{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,6]],"date-time":"2026-07-06T06:16:35Z","timestamp":1783318595542,"version":"3.54.6"},"reference-count":68,"publisher":"Springer Science and Business Media LLC","issue":"6","license":[{"start":{"date-parts":[[2026,4,24]],"date-time":"2026-04-24T00:00:00Z","timestamp":1776988800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,4,24]],"date-time":"2026-04-24T00:00:00Z","timestamp":1776988800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100002858","name":"China Postdoctoral Science Foundation","doi-asserted-by":"crossref","award":["2024M762126, GZC20231728"],"award-info":[{"award-number":["2024M762126, GZC20231728"]}],"id":[{"id":"10.13039\/501100002858","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["62576214, 62376161"],"award-info":[{"award-number":["62576214, 62376161"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["62576214, 62376161"],"award-info":[{"award-number":["62576214, 62376161"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100021171","name":"Guangdong Basic and Applied Basic Research Foundation","doi-asserted-by":"crossref","award":["2024B1515020109"],"award-info":[{"award-number":["2024B1515020109"]}],"id":[{"id":"10.13039\/501100021171","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Int. J. Mach. Learn. &amp; Cyber."],"published-print":{"date-parts":[[2026,6]]},"DOI":"10.1007\/s13042-026-03102-8","type":"journal-article","created":{"date-parts":[[2026,4,24]],"date-time":"2026-04-24T08:46:35Z","timestamp":1777020395000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Graph-based multi-instance learning: a survey"],"prefix":"10.1007","volume":"17","author":[{"given":"Guo","family":"Ye","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xinlei","family":"Zhou","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chao","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ran","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,4,24]]},"reference":[{"issue":"1\u20132","key":"3102_CR1","doi-asserted-by":"publisher","first-page":"31","DOI":"10.1016\/S0004-3702(96)00034-3","volume":"89","author":"TG Dietterich","year":"1997","unstructured":"Dietterich TG, Lathrop RH, Lozano-P\u00e9rez T (1997) Solving the multiple instance problem with axis-parallel rectangles. Artif Intell 89(1\u20132):31\u201371","journal-title":"Artif Intell"},{"key":"3102_CR2","unstructured":"Ilse M, Tomczak J, Welling M (2018) Attention-based deep multiple instance learning. In: Proc Int Conf Mach Learn pp. 2127\u20132136"},{"key":"3102_CR3","doi-asserted-by":"publisher","first-page":"107","DOI":"10.1016\/j.patrec.2013.07.002","volume":"37","author":"Y Pei","year":"2014","unstructured":"Pei Y, Fern XZ (2014) Constrained instance clustering in multi-instance multi-label learning. Pattern Recogn Lett 37:107\u2013114","journal-title":"Pattern Recogn Lett"},{"key":"3102_CR4","first-page":"2136","volume":"34","author":"Z Shao","year":"2021","unstructured":"Shao Z, Bian H, Chen Y, Wang Y, Zhang J, Ji X et al (2021) Transmil: transformer based correlated multiple instance learning for whole slide image classification. Adv Neural Inf Proces Syst 34:2136\u20132147","journal-title":"Adv Neural Inf Proces Syst"},{"key":"3102_CR5","doi-asserted-by":"crossref","unstructured":"Zheng T, Jiang K, Yao H (2024) Dynamic policy-driven adaptive multi-instance learning for whole slide image classification. In: Proc IEEE Comput Soc Conf Comput Vision Pattern Recognit pp. 8028\u20138037","DOI":"10.1109\/CVPR52733.2024.00767"},{"issue":"4","key":"3102_CR6","doi-asserted-by":"publisher","first-page":"1315","DOI":"10.1109\/TCDS.2024.3349705","volume":"16","author":"Y Liu","year":"2024","unstructured":"Liu Y, Ren J, Xu J, Bai X, Kaur R, Xia F (2024) Multiple instance learning for cheating detection and localization in online examinations. IEEE Trans Cogn Dev Syst 16(4):1315\u20131326","journal-title":"IEEE Trans Cogn Dev Syst"},{"key":"3102_CR7","doi-asserted-by":"publisher","first-page":"123","DOI":"10.1016\/j.patrec.2023.10.029","volume":"176","author":"Y Zhang","year":"2023","unstructured":"Zhang Y, Jiang Y, Zhang Q, Liu D (2023) Multi-label learning based on instance correlation and feature redundancy. Pattern Recogn Lett 176:123\u2013130","journal-title":"Pattern Recogn Lett"},{"issue":"1","key":"3102_CR8","first-page":"1","volume":"19","author":"W Chen","year":"2023","unstructured":"Chen W, Li G, Zhang X, Wang S, Li L, Huang Q (2023) Weakly supervised text-based actor-action video segmentation by clip-level multi-instance learning. ACM Trans Multimedia Comput Commun Appl 19(1):1\u201322","journal-title":"ACM Trans Multimedia Comput Commun Appl"},{"issue":"2","key":"3102_CR9","doi-asserted-by":"publisher","first-page":"445","DOI":"10.1007\/s13042-022-01572-0","volume":"14","author":"J Cheng","year":"2023","unstructured":"Cheng J, Zhang F, Wang G, Zhang W (2023) A multi-stage fusion instance learning method for anomalous event detection in videos. Int J Mach Learn Cybern 14(2):445\u2013454","journal-title":"Int J Mach Learn Cybern"},{"issue":"10","key":"3102_CR10","doi-asserted-by":"publisher","first-page":"12133","DOI":"10.1109\/TPAMI.2023.3277738","volume":"45","author":"F Wan","year":"2023","unstructured":"Wan F, Ye Q, Yuan T, Xu S, Liu J, Ji X, Huang Q (2023) Multiple instance differentiation learning for active object detection. IEEE Trans Pattern Anal Mach Intell 45(10):12133\u201312147","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"3102_CR11","first-page":"16138","volume":"38","author":"F Xu","year":"2024","unstructured":"Xu F, Zhao Y, Wu B, Huang Y, Ren Q, Xiao Y, He B, Zheng J, Yao J (2024) A label disambiguation-based multimodal massive multiple instance learning approach for immune repertoire classification. Proc AAAI Conf Artif Intell 38:16138\u201316146","journal-title":"Proc AAAI Conf Artif Intell"},{"key":"3102_CR12","doi-asserted-by":"crossref","unstructured":"Zhou Z-H, Xu J-M (2007) On the relation between multi-instance learning and semi-supervised learning. In: Proc Int Conf Mach Learn pp. 1167\u20131174","DOI":"10.1145\/1273496.1273643"},{"key":"3102_CR13","doi-asserted-by":"crossref","unstructured":"Zhou Z-H, Sun Y-Y, Li Y-F (2009) Multi-instance learning by treating instances as non-iid samples. In: Proc Int Conf Mach Learn pp. 1249\u20131256","DOI":"10.1145\/1553374.1553534"},{"key":"3102_CR14","doi-asserted-by":"crossref","unstructured":"McGovern A, Jensen D (2003) Identifying predictive structures in relational data using multiple instance learning. In: Proc Int Conf Mach Learn pp. 528\u2013535","DOI":"10.21236\/ADA465314"},{"key":"3102_CR15","unstructured":"Tu M, Huang J, He X, Zhou B (2019) Multiple instance learning with graph neural networks. arXiv preprint arXiv:1906.04881"},{"key":"3102_CR16","doi-asserted-by":"crossref","unstructured":"Waqas M, Ahmed SU, Tahir MA, Wu J, Qureshi R (2024) Exploring multiple instance learning (mil): a brief survey. Expert Sys Appl 123893","DOI":"10.1016\/j.eswa.2024.123893"},{"issue":"20","key":"3102_CR17","doi-asserted-by":"publisher","first-page":"4323","DOI":"10.3390\/electronics12204323","volume":"12","author":"S Fatima","year":"2023","unstructured":"Fatima S, Ali S, Kim H-C (2023) A comprehensive review on multiple instance learning. Electronics 12(20):4323","journal-title":"Electronics"},{"key":"3102_CR18","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 (2018) Multiple instance learning: a survey of problem characteristics and applications. Pattern Recogn 77:329\u2013353","journal-title":"Pattern Recogn"},{"issue":"1","key":"3102_CR19","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1017\/S026988890999035X","volume":"25","author":"J Foulds","year":"2010","unstructured":"Foulds J, Frank E (2010) A review of multi-instance learning assumptions. Knowl Eng Rev 25(1):1\u201325","journal-title":"Knowl Eng Rev"},{"key":"3102_CR20","doi-asserted-by":"publisher","first-page":"155","DOI":"10.1007\/s10994-016-5560-1","volume":"105","author":"X-S Wei","year":"2016","unstructured":"Wei X-S, Zhou Z-H (2016) An empirical study on image bag generators for multi-instance learning. Mach Learn 105:155\u2013198","journal-title":"Mach Learn"},{"key":"3102_CR21","doi-asserted-by":"crossref","unstructured":"Zhu L, Zhao B, Gao Y (2008) Multi-class multi-instance learning for lung cancer image classification based on bag feature selection. In: 2008 Fifth International Conference on Fuzzy Systems and Knowledge Discovery. IEEE 2:487\u2013492","DOI":"10.1109\/FSKD.2008.54"},{"key":"3102_CR22","unstructured":"Ju W, Yi S, Wang Y, Long Q, Luo J, Xiao Z, Zhang M A (2024) Survey of data-efficient graph learning. arXiv preprint arXiv:2402.00447"},{"key":"3102_CR23","doi-asserted-by":"crossref","unstructured":"Wang L, Sahbi H (2013) Directed acyclic graph kernels for action recognition. In: Proc IEEE Int Conf Comput Vis pp. 3168\u20133175","DOI":"10.1109\/ICCV.2013.393"},{"key":"3102_CR24","doi-asserted-by":"publisher","first-page":"148","DOI":"10.1016\/j.patcog.2015.11.022","volume":"53","author":"Y Yi","year":"2016","unstructured":"Yi Y, Lin M (2016) Human action recognition with graph-based multiple-instance learning. Pattern Recogn 53:148\u2013162","journal-title":"Pattern Recogn"},{"issue":"8","key":"3102_CR25","doi-asserted-by":"publisher","first-page":"1616","DOI":"10.1109\/TMM.2016.2572000","volume":"18","author":"X Ding","year":"2016","unstructured":"Ding X, Li B, Xiong W, Guo W, Hu W, Wang B (2016) Multi-instance multi-label learning combining hierarchical context and its application to image annotation. IEEE Trans Multimedia 18(8):1616\u20131627","journal-title":"IEEE Trans Multimedia"},{"issue":"12","key":"3102_CR26","doi-asserted-by":"publisher","first-page":"2554","DOI":"10.1109\/TPAMI.2017.2669303","volume":"39","author":"B Li","year":"2017","unstructured":"Li B, Yuan C, Xiong W, Hu W, Peng H, Ding X, Maybank S (2017) Multi-view multi-instance learning based on joint sparse representation and multi-view dictionary learning. IEEE Trans Pattern Anal Mach Intell 39(12):2554\u20132560","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"issue":"5","key":"3102_CR27","doi-asserted-by":"publisher","first-page":"808","DOI":"10.1016\/j.media.2014.04.006","volume":"18","author":"T Tong","year":"2014","unstructured":"Tong T, Wolz R, Gao Q, Guerrero R, Hajnal JV, Rueckert D, Initiative ADN et al (2014) Multiple instance learning for classification of dementia in brain MRI. Med Image Anal 18(5):808\u2013818","journal-title":"Med Image Anal"},{"issue":"19","key":"3102_CR28","doi-asserted-by":"publisher","first-page":"12263","DOI":"10.1007\/s11042-016-3494-z","volume":"75","author":"C Liu","year":"2016","unstructured":"Liu C, Chen T, Ding X, Zou H, Tong Y (2016) A multi-instance multi-label learning algorithm based on instance correlations. Multimedia Tools Appl 75(19):12263\u201312284","journal-title":"Multimedia Tools Appl"},{"key":"3102_CR29","doi-asserted-by":"publisher","first-page":"112","DOI":"10.1016\/j.compmedimag.2018.08.008","volume":"69","author":"P Cao","year":"2018","unstructured":"Cao P, Ren F, Wan C, Yang J, Zaiane O (2018) Efficient multi-kernel multi-instance learning using weakly supervised and imbalanced data for diabetic retinopathy diagnosis. Comput Med Imaging Graph 69:112\u2013124","journal-title":"Comput Med Imaging Graph"},{"key":"3102_CR30","doi-asserted-by":"crossref","unstructured":"Hajimirsadeghi H, Yan W, Vahdat A, Mori G (2015) Visual recognition by counting instances: a multi-instance cardinality potential kernel. In: Proc IEEE Comput Soc Conf Comput Vision Pattern Recognit pp. 2596\u20132605","DOI":"10.1109\/CVPR.2015.7298875"},{"key":"3102_CR31","doi-asserted-by":"crossref","unstructured":"Zhao Y, Wang Y, Wang Z, Zhang C (2021) Multi-graph multi-label learning with dual-granularity labeling. In: Proc ACM SIGKDD Int Conf Knowl Discov Data Min pp. 2327\u20132337","DOI":"10.1145\/3447548.3467339"},{"key":"3102_CR32","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2023.121900","volume":"238","author":"J Zhang","year":"2024","unstructured":"Zhang J, Wu Y, Hao F, Liu X, Li M, Zhou D, Zheng W (2024) Double similarities weighted multi-instance learning kernel and its application. Expert Sys Appl 238:121900","journal-title":"Expert Sys Appl"},{"key":"3102_CR33","doi-asserted-by":"crossref","unstructured":"Raju A, Yao J, Haq MM, Jonnagaddala J, Huang J (2020) Graph attention multi-instance learning for accurate colorectal cancer staging. In: Med Image Comput Comput Assist Interv pp. 529\u2013539. Springer","DOI":"10.1007\/978-3-030-59722-1_51"},{"key":"3102_CR34","doi-asserted-by":"crossref","unstructured":"Zhao Y, Yang F, Fang Y, Liu H, Zhou N, Zhang J, Sun J, Yang S, Menze B, Fan X, et al (2020) Predicting lymph node metastasis using histopathological images based on multiple instance learning with deep graph convolution. In: Proc IEEE Comput Soc Conf Comput Vision Pattern Recognit pp. 4837\u20134846","DOI":"10.1109\/CVPR42600.2020.00489"},{"key":"3102_CR35","doi-asserted-by":"crossref","unstructured":"Adnan M, Kalra S, Tizhoosh H.R. (2020) Representation learning of histopathology images using graph neural networks. In: Proc IEEE Comput Soc Conf Comput Vision Pattern Recognit Workshops, pp. 988\u2013989","DOI":"10.1109\/CVPRW50498.2020.00502"},{"key":"3102_CR36","doi-asserted-by":"crossref","unstructured":"Chen R.J., Lu M.Y., Shaban M, Chen C, Chen T.Y, Williamson DF, Mahmood F (2021) Whole slide images are 2d point clouds: context-aware survival prediction using patch-based graph convolutional networks. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 339\u2013349. Springer","DOI":"10.1007\/978-3-030-87237-3_33"},{"key":"3102_CR37","first-page":"7922","volume":"36","author":"S Pal","year":"2022","unstructured":"Pal S, Valkanas A, Regol F, Coates M (2022) Bag graph: multiple instance learning using bayesian graph neural networks. Proc AAAI Conf Artif Intell 36:7922\u20137930","journal-title":"Proc AAAI Conf Artif Intell"},{"key":"3102_CR38","doi-asserted-by":"crossref","unstructured":"Guan Y, Zhang J, Tian K, Yang S, Dong P, Xiang J, Yang W, Huang J, Zhang Y, Han X (2022) Node-aligned graph convolutional network for whole-slide image representation and classification. In: Proc IEEE Comput Soc Conf Comput Vision Pattern Recognit pp. 18813\u201318823","DOI":"10.1109\/CVPR52688.2022.01825"},{"key":"3102_CR39","doi-asserted-by":"publisher","first-page":"53","DOI":"10.1016\/j.patrec.2022.01.003","volume":"156","author":"X Li","year":"2022","unstructured":"Li X, Wu H, Li M, Liu H (2022) Multi-label video classification via coupling attentional multiple instance learning with label relation graph. Pattern Recogn Lett 156:53\u201359","journal-title":"Pattern Recogn Lett"},{"issue":"10","key":"3102_CR40","doi-asserted-by":"publisher","first-page":"3000","DOI":"10.1109\/TMI.2023.3273236","volume":"42","author":"J Shi","year":"2023","unstructured":"Shi J, Tang L, Li Y, Zhang X, Gao Z, Zheng Y, Wang C, Gong T, Li C (2023) A structure-aware hierarchical graph-based multiple instance learning framework for PT staging in histopathological image. IEEE Trans Med Imaging 42(10):3000\u20133011","journal-title":"IEEE Trans Med Imaging"},{"key":"3102_CR41","doi-asserted-by":"crossref","unstructured":"Bontempo G, Bartolini N, Lovino M, Bolelli F, Virtanen A, Ficarra E (2023) Enhancing PFI prediction with GDS-MIL: a graph-based dual stream MIL approach. In: International Conference on Image Analysis and Processing, pp. 550\u2013562. Springer","DOI":"10.1007\/978-3-031-43148-7_46"},{"issue":"12","key":"3102_CR42","doi-asserted-by":"publisher","first-page":"10528","DOI":"10.1109\/TNNLS.2022.3168431","volume":"34","author":"Y Zeng","year":"2022","unstructured":"Zeng Y, Wang Y, Liao D, Li G, Huang W, Xu J, Cao D, Man H (2022) Keyword-based diverse image retrieval with variational multiple instance graph. IEEE Trans Neural Networks Learn Sys 34(12):10528\u201310537","journal-title":"IEEE Trans Neural Networks Learn Sys"},{"issue":"6","key":"3102_CR43","doi-asserted-by":"publisher","first-page":"5549","DOI":"10.1007\/s11760-024-03254-6","volume":"18","author":"Y Ma","year":"2024","unstructured":"Ma Y, Luo Y, Yang Z (2024) GCN-based mil: multi-instance learning utilizing structural relationships among instances. Signal Image Video Process 18(6):5549\u20135561","journal-title":"Signal Image Video Process"},{"key":"3102_CR44","doi-asserted-by":"crossref","unstructured":"Yu M, Wang H, Fu X, Gao J, Liu Z, Li X (2024) DualGCN-MIL: Whole slide image classification based on double relationship graph learning. In: ICASSP 2024-2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1986\u20131990","DOI":"10.1109\/ICASSP48485.2024.10448300"},{"key":"3102_CR45","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2024.103197","volume":"96","author":"R Bazargani","year":"2024","unstructured":"Bazargani R, Fazli L, Gleave M, Goldenberg L, Bashashati A, Salcudean S (2024) Multi-scale relational graph convolutional network for multiple instance learning in histopathology images. Med Image Anal 96:103197","journal-title":"Med Image Anal"},{"issue":"4","key":"3102_CR46","doi-asserted-by":"publisher","first-page":"6693","DOI":"10.1109\/TNNLS.2024.3392575","volume":"36","author":"X Zhao","year":"2024","unstructured":"Zhao X, Dai Q, Bai X, Wu J, Peng H, Peng H, Yu Z, Yu PS (2024) Reinforced GNNS for multiple instance learning. IEEE Trans Neural Networks Learn Sys 36(4):6693\u20136707","journal-title":"IEEE Trans Neural Networks Learn Sys"},{"key":"3102_CR47","doi-asserted-by":"crossref","unstructured":"Kim J, Wong B, Fu H, Qui\u00f1ones W.R, Yi M (2024) Micromil: Graph-based contextual multiple instance learning for patient diagnosis using microscopy images. arXiv preprint arXiv:2407.21604","DOI":"10.1007\/978-3-032-04927-8_37"},{"key":"3102_CR48","doi-asserted-by":"crossref","unstructured":"Wang F, Xin J, Zhao W, Jiang Y, Yeung M, Wang L, Yu L (2025) TAD-graph: enhancing whole slide image analysis via task-aware subgraph disentanglement. IEEE Trans Med Imag","DOI":"10.1109\/TMI.2025.3545680"},{"key":"3102_CR49","unstructured":"Pereira R, Verdelho MR, Barata C, Santiago C (2025) The role of graph-based MIL and interventional training in the generalization of WSI classifiers. arXiv preprint arXiv:2501.19048"},{"issue":"11","key":"3102_CR50","doi-asserted-by":"publisher","first-page":"2274","DOI":"10.1109\/TPAMI.2012.120","volume":"34","author":"R Achanta","year":"2012","unstructured":"Achanta R, Shaji A, Smith K, Lucchi A, Fua P, S\u00fcsstrunk S (2012) Slic superpixels compared to state-of-the-art superpixel methods. IEEE Trans Pattern Anal Mach Intell 34(11):2274\u20132282","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"3102_CR51","unstructured":"Andrews S, Tsochantaridis I, Hofmann T (2002) Support vector machines for multiple-instance learning. Adv Neur Inf Proces Syst 15"},{"key":"3102_CR52","doi-asserted-by":"crossref","unstructured":"Decenci\u00e8re E, Zhang X, Cazuguel G, Lay B, Cochener B, Trone C, Gain P, Ord\u00f3\u00f1ez-Varela J-R, Massin P, Erginay A, et al (2014) Feedback on a publicly distributed image database: the Messidor database. Image Anal Stereol 231\u2013234","DOI":"10.5566\/ias.1155"},{"issue":"12","key":"3102_CR53","doi-asserted-by":"publisher","first-page":"1931","DOI":"10.1109\/TPAMI.2006.248","volume":"28","author":"Y Chen","year":"2006","unstructured":"Chen Y, Bi J, Wang JZ (2006) Miles: multiple-instance learning via embedded instance selection. IEEE Trans Pattern Anal Mach Intell 28(12):1931\u20131947","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"3102_CR54","first-page":"5742","volume":"34","author":"X Shi","year":"2020","unstructured":"Shi X, Xing F, Xie Y, Zhang Z, Cui L, Yang L (2020) Loss-based attention for deep multiple instance learning. Proc AAAI Conf Artif Intell 34:5742\u20135749","journal-title":"Proc AAAI Conf Artif Intell"},{"issue":"5","key":"3102_CR55","doi-asserted-by":"publisher","first-page":"1196","DOI":"10.1109\/TMI.2016.2525803","volume":"35","author":"K Sirinukunwattana","year":"2016","unstructured":"Sirinukunwattana K, Raza SEA, Tsang Y-W, Snead DR, Cree IA, Rajpoot NM (2016) Locality sensitive deep learning for detection and classification of nuclei in routine colon cancer histology images. IEEE Trans Med Imaging 35(5):1196\u20131206","journal-title":"IEEE Trans Med Imaging"},{"key":"3102_CR56","doi-asserted-by":"publisher","first-page":"9","DOI":"10.1016\/j.neucom.2019.09.044","volume":"375","author":"Y Benhammou","year":"2020","unstructured":"Benhammou Y, Achchab B, Herrera F, Tabik S (2020) Breakhis based breast cancer automatic diagnosis using deep learning: taxonomy, survey and insights. Neurocomputing 375:9\u201324","journal-title":"Neurocomputing"},{"issue":"2","key":"3102_CR57","doi-asserted-by":"publisher","first-page":"135","DOI":"10.1007\/s10489-005-5602-z","volume":"22","author":"Z-H Zhou","year":"2005","unstructured":"Zhou Z-H, Jiang K, Li M (2005) Multi-instance learning based web mining. Appl Intell 22(2):135\u2013147","journal-title":"Appl Intell"},{"key":"3102_CR58","unstructured":"Srinivasan A, Muggleton S, King R (1995) Comparing the use of background knowledge by inductive logic programming systems. In: Proceedings of the 5th International Workshop on Inductive Logic Programming pp. 199\u2013230"},{"key":"3102_CR59","unstructured":"Xiao H, Rasul K, Vollgraf R (2017) Fashion-mnist: a novel image dataset for benchmarking machine learning algorithms. arXiv preprint arXiv:1708.07747"},{"key":"3102_CR60","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2023.103020","volume":"91","author":"P Liu","year":"2024","unstructured":"Liu P, Ji L, Ye F, Fu B (2024) Advmil: adversarial multiple instance learning for the survival analysis on whole-slide images. Med Image Anal 91:103020","journal-title":"Med Image Anal"},{"issue":"12","key":"3102_CR61","doi-asserted-by":"publisher","first-page":"6617","DOI":"10.1109\/TAI.2024.3454591","volume":"5","author":"Z Shao","year":"2024","unstructured":"Shao Z, Dai L, Wang Y, Wang H, Zhang Y (2024) Augdiff: diffusion-based feature augmentation for multiple instance learning in whole slide image. IEEE Trans Artif Intell 5(12):6617\u20136628","journal-title":"IEEE Trans Artif Intell"},{"key":"3102_CR62","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2025.103647","volume":"105","author":"Z Mi","year":"2025","unstructured":"Mi Z, Zhang J, Liu X, Yue G, Yue J, Wei M, Li Y, Wu Y (2025) Multi-instance curriculum learning for histopathology image classification with bias reduction. Med Image Anal 105:103647","journal-title":"Med Image Anal"},{"key":"3102_CR63","unstructured":"Ghaffarzadegan S (2018) Deep multiple instance feature learning via variational autoencoder. In: AAAI Workshops, pp. 132\u2013140"},{"issue":"3","key":"3102_CR64","first-page":"1","volume":"16","author":"R Yang","year":"2025","unstructured":"Yang R, Ma J, Gao W, Lin H (2025) LLM-enhanced multiple instance learning for joint rumor and stance detection with social context information. ACM Trans Intell Syst Technol 16(3):1\u201327","journal-title":"ACM Trans Intell Syst Technol"},{"key":"3102_CR65","doi-asserted-by":"crossref","unstructured":"Shi J, Li C, Gong T, Zheng Y, Fu H (2024) Vila-mil: Dual-scale vision-language multiple instance learning for whole slide image classification. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 11248\u201311258","DOI":"10.1109\/CVPR52733.2024.01069"},{"key":"3102_CR66","doi-asserted-by":"crossref","unstructured":"Kim K, Lee Y, Park D, Eo T, Youn D, Lee H, Hwang D (2024) LLM-guided multi-modal multiple instance learning for 5-year overall survival prediction of lung cancer. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 239\u2013249. Springer","DOI":"10.1007\/978-3-031-72384-1_23"},{"key":"3102_CR67","unstructured":"Raju S, Islam MM, Haque MR, Altaheri H, Karray F (2025) GNN-ViTCap: Gnn-enhanced multiple instance learning with vision transformers for whole slide image classification and captioning. arXiv preprint arXiv:2507.07006"},{"issue":"3","key":"3102_CR68","first-page":"1","volume":"16","author":"R Yang","year":"2025","unstructured":"Yang R, Ma J, Gao W, Lin H (2025) LLM-enhanced multiple instance learning for joint rumor and stance detection with social context information. ACM Trans Intell Syst Technol 16(3):1\u201327","journal-title":"ACM Trans Intell Syst Technol"}],"container-title":["International Journal of Machine Learning and Cybernetics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13042-026-03102-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s13042-026-03102-8","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13042-026-03102-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,7,6]],"date-time":"2026-07-06T05:57:53Z","timestamp":1783317473000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s13042-026-03102-8"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,4,24]]},"references-count":68,"journal-issue":{"issue":"6","published-print":{"date-parts":[[2026,6]]}},"alternative-id":["3102"],"URL":"https:\/\/doi.org\/10.1007\/s13042-026-03102-8","relation":{},"ISSN":["1868-8071","1868-808X"],"issn-type":[{"value":"1868-8071","type":"print"},{"value":"1868-808X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,4,24]]},"assertion":[{"value":"6 August 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"3 April 2026","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"24 April 2026","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}],"article-number":"274"}}