{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,13]],"date-time":"2026-07-13T11:40:05Z","timestamp":1783942805498,"version":"3.55.0"},"reference-count":49,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2025,12,15]],"date-time":"2025-12-15T00:00:00Z","timestamp":1765756800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2026,1,13]],"date-time":"2026-01-13T00:00:00Z","timestamp":1768262400000},"content-version":"vor","delay-in-days":29,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"funder":[{"name":"Joint Fund Program of the Science and Technology Department of Liaoning Province","award":["2023-BSBA-365"],"award-info":[{"award-number":["2023-BSBA-365"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["npj Digit. Med."],"DOI":"10.1038\/s41746-025-02214-9","type":"journal-article","created":{"date-parts":[[2025,12,15]],"date-time":"2025-12-15T06:16:34Z","timestamp":1765779394000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Random features meet MIL: a deep GP approach to colorectal MSI prediction"],"prefix":"10.1038","volume":"9","author":[{"given":"Shixuan","family":"Shen","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zeyang","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tianmu","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kangle","family":"Ma","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhen","family":"Tian","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fuqiang","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qingyue","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,12,15]]},"reference":[{"key":"2214_CR1","doi-asserted-by":"publisher","first-page":"330","DOI":"10.1038\/nature11252","volume":"487","author":"The Cancer Genome Atlas Network.","year":"2012","unstructured":"The Cancer Genome Atlas Network. Comprehensive molecular characterization of human colon and rectal cancer. Nature 487, 330\u2013337 (2012).","journal-title":"Nature"},{"key":"2214_CR2","doi-asserted-by":"publisher","first-page":"1350","DOI":"10.1038\/nm.3967","volume":"21","author":"J Guinney","year":"2015","unstructured":"Guinney, J. et al. The consensus molecular subtypes of colorectal cancer. Nat. Med. 21, 1350\u20131356 (2015).","journal-title":"Nat. Med."},{"key":"2214_CR3","doi-asserted-by":"publisher","first-page":"599","DOI":"10.1038\/modpathol.2016.198","volume":"30","author":"J Shia","year":"2017","unstructured":"Shia, J. et al. Morphological characterization of colorectal cancers in the Cancer Genome Atlas reveals distinct morphology\u2013molecular associations. Mod. Pathol. 30, 599\u2013609 (2017).","journal-title":"Mod. Pathol."},{"key":"2214_CR4","doi-asserted-by":"publisher","first-page":"1315","DOI":"10.1038\/modpathol.2012.94","volume":"25","author":"B Mitrovic","year":"2012","unstructured":"Mitrovic, B., Schaeffer, J. F., Riddell, R. H. & Kirsch, R. P. Tumor budding in colorectal carcinoma: time to take notice. Mod. Pathol. 25, 1315\u20131325 (2012).","journal-title":"Mod. Pathol."},{"key":"2214_CR5","doi-asserted-by":"publisher","first-page":"2417","DOI":"10.1002\/1097-0142(20010615)91:12<2417::AID-CNCR1276>3.0.CO;2-U","volume":"91","author":"TC Smyrk","year":"2001","unstructured":"Smyrk, T. C., Watson, P., Kaul, K. & Lynch, H. T. Tumor-infiltrating lymphocytes are a marker for microsatellite instability in colorectal carcinoma. Cancer 91, 2417\u20132422 (2001).","journal-title":"Cancer"},{"key":"2214_CR6","doi-asserted-by":"publisher","first-page":"60","DOI":"10.1016\/j.media.2017.07.005","volume":"42","author":"G Litjens","year":"2017","unstructured":"Litjens, G. et al. A survey on deep learning in medical image analysis. Med. Image Anal. 42, 60\u201388 (2017).","journal-title":"Med. Image Anal."},{"key":"2214_CR7","doi-asserted-by":"publisher","first-page":"775","DOI":"10.1038\/s41591-021-01343-4","volume":"27","author":"J van der Laak","year":"2021","unstructured":"van der Laak, J., Litjens, G. & Ciompi, F. Deep learning in histopathology: the path to the clinic. Nat. Med. 27, 775\u2013784 (2021).","journal-title":"Nat. Med."},{"key":"2214_CR8","doi-asserted-by":"publisher","first-page":"29","DOI":"10.4103\/2153-3539.186902","volume":"7","author":"A Janowczyk","year":"2016","unstructured":"Janowczyk, A. & Madabhushi, A. Deep learning for digital pathology image analysis: a comprehensive tutorial with selected use cases. J. Pathol. Inform. 7, 29 (2016).","journal-title":"J. Pathol. Inform."},{"key":"2214_CR9","doi-asserted-by":"publisher","first-page":"2199","DOI":"10.1001\/jama.2017.14585","volume":"318","author":"B Ehteshami Bejnordi","year":"2017","unstructured":"Ehteshami Bejnordi, B. et al. Diagnostic assessment of deep learning algorithms for detection of lymph node metastases in women with breast cancer. JAMA 318, 2199\u20132210 (2017).","journal-title":"JAMA"},{"key":"2214_CR10","doi-asserted-by":"publisher","first-page":"1559","DOI":"10.1038\/s41591-018-0177-5","volume":"24","author":"N Coudray","year":"2018","unstructured":"Coudray, N. et al. Classification and mutation prediction from non-small cell lung cancer histopathology images using deep learning. Nat. Med. 24, 1559\u20131567 (2018).","journal-title":"Nat. Med."},{"key":"2214_CR11","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":"2214_CR12","doi-asserted-by":"publisher","first-page":"44","DOI":"10.1093\/nsr\/nwx106","volume":"5","author":"Z-H Zhou","year":"2017","unstructured":"Zhou, Z.-H. A brief introduction to weakly supervised learning. Natl Sci. Rev. 5, 44\u201353 (2017).","journal-title":"Natl Sci. Rev."},{"key":"2214_CR13","doi-asserted-by":"publisher","first-page":"81","DOI":"10.1016\/j.artint.2013.06.003","volume":"201","author":"J Amores","year":"2013","unstructured":"Amores, J. Multiple instance classification: review, taxonomy and comparative study. Artif. Intell. 201, 81\u2013105 (2013).","journal-title":"Artif. Intell."},{"key":"2214_CR14","doi-asserted-by":"publisher","first-page":"329","DOI":"10.1016\/j.patcog.2017.10.009","volume":"77","author":"M-A Carbonneau","year":"2018","unstructured":"Carbonneau, M.-A., Cheplygina, V., Granger, E. & Gagnon, G. Multiple instance learning: a survey of problem characteristics and applications. Pattern Recognit. 77, 329\u2013353 (2018).","journal-title":"Pattern Recognit."},{"key":"2214_CR15","first-page":"102203","volume":"74","author":"NG Laleh","year":"2022","unstructured":"Laleh, N. G. et al. Benchmarking weakly-supervised deep learning pipelines for whole slide classification in multiple large patient cohorts. Med. Image Anal. 74, 102203 (2022).","journal-title":"Med. Image Anal."},{"key":"2214_CR16","doi-asserted-by":"publisher","first-page":"555","DOI":"10.1038\/s41551-020-00682-w","volume":"5","author":"MY Lu","year":"2021","unstructured":"Lu, M. Y. et al. Data-efficient and weakly supervised computational pathology on whole-slide images. Nat. Biomed. Eng. 5, 555\u2013570 (2021).","journal-title":"Nat. Biomed. Eng."},{"key":"2214_CR17","unstructured":"Shao, Z. et al. Transmil: Transformer based correlated multiple instance learning for whole slide image classification. In Advances in Neural Information Processing Systems, Vol. 34, 2136\u20132147 (2021)."},{"key":"2214_CR18","doi-asserted-by":"publisher","first-page":"200","DOI":"10.1016\/j.ejca.2021.07.012","volume":"155","author":"S Kuntz","year":"2021","unstructured":"Kuntz, S. et al. Gastrointestinal cancer classification and prognostication from histology using deep learning: systematic review. Eur. J. Cancer 155, 200\u2013215 (2021).","journal-title":"Eur. J. Cancer"},{"key":"2214_CR19","doi-asserted-by":"crossref","unstructured":"Afonso, M. et al. Multiple instance learning for WSI: a comparative analysis of attention-based architectures. Front. Med. 15, 100403 (2024).","DOI":"10.1016\/j.jpi.2024.100403"},{"key":"2214_CR20","doi-asserted-by":"crossref","unstructured":"Rasmussen, C. E. & Williams, C. K. I. Gaussian Processes for Machine Learning (MIT Press, 2006).","DOI":"10.7551\/mitpress\/3206.001.0001"},{"key":"2214_CR21","first-page":"1","volume":"16","author":"J Hensman","year":"2015","unstructured":"Hensman, J., Matthews, A. Gd. G. & Ghahramani, Z. Scalable variational Gaussian process classification. J. Mach. Learn. Res. 16, 1\u201335 (2015).","journal-title":"J. Mach. Learn. Res."},{"key":"2214_CR22","first-page":"1865","volume":"11","author":"M L\u00e1zaro-Gredilla","year":"2010","unstructured":"L\u00e1zaro-Gredilla, M., Qui\u00f1onero-Candela, J., Rasmussen, C. E. & Figueiras-Vidal, A. R. Sparse spectrum Gaussian process regression. J. Mach. Learn. Res. 11, 1865\u20131881 (2010).","journal-title":"J. Mach. Learn. Res."},{"key":"2214_CR23","first-page":"1","volume":"18","author":"TD Bui","year":"2017","unstructured":"Bui, T. D., Yan, J. & Turner, R. E. A unifying framework for Gaussian process pseudo-point approximation. J. Mach. Learn. Res. 18, 1\u201372 (2017).","journal-title":"J. Mach. Learn. Res."},{"key":"2214_CR24","doi-asserted-by":"publisher","DOI":"10.1038\/srep27988","volume":"6","author":"JN Kather","year":"2016","unstructured":"Kather, J. N. et al. Multi-class tissue classification of colorectal cancer histology images. Sci. Rep. 6, 27988 (2016).","journal-title":"Sci. Rep."},{"key":"2214_CR25","doi-asserted-by":"publisher","first-page":"btad114","DOI":"10.1093\/bioinformatics\/btad114","volume":"39","author":"Z Wang","year":"2023","unstructured":"Wang, Z. et al. Multiplex-detection-based multiple instance learning for whole-slide images. Bioinformatics 39, btad114 (2023).","journal-title":"Bioinformatics"},{"key":"2214_CR26","unstructured":"Qu, L., Ma, Y., Luo, X., Wang, M. & Song, Z. A good instance classifier is all you need: rethinking MIL for whole slide image classification. In IEEE Transactions on Circuits and Systems for Video Technology (2024)."},{"key":"2214_CR27","doi-asserted-by":"publisher","first-page":"e763","DOI":"10.1016\/S2589-7500(21)00180-1","volume":"3","author":"M Bilal","year":"2021","unstructured":"Bilal, M. et al. Predicting molecular pathways and key mutations in colorectal cancer from routine histology using weakly supervised deep learning. Lancet Digit. Health 3, e763\u2013e772 (2021).","journal-title":"Lancet Digit. Health"},{"key":"2214_CR28","doi-asserted-by":"publisher","first-page":"100980","DOI":"10.1016\/j.xcrm.2023.100980","volume":"4","author":"JM Niehues","year":"2023","unstructured":"Niehues, J. M. et al. Generalizable biomarker prediction from cancer pathology slides with self-supervised deep learning: a retrospective multi-centric study. Cell Rep. Med. 4, 100980 (2023).","journal-title":"Cell Rep. Med."},{"key":"2214_CR29","doi-asserted-by":"publisher","first-page":"1406\u20131416.e11","DOI":"10.1053\/j.gastro.2020.06.021","volume":"159","author":"A Echle","year":"2020","unstructured":"Echle, A. et al. Clinical-grade detection of microsatellite instability in colorectal tumors by deep learning. Gastroenterology 159, 1406\u20131416.e11 (2020).","journal-title":"Gastroenterology"},{"key":"2214_CR30","doi-asserted-by":"publisher","first-page":"1054","DOI":"10.1038\/s41591-019-0462-y","volume":"25","author":"JN Kather","year":"2019","unstructured":"Kather, J. N. et al. Deep learning can predict microsatellite instability directly from histology in gastrointestinal cancer. Nat. Med. 25, 1054\u20131056 (2019).","journal-title":"Nat. Med."},{"key":"2214_CR31","doi-asserted-by":"publisher","first-page":"34","DOI":"10.1016\/j.csbj.2018.01.001","volume":"16","author":"D Komura","year":"2018","unstructured":"Komura, D. & Ishikawa, S. Machine learning methods for histopathological image analysis. Comput. Struct. Biotechnol. J. 16, 34\u201342 (2018).","journal-title":"Comput. Struct. Biotechnol. J."},{"key":"2214_CR32","first-page":"558","volume":"38","author":"D Tellez","year":"2019","unstructured":"Tellez, D. et al. Quantifying the effects of data augmentation and stain color normalization in convolutional neural networks for computational pathology. IEEE Trans. Med. Imaging 38, 558\u2013568 (2019).","journal-title":"IEEE Trans. Med. Imaging"},{"key":"2214_CR33","doi-asserted-by":"publisher","first-page":"e1002730","DOI":"10.1371\/journal.pmed.1002730","volume":"16","author":"JN Kather","year":"2019","unstructured":"Kather, J. N. et al. Predicting survival from colorectal cancer histology slides using deep learning: a retrospective multicenter study. PLOS Med. 16, e1002730 (2019).","journal-title":"PLOS Med."},{"key":"2214_CR34","first-page":"331","volume":"625","author":"H Xu","year":"2024","unstructured":"Xu, H. et al. A whole-slide foundation model for digital pathology from anatomic pathology archives. Nature 625, 331\u2013339 (2024).","journal-title":"Nature"},{"key":"2214_CR35","doi-asserted-by":"publisher","first-page":"1962","DOI":"10.1109\/TMI.2016.2529665","volume":"35","author":"A Vahadane","year":"2016","unstructured":"Vahadane, A. et al. Structure-preserving color normalization and sparse stain separation for histological images. IEEE Trans. Med. Imaging 35, 1962\u20131971 (2016).","journal-title":"IEEE Trans. Med. Imaging"},{"key":"2214_CR36","doi-asserted-by":"publisher","first-page":"1113","DOI":"10.1038\/ng.2764","volume":"45","author":"JN Weinstein","year":"2013","unstructured":"Weinstein, J. N. et al. The Cancer Genome Atlas Pan-Cancer analysis project. Nat. Genet. 45, 1113\u20131120 (2013).","journal-title":"Nat. Genet."},{"key":"2214_CR37","doi-asserted-by":"crossref","first-page":"15","DOI":"10.1016\/j.cell.2016.03.002","volume":"165","author":"RL Grossman","year":"2016","unstructured":"Grossman, R. L. et al. Toward a shared vision for cancer genomic data. Cell 165, 15\u201318 (2016).","journal-title":"Cell"},{"key":"2214_CR38","doi-asserted-by":"publisher","unstructured":"Singh, D. P. et al. A comprehensive study of enhanced computational approaches for breast cancer classification: comparative analysis with existing state of the art methods. Arch. Comput. Method Eng. https:\/\/doi.org\/10.1007\/s11831-025-10414-5 (2025).","DOI":"10.1007\/s11831-025-10414-5"},{"key":"2214_CR39","doi-asserted-by":"publisher","first-page":"108368","DOI":"10.1016\/j.compbiolchem.2025.108368","volume":"115","author":"DP Singh","year":"2025","unstructured":"Singh, D. P. et al. CICADA (UCX): a novel approach for automated breast cancer classification through aggressiveness delineation. Comput. Biol. Chem. 115, 108368 (2025).","journal-title":"Comput. Biol. Chem."},{"key":"2214_CR40","doi-asserted-by":"crossref","unstructured":"Singh, D. P. et al. A comprehensive review of various machine learning and deep learning models for anti-cancer drug response prediction: comparative analysis with existing state of the art methods. Arch. Comput. Method Eng. 32, 3733\u20133757 (2025).","DOI":"10.1007\/s11831-025-10255-2"},{"key":"2214_CR41","doi-asserted-by":"crossref","unstructured":"Banerjee, T. Towards automated and reliable lung cancer detection in histopathological images using DY-FSPAN: a feature-summarized pyramidal attention network for explainable AI. Comput. Biol. Chem. 118, 108500 (2025).","DOI":"10.1016\/j.compbiolchem.2025.108500"},{"key":"2214_CR42","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-025-12602-6","volume":"15","author":"T Banerjee","year":"2025","unstructured":"Banerjee, T. et al. A novel hybrid deep learning approach combining deep feature attention and statistical validation for enhanced thyroid ultrasound segmentation. Sci. Rep. 15, 27207 (2025).","journal-title":"Sci. Rep."},{"key":"2214_CR43","doi-asserted-by":"publisher","first-page":"108852","DOI":"10.1016\/j.bspc.2025.108852","volume":"113","author":"I Pacal","year":"2026","unstructured":"Pacal, I. & Banerjee, T. Towards accurate and interpretable brain tumor diagnosis: T-FSPANNet with tri-attribute and pyramidal attention-based feature fusion. Biomed. Signal Process. Control 113, 108852 (2026).","journal-title":"Biomed. Signal Process. Control"},{"key":"2214_CR44","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-025-11574-x","volume":"15","author":"T Banerjee","year":"2025","unstructured":"Banerjee, T. et al. Pyramidal attention-based T network for brain tumor classification: a comprehensive analysis of transfer learning approaches for clinically reliable AI hybrid approaches. Sci. Rep. 15, 28669 (2025).","journal-title":"Sci. Rep."},{"key":"2214_CR45","doi-asserted-by":"publisher","unstructured":"Narayan, Y. et al. A comparative evaluation of deep learning architectures for prostate cancer segmentation: introducing trionixnet with n-core multi-attention mechanism. Arch. Comput. Method Eng. 1\u201340 https:\/\/doi.org\/10.1007\/s11831-025-10411-8 (2025).","DOI":"10.1007\/s11831-025-10411-8"},{"key":"2214_CR46","doi-asserted-by":"publisher","first-page":"e70034","DOI":"10.1002\/jdn.70034","volume":"85","author":"T Banerjee","year":"2025","unstructured":"Banerjee, T. Electromagnetic interaction algorithm (EIA)-based feature selection with adaptive kernel attention network (AKAttNet) for autism spectrum disorder classification. Int. J. Dev. Neurosci. 85, e70034 (2025).","journal-title":"Int. J. Dev. Neurosci."},{"key":"2214_CR47","doi-asserted-by":"publisher","unstructured":"Singh, D. P. et al. A comprehensive study on deep learning models for the detection of diabetic retinopathy using pathological images. Arch. Comput. Method Eng. 1\u201330 https:\/\/doi.org\/10.1007\/s11831-025-10315-7 (2025).","DOI":"10.1007\/s11831-025-10315-7"},{"key":"2214_CR48","doi-asserted-by":"publisher","unstructured":"Banerjee, T., Singh, D. P. & Kour, P. Advances in deep neural, transformer learning, and kernel-based methods for diabetic retinopathy detection: a comprehensive review. Arch. Comput. Method Eng. 1\u201349 https:\/\/doi.org\/10.1007\/s11831-025-10376-8 (2025).","DOI":"10.1007\/s11831-025-10376-8"},{"key":"2214_CR49","doi-asserted-by":"publisher","unstructured":"Banerjee, T. Comparing bipartite convoluted and attention-driven methods for skin cancer detection: a review of explainable AI and transfer learning strategies. Arch. Comput. Method Eng. 1\u201325 https:\/\/doi.org\/10.1007\/s11831-025-10379-5 (2025).","DOI":"10.1007\/s11831-025-10379-5"}],"container-title":["npj Digital Medicine"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.nature.com\/articles\/s41746-025-02214-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41746-025-02214-9","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41746-025-02214-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,1,14]],"date-time":"2026-01-14T05:07:17Z","timestamp":1768367237000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.nature.com\/articles\/s41746-025-02214-9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,12,15]]},"references-count":49,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2026,12]]}},"alternative-id":["2214"],"URL":"https:\/\/doi.org\/10.1038\/s41746-025-02214-9","relation":{},"ISSN":["2398-6352"],"issn-type":[{"value":"2398-6352","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,12,15]]},"assertion":[{"value":"4 September 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"23 November 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"15 December 2025","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":"40"}}