Wu, X., Wu, X., Nirmal, A. J., Lian, C. G., Sorger, P. K., Semenov, Y. R., & Zhao, C. (2025). A survey on computational pathology foundation models: Datasets, adaptation strategies, and evaluation tasks. arXiv: 2501.15724.","DOI":"10.1007\/s10115-026-02806-1"},{"key":"10.1016\/j.eswa.2026.133079_bib0034","unstructured":"Li, X. L., & Liang, P. (2021). Prefix-tuning: Optimizing continuous prompts for generation. arXiv: 2101.00190."},{"key":"10.1016\/j.eswa.2026.133079_bib0035","unstructured":"Lin, Y., Zhu, Z., Cheng, K.-T., & Chen, H. (2024). Prompt-guided adaptive model transformation for whole slide image classification. arXiv: 2403.12537."},{"issue":"5","key":"10.1016\/j.eswa.2026.133079_bib0036","doi-asserted-by":"crossref","first-page":"1841","DOI":"10.1109\/TMI.2024.3351213","article-title":"Pseudo-bag mixup augmentation for multiple instance learning-based whole slide image classification","volume":"43","author":"Liu","year":"2024","journal-title":"IEEE Transactions on Medical Imaging"},{"issue":"1","key":"10.1016\/j.eswa.2026.133079_bib0037","first-page":"1","article-title":"Isocitrate dehydrogenase (IDH) status prediction in histopathology images of gliomas using deep learning","volume":"10","author":"Liu","year":"2020","journal-title":"Scientific Reports"},{"key":"10.1016\/j.eswa.2026.133079_bib0038","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 Medicine"},{"issue":"6","key":"10.1016\/j.eswa.2026.133079_bib0039","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":"Nature Biomedical Engineering"},{"issue":"3","key":"10.1016\/j.eswa.2026.133079_bib0040","doi-asserted-by":"crossref","first-page":"545","DOI":"10.1038\/s41551-025-01488-4","article-title":"A generalizable pathology foundation model using a unified knowledge distillation pretraining framework","volume":"10","author":"Ma","year":"2026","journal-title":"Nature Biomedical Engineering"},{"issue":"Nov","key":"10.1016\/j.eswa.2026.133079_bib0041","first-page":"2579","article-title":"Visualizing data using t-SNE","volume":"9","author":"Van der Maaten","year":"2008","journal-title":"Journal of Machine Learning Research"},{"key":"10.1016\/j.eswa.2026.133079_bib0042","series-title":"International conference on medical image computing and computer-assisted intervention","first-page":"24","article-title":"DGMIL: Distribution guided multiple instance learning for whole slide image classification","author":"Qu","year":"2022"},{"key":"10.1016\/j.eswa.2026.133079_bib0043","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":"Advances in Neural Information Processing Systems"},{"key":"10.1016\/j.eswa.2026.133079_bib0044","first-page":"13","article-title":"Dimensionality reduction: A comparative review","volume":"10","author":"Van Der Maaten","year":"2009","journal-title":"Journal of Machine Learning Research"},{"key":"10.1016\/j.eswa.2026.133079_bib0045","unstructured":"Wang, J., Fan, L., Zhang, D., Jing, W., Di, D., Song, Y., Liu, S., & Cong, C. (2026). Visual prompt-agnostic evolution. arXiv: 2601.20232."},{"key":"10.1016\/j.eswa.2026.133079_bib0046","doi-asserted-by":"crossref","unstructured":"Xu, R., Luo, F., Zhang, Z., Tan, C., Chang, B., Huang, S., & Huang, F. (2021). Raise a child in large language model: Towards effective and generalizable fine-tuning. arXiv: 2109.05687.","DOI":"10.18653\/v1\/2021.emnlp-main.749"},{"key":"10.1016\/j.eswa.2026.133079_bib0047","doi-asserted-by":"crossref","DOI":"10.1016\/j.cmpb.2024.108161","article-title":"ProDiv: Prototype-driven consistent pseudo-bag division for whole-slide image classification","volume":"249","author":"Yang","year":"2024","journal-title":"Computer Methods and Programs in Biomedicine"},{"key":"10.1016\/j.eswa.2026.133079_bib0048","unstructured":"Zhang, D., Feng, T., Xue, L., Wang, Y., Dong, Y., & Tang, J. (2025). Parameter-efficient fine-tuning for foundation models. arXiv: 2501.13787."},{"key":"10.1016\/j.eswa.2026.133079_bib0049","unstructured":"Zhang, H., Cisse, M., Dauphin, Y. N., & Lopez-Paz, D. (2017). mixup: Beyond empirical risk minimization. arXiv: 1710.09412."},{"key":"10.1016\/j.eswa.2026.133079_bib0050","series-title":"International conference on medical image computing and computer-assisted intervention","first-page":"624","article-title":"Prompt-MIL: Boosting multi-instance learning schemes via task-specific prompt tuning","author":"Zhang","year":"2023"},{"key":"10.1016\/j.eswa.2026.133079_bib0051","series-title":"Medical image computing and computer assisted intervention","first-page":"192","article-title":"Gigapixel whole-slide images classification using locally supervised learning","author":"Zhang","year":"2022"},{"key":"10.1016\/j.eswa.2026.133079_bib0052","series-title":"Computer vision\u2013ECCV 2020: 16th European conference, Glasgow, UK, August 23\u201328, 2020, proceedings, Part III 16","first-page":"698","article-title":"Side-tuning: A baseline for network adaptation via additive side networks","author":"Zhang","year":"2020"},{"key":"10.1016\/j.eswa.2026.133079_bib0053","series-title":"European conference on computer vision","first-page":"125","article-title":"Attention-challenging multiple instance learning for whole slide image classification","author":"Zhang","year":"2024"},{"key":"10.1016\/j.eswa.2026.133079_bib0054","series-title":"Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition","first-page":"4837","article-title":"Predicting lymph node metastasis using histopathological images based on multiple instance learning with deep graph convolution","author":"Zhao","year":"2020"}],"container-title":["Expert Systems with Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0957417426019901?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0957417426019901?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,9,15]],"date-time":"2026-09-15T15:59:58Z","timestamp":1789487998000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0957417426019901"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,12]]},"references-count":54,"alternative-id":["S0957417426019901"],"URL":"https:\/\/doi.org\/10.1016\/j.eswa.2026.133079","relation":{},"ISSN":["0957-4174"],"issn-type":[{"value":"0957-4174","type":"print"}],"subject":[],"published":{"date-parts":[[2026,12]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Slide-aware deep feature prompting for enhanced whole slide image classification","name":"articletitle","label":"Article Title"},{"value":"Expert Systems with Applications","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.eswa.2026.133079","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 The Author(s). Published by Elsevier Ltd.","name":"copyright","label":"Copyright"}],"article-number":"133079"}}