{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,16]],"date-time":"2026-07-16T14:54:16Z","timestamp":1784213656044,"version":"3.55.0"},"reference-count":40,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2026,1,13]],"date-time":"2026-01-13T00:00:00Z","timestamp":1768262400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2026,1,31]],"date-time":"2026-01-31T00:00:00Z","timestamp":1769817600000},"content-version":"vor","delay-in-days":18,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"funder":[{"name":"Joint Funds for the Innovation of Science and Technology, Fujian Province","award":["2023Y9299"],"award-info":[{"award-number":["2023Y9299"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["npj Digit. Med."],"DOI":"10.1038\/s41746-025-02287-6","type":"journal-article","created":{"date-parts":[[2026,1,13]],"date-time":"2026-01-13T06:25:13Z","timestamp":1768285513000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Geometric multi-instance learning for weakly supervised gastric cancer segmentation"],"prefix":"10.1038","volume":"9","author":[{"given":"Chenshen","family":"Huang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Haoyun","family":"Xia","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xi","family":"Xiao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hong","family":"Chen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yiqing","family":"Jiang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yahui","family":"Lyu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhizhan","family":"Ni","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tianyang","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ning","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qi","family":"Huang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,1,13]]},"reference":[{"key":"2287_CR1","first-page":"209","volume":"71","author":"H Sung","year":"2021","unstructured":"Sung, H. et al. Global cancer statistics 2020: Globocan estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J. Clin. 71, 209\u2013249 (2021).","journal-title":"CA Cancer J. Clin."},{"key":"2287_CR2","doi-asserted-by":"publisher","first-page":"170","DOI":"10.1016\/j.media.2016.06.037","volume":"33","author":"A Madabhushi","year":"2016","unstructured":"Madabhushi, A. & Lee, G. Image analysis and machine learning in digital pathology: challenges and opportunities. Med. Image Anal. 33, 170\u2013175 (2016).","journal-title":"Med. Image Anal."},{"key":"2287_CR3","doi-asserted-by":"publisher","first-page":"1301","DOI":"10.1038\/s41591-019-0508-1","volume":"25","author":"G Campanella","year":"2019","unstructured":"Campanella, G. et al. Clinical-grade computational pathology using weakly supervised deep learning on whole slide images. Nat. Med. 25, 1301\u20131309 (2019).","journal-title":"Nat. Med."},{"key":"2287_CR4","unstructured":"Ilse, M., Tomczak, J. M. & Welling, M. Attention-based deep multiple instance learning. In International Conference on Machine Learning, 2127\u20132136 (PMLR, 2018)."},{"key":"2287_CR5","unstructured":"Shao, Z. et al. Transmil: Transformer based correlated multiple instance learning for whole slide image classification. In Advances in Neural Information Processing Systems, 34, 2136\u20132147 (2021)."},{"key":"2287_CR6","doi-asserted-by":"publisher","first-page":"1565","DOI":"10.1007\/s11517-023-02799-x","volume":"61","author":"L Tan","year":"2023","unstructured":"Tan, L. et al. Colorectal cancer lymph node metastasis prediction with weakly supervised transformer-based multi-instance learning. Med. Biol. Eng. Comput. 61, 1565\u20131580 (2023).","journal-title":"Med. Biol. Eng. Comput."},{"key":"2287_CR7","doi-asserted-by":"publisher","first-page":"235013","DOI":"10.1088\/1361-6560\/ac3b32","volume":"66","author":"L Zhao","year":"2021","unstructured":"Zhao, L. et al. Lung cancer subtype classification using histopathological images based on weakly supervised multi-instance learning. Phys. Med. Biol. 66, 235013 (2021).","journal-title":"Phys. Med. Biol."},{"key":"2287_CR8","doi-asserted-by":"crossref","unstructured":"Zhang, J. et al. 2dmamba: Efficient state space model for image representation with applications on giga-pixel whole slide image classification. In Proc. Computer Vision and Pattern Recognition Conference, 3583\u20133592 (2025).","DOI":"10.1109\/CVPR52734.2025.00339"},{"key":"2287_CR9","doi-asserted-by":"publisher","DOI":"10.1038\/s41746-025-01471-y","volume":"8","author":"E Fountzilas","year":"2025","unstructured":"Fountzilas, E., Pearce, T., Baysal, M. A., Chakraborty, A. & Tsimberidou, A. M. Convergence of evolving artificial intelligence and machine learning techniques in precision oncology. NPJ Digit. Med. 8, 75 (2025).","journal-title":"NPJ Digit. Med."},{"key":"2287_CR10","doi-asserted-by":"publisher","DOI":"10.1038\/s41746-025-01524-2","volume":"8","author":"Y Ma","year":"2025","unstructured":"Ma, Y., Jamdade, S., Konduri, L. & Sailem, H. Ai in histopathology explorer for comprehensive analysis of the evolving ai landscape in histopathology. npj Digit. Med. 8, 156 (2025).","journal-title":"npj Digit. Med."},{"key":"2287_CR11","doi-asserted-by":"crossref","unstructured":"Gao, Z. et al. Accurate spatial quantification in computational pathology with multiple instance learning. MedRxiv 2024\u201304 (2024).","DOI":"10.1101\/2024.04.25.24306364"},{"key":"2287_CR12","doi-asserted-by":"crossref","unstructured":"Caron, M. et al. Emerging properties in self-supervised vision transformers. In Proc. IEEE\/CVF International Conference on Computer Vision, 9650\u20139660 (2021).","DOI":"10.1109\/ICCV48922.2021.00951"},{"key":"2287_CR13","doi-asserted-by":"publisher","first-page":"293","DOI":"10.1038\/s41596-024-01047-2","volume":"20","author":"OS El Nahhas","year":"2025","unstructured":"El Nahhas, O. S. et al. From whole-slide image to biomarker prediction: end-to-end weakly supervised deep learning in computational pathology. Nat. Protoc. 20, 293\u2013316 (2025).","journal-title":"Nat. Protoc."},{"key":"2287_CR14","doi-asserted-by":"publisher","first-page":"930","DOI":"10.1038\/s44222-023-00096-8","volume":"1","author":"AH Song","year":"2023","unstructured":"Song, A. H. et al. Artificial intelligence for digital and computational pathology. Nat. Rev. Bioeng. 1, 930\u2013949 (2023).","journal-title":"Nat. Rev. Bioeng."},{"key":"2287_CR15","doi-asserted-by":"publisher","first-page":"1744","DOI":"10.1038\/s41591-022-01905-0","volume":"28","author":"SJ Wagner","year":"2022","unstructured":"Wagner, S. J. et al. Make deep learning algorithms in computational pathology more reproducible and reusable. Nat. Med. 28, 1744\u20131746 (2022).","journal-title":"Nat. Med."},{"key":"2287_CR16","doi-asserted-by":"publisher","first-page":"102298","DOI":"10.1016\/j.media.2021.102298","volume":"76","author":"MY Lu","year":"2022","unstructured":"Lu, M. Y. et al. Federated learning for computational pathology on gigapixel whole slide images. Med. Image Anal. 76, 102298 (2022).","journal-title":"Med. Image Anal."},{"key":"2287_CR17","doi-asserted-by":"crossref","unstructured":"Xiao, X. et al. Visual instance-aware prompt tuning. In Proc. 33rd ACM International Conference on Multimedia. 2880\u20132889 (2025).","DOI":"10.1145\/3746027.3754858"},{"key":"2287_CR18","doi-asserted-by":"crossref","unstructured":"Wibawa, M. S., Lo, K.-W., Young, L. S. & Rajpoot, N. Multi-scale attention-based multiple instance learning for classification of multi-gigapixel histology images. In European Conference on Computer Vision, 635\u2013647 (Springer, 2022).","DOI":"10.1007\/978-3-031-25082-8_43"},{"key":"2287_CR19","doi-asserted-by":"publisher","first-page":"2888","DOI":"10.1109\/TMI.2024.3381994","volume":"43","author":"M Liu","year":"2024","unstructured":"Liu, M. et al. Exploiting geometric features via hierarchical graph pyramid transformer for cancer diagnosis using histopathological images. IEEE Trans. Med. Imaging 43, 2888\u20132900 (2024).","journal-title":"IEEE Trans. Med. Imaging"},{"key":"2287_CR20","doi-asserted-by":"publisher","first-page":"108461","DOI":"10.1016\/j.compbiomed.2024.108461","volume":"174","author":"Z Diao","year":"2024","unstructured":"Diao, Z. & Jiang, H. A multi-instance tumor subtype classification method for small pet datasets using RA-DL attention module guided deep feature extraction with radiomics features. Comput. Biol. Med. 174, 108461 (2024).","journal-title":"Comput. Biol. Med."},{"key":"2287_CR21","unstructured":"Zhang, Y., Xia, Z., Yin, G. & Liu, B. Cluster-level sparse multi-instance learning for whole-slide images. arXiv preprint arXiv:2509.11034 (2025)."},{"key":"2287_CR22","doi-asserted-by":"publisher","first-page":"061402","DOI":"10.1117\/1.JMI.12.6.061402","volume":"12","author":"JW Tan","year":"2025","unstructured":"Tan, J. W., Lee, K. & Jeong, W.-K. Hid-con: weakly supervised intrahepatic cholangiocarcinoma subtype classification of whole slide images using contrastive hidden class detection. J. Med. Imaging 12, 061402\u2013061402 (2025).","journal-title":"J. Med. Imaging"},{"key":"2287_CR23","doi-asserted-by":"crossref","unstructured":"Wei, F. et al. Weakly-supervised segmentation with ensemble explainable AI: A comprehensive evaluation on crack detection. Rev. Sci. Instrum. 96, 045106 (2025).","DOI":"10.1063\/5.0249805"},{"key":"2287_CR24","doi-asserted-by":"publisher","DOI":"10.1186\/s12880-025-01759-1","volume":"25","author":"X Huang","year":"2025","unstructured":"Huang, X. et al. 2.5 d deep learning radiomics and clinical data for predicting occult lymph node metastasis in lung adenocarcinoma. BMC Med. Imaging 25, 225 (2025).","journal-title":"BMC Med. Imaging"},{"key":"2287_CR25","doi-asserted-by":"crossref","unstructured":"Liang, M. et al. NSB-H2GAN: \u201cNegative Sample\u201d-Boosted Hierarchical Heterogeneous Graph Attention Network for Interpretable Classification of Whole-Slide Images. IEEE Trans. Image Process. 34, 4215\u20134229 (2025).","DOI":"10.1109\/TIP.2025.3583127"},{"key":"2287_CR26","first-page":"3474","volume":"41","author":"C Li","year":"2025","unstructured":"Li, C., Weng, X., Li, Y. & Zhang, T. Multimodal learning engagement assessment system: an innovative approach to optimizing learning engagement. Int. J. Hum.-Comput. Interact. 41, 3474\u20133490 (2025).","journal-title":"Int. J. Hum.-Comput. Interact."},{"key":"2287_CR27","doi-asserted-by":"crossref","unstructured":"Lu, M. Y. et al. Data-efficient and weakly supervised computational pathology on whole-slide images. Nat. Biomed. Eng. 5, 555\u2013570 (2021).","DOI":"10.1038\/s41551-020-00682-w"},{"key":"2287_CR28","doi-asserted-by":"crossref","unstructured":"Zhang, H. et al. Dtfd-mil: Double-tier feature distillation multiple instance learning for histopathology whole slide image classification. In Proc. IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 18802\u201318812 (2022).","DOI":"10.1109\/CVPR52688.2022.01824"},{"key":"2287_CR29","unstructured":"Xiao, X. et al. Describe anything in medical images. arXiv preprint arXiv:2505.05804 (2025)."},{"key":"2287_CR30","doi-asserted-by":"crossref","unstructured":"Xiao, X. et al. Hgtdp-dta: Hybrid graph-transformer with dynamic prompt for drug-target binding affinity prediction. In International Conference on Neural Information Processing, 340\u2013354 (Springer, 2024).","DOI":"10.1007\/978-981-96-6585-3_24"},{"key":"2287_CR31","doi-asserted-by":"crossref","unstructured":"Lin, T., Yu, Z., Hu, H., Xu, Y. & Chen, C.-W. Interventional bag multi-instance learning on whole-slide pathological images. In Proc. IEEE\/CVF Conference on Computer Vision and Pattern Recognition, 19830\u201319839 (2023).","DOI":"10.1109\/CVPR52729.2023.01899"},{"key":"2287_CR32","doi-asserted-by":"crossref","unstructured":"Chen, R. J. et al. Whole slide images are 2d point clouds: Context-aware survival prediction using patch-based graph convolutional networks. International Conference on Medical Image Computing and Computer-Assisted Intervention. 339\u2013349 (Cham: Springer International Publishing, 2021).","DOI":"10.1007\/978-3-030-87237-3_33"},{"key":"2287_CR33","doi-asserted-by":"crossref","unstructured":"Cancer Genome Atlas Research Network. Comprehensive molecular characterization of gastric adenocarcinoma. Nature 513, 202 (2014).","DOI":"10.1038\/nature13480"},{"key":"2287_CR34","doi-asserted-by":"publisher","first-page":"105207","DOI":"10.1016\/j.compbiomed.2021.105207","volume":"142","author":"W Hu","year":"2022","unstructured":"Hu, W. et al. Gashissdb: a new gastric histopathology image dataset for computer aided diagnosis of gastric cancer. Comput. Biol. Med. 142, 105207 (2022).","journal-title":"Comput. Biol. Med."},{"key":"2287_CR35","doi-asserted-by":"crossref","unstructured":"Tellez, D. et al. Quantifying the effects of data augmentation and stain color normalization in convolutional neural networks for computational pathology. Med. Image Anal. 58, 101544 (2019).","DOI":"10.1016\/j.media.2019.101544"},{"key":"2287_CR36","unstructured":"Loshchilov, I. & Hutter, F. Decoupled weight decay regularization. arXiv preprint arXiv:1711.05101 (2017)."},{"key":"2287_CR37","doi-asserted-by":"crossref","unstructured":"Li, B. et al. Dual-stream multiple instance learning network for whole slide image classification with self-supervised contrastive learning. In Proc. IEEE\/CVF Conference on Computer Vision and Pattern Recognition. 14318\u201314328 (2021).","DOI":"10.1109\/CVPR46437.2021.01409"},{"key":"2287_CR38","doi-asserted-by":"crossref","unstructured":"Zhou, B., Khosla, A., Lapedriza, A., Oliva, A. & Torra, A. Learning deep features for discriminative localization. In Proc. IEEE Conference on Computer Vision and Pattern Recognition, 2921\u20132929 (2016).","DOI":"10.1109\/CVPR.2016.319"},{"key":"2287_CR39","doi-asserted-by":"crossref","unstructured":"Chan, T. H., Cendra, F. J., Ma, L., Yin, G. & Yu, L. Histopathology whole slide image analysis with heterogeneous graph representation learning. In Proc. IEEE\/CVF Conference on Computer Vision and Pattern Recognition, 15661\u201315670 (2023).","DOI":"10.1109\/CVPR52729.2023.01503"},{"key":"2287_CR40","doi-asserted-by":"crossref","unstructured":"Macenko, M. et al. A method for normalizing histology slides for quantitative analysis. In 2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, 1107\u20131110 (IEEE, 2009).","DOI":"10.1109\/ISBI.2009.5193250"}],"container-title":["npj Digital Medicine"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.nature.com\/articles\/s41746-025-02287-6","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41746-025-02287-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41746-025-02287-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,1,31]],"date-time":"2026-01-31T04:15:46Z","timestamp":1769832946000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.nature.com\/articles\/s41746-025-02287-6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,1,13]]},"references-count":40,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2026,12]]}},"alternative-id":["2287"],"URL":"https:\/\/doi.org\/10.1038\/s41746-025-02287-6","relation":{},"ISSN":["2398-6352"],"issn-type":[{"value":"2398-6352","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,1,13]]},"assertion":[{"value":"30 September 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"16 December 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"13 January 2026","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"The authors declare no competing interests.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"101"}}