{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,15]],"date-time":"2026-04-15T16:08:37Z","timestamp":1776269317597,"version":"3.50.1"},"reference-count":52,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2026,4,15]],"date-time":"2026-04-15T00:00:00Z","timestamp":1776211200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2026,4,15]],"date-time":"2026-04-15T00:00:00Z","timestamp":1776211200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/100000050","name":"National Heart, Lung, and Blood Institute","doi-asserted-by":"publisher","award":["U24-HL148865"],"award-info":[{"award-number":["U24-HL148865"]}],"id":[{"id":"10.13039\/100000050","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Med Syst"],"DOI":"10.1007\/s10916-026-02379-0","type":"journal-article","created":{"date-parts":[[2026,4,15]],"date-time":"2026-04-15T15:13:54Z","timestamp":1776266034000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["End-to-End Multimodal Multiple Instance Learning for Cancer Histopathology Classification with Dual-Attention Fusion"],"prefix":"10.1007","volume":"50","author":[{"given":"Satoshi","family":"Shirae","sequence":"first","affiliation":[]},{"given":"Shyam S.","family":"Debsarkar","sequence":"additional","affiliation":[]},{"given":"Hiroharu","family":"Kawanaka","sequence":"additional","affiliation":[]},{"given":"Bruce J.","family":"Aronow","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7163-7453","authenticated-orcid":false,"given":"V. B. Surya","family":"Prasath","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2026,4,15]]},"reference":[{"key":"2379_CR1","unstructured":"Rubin, R., Strayer, D. S., and Rubin, E., Rubin\u2019s Pathology: Clinicopathologic Foundations of Medicine. Lippincott Williams & Wilkins, Baltimore, MD, USA, 2008."},{"key":"2379_CR2","doi-asserted-by":"publisher","unstructured":"Huang, Y., Lei, Y., Wang, Q., Li, D., Ma, L., Guo, L., Tang, M., Liu, G., Yan, Q., Shen, L., et al., Telepathology consultation for frozen section diagnosis in china. Diagn. Pathol. 13:1\u20136, 2018. https:\/\/doi.org\/10.1186\/s13000-018-0705-0","DOI":"10.1186\/s13000-018-0705-0"},{"key":"2379_CR3","doi-asserted-by":"publisher","unstructured":"Boyce, B., An update on the validation of whole slide imaging systems following fda approval of a system for a routine pathology diagnostic service in the united states. Biotechn. Histochem. 92(6):381\u2013389, 2017. https:\/\/doi.org\/10.1080\/10520295.2017.1355476","DOI":"10.1080\/10520295.2017.1355476"},{"key":"2379_CR4","doi-asserted-by":"publisher","unstructured":"Boroujeni, A. M., Dehghani, A., Yousefi, E., and Gupta, R., Whole slide imaging for her2\/neu immunohistochemistry: Reproducibility study for digital and paired glass slide interpretation. Am. J. Clin. Pathol. 146(suppl_1):36, 2016. https:\/\/doi.org\/10.1093\/ajcp\/aqw161.036","DOI":"10.1093\/ajcp\/aqw161.036"},{"key":"2379_CR5","doi-asserted-by":"publisher","unstructured":"Gurcan, M. N., Boucheron, L. E., Can, A., Madabhushi, A., Rajpoot, N. M., and Yener, B., Histopathological image analysis: A review. IEEE Rev. Biomed. Eng. 2:147\u2013171, 2009. https:\/\/doi.org\/10.1109\/RBME.2009.2034865","DOI":"10.1109\/RBME.2009.2034865"},{"key":"2379_CR6","doi-asserted-by":"publisher","unstructured":"Laak, J., Litjens, G., and Ciompi, F., Deep learning in histopathology: the path to the clinic. Nat. Med. 27(5):775\u2013784, 2021. https:\/\/doi.org\/10.1038\/s41591-021-01343-4","DOI":"10.1038\/s41591-021-01343-4"},{"key":"2379_CR7","doi-asserted-by":"publisher","unstructured":"Gadermayr, M., and Tschuchnig, M., Multiple instance learning for digital pathology: A review of the state-of-the-art, limitations & future potential. Computer. Med. Imag. Graph. 112:102337, 2024. https:\/\/doi.org\/10.1016\/j.compmedimag.2024.102337","DOI":"10.1016\/j.compmedimag.2024.102337"},{"key":"2379_CR8","doi-asserted-by":"publisher","unstructured":"Yonekura, A., Kawanaka, H., Prasath, V. B. S., Aronow, B. J., and Takase, H., Automatic disease stage classification of glioblastoma multiforme histopathological images using deep convolutional neural network. Biomed. Eng. Lett. 8(3):321\u2013327, 2018. https:\/\/doi.org\/10.1007\/s13534-018-0077-0","DOI":"10.1007\/s13534-018-0077-0"},{"key":"2379_CR9","doi-asserted-by":"publisher","unstructured":"Shirae, S., Debsarkar, S. S., Kawanaka, H., Aronow, B., and Prasath, V. B. S., Multimodal ensemble fusion deep learning using histopathological images and clinical data for glioma subtype classification. IEEE Access 13:57780\u201357797, 2025. https:\/\/doi.org\/10.1109\/ACCESS.2025.3556713","DOI":"10.1109\/ACCESS.2025.3556713"},{"key":"2379_CR10","doi-asserted-by":"publisher","unstructured":"Lu, M. Y., Williamson, D. F., Chen, T. Y., Chen, R. J., Barbieri, M., and Mahmood, F., Data-efficient and weakly supervised computational pathology on whole-slide images. Nat. Biomed. Eng. 5(6):555\u2013570, 2021. https:\/\/doi.org\/10.1038\/s41551-020-00682-w","DOI":"10.1038\/s41551-020-00682-w"},{"key":"2379_CR11","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.: Transmil: Transformer based correlated multiple instance learning for whole slide image classification. Advances in Neural Information Processing Systems. 34, 2136\u20132147 (2021)","journal-title":"Advances in Neural Information Processing Systems."},{"key":"2379_CR12","doi-asserted-by":"crossref","unstructured":"Li, B., Li, Y., Eliceiri, K.W.: Dual-stream multiple instance learning network for whole slide image classification with self-supervised contrastive learning. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 14318\u201314328 (2021). https:\/\/doi.org\/10.1109\/CVPR46437.2021.01409","DOI":"10.1109\/CVPR46437.2021.01409"},{"key":"2379_CR13","doi-asserted-by":"crossref","unstructured":"Qin, D., Leichner, C., Delakis, M., Fornoni, M., Luo, S., Yang, F., Wang, W., Banbury, C., Ye, C., Akin, B., et al.: Mobilenetv4: universal models for the mobile ecosystem. In: European Conference on Computer Vision, pp. 78\u201396 (2024). https:\/\/doi.org\/10.1007\/978-3-031-73661-2_5 . Springer","DOI":"10.1007\/978-3-031-73661-2_5"},{"key":"2379_CR14","doi-asserted-by":"crossref","unstructured":"Shirae, S., Debsarkar, S.S., Kawanaka, H., Aronow, B., Prasath, V.S.: Multiple instance learning using reduced deep learning model for glioma subtype classification. In: Procedia Computer Science, vol. 270, pp. 2434\u20132442. Elsevier, Osaka, Japan (2025). https:\/\/doi.org\/10.1016\/j.procs.2025.09.365","DOI":"10.1016\/j.procs.2025.09.365"},{"key":"2379_CR15","doi-asserted-by":"publisher","unstructured":"Acosta, J. N., Falcone, G. J., Rajpurkar, P., and Topol, E. J., Multimodal biomedical ai. Nat. Med. 28(9):1773\u20131784, 2022. https:\/\/doi.org\/10.1038\/s41591-022-01981-2","DOI":"10.1038\/s41591-022-01981-2"},{"key":"2379_CR16","unstructured":"Nutt, C. L., Mani, D., Betensky, R. A., Tamayo, P., Cairncross, J. G., Ladd, C., Pohl, U., Hartmann, C., McLaughlin, M. E., Batchelor, T. T., et al., Gene expression-based classification of malignant gliomas correlates better with survival than histological classification. Cancer Res. 63(7):1602\u20131607, 2003."},{"key":"2379_CR17","doi-asserted-by":"publisher","unstructured":"S\u00f8rlie, T., Perou, C. M., Tibshirani, R., Aas, T., Geisler, S., Johnsen, H., Hastie, T., Eisen, M. B., Van De\u00a0Rijn, M., Jeffrey, S. S., et al., Gene expression patterns of breast carcinomas distinguish tumor subclasses with clinical implications. Proceed. Nat. Acad. Sci. 98(19):10869\u201310874, 2001. https:\/\/doi.org\/10.1073\/pnas.191367098","DOI":"10.1073\/pnas.191367098"},{"key":"2379_CR18","doi-asserted-by":"publisher","unstructured":"Valk, P. J., Verhaak, R. G., Beijen, M. A., Erpelinck, C. A., Doorn-Khosrovani, S. B. v. W., Boer, J. M., Beverloo, H. B., Moorhouse, M. J., Van Der\u00a0Spek, P. J., L\u00f6wenberg, B., et al., Prognostically useful gene-expression profiles in acute myeloid leukemia. New England J. Med. 350(16):1617\u20131628, 2004. https:\/\/doi.org\/10.1056\/NEJMoa040465","DOI":"10.1056\/NEJMoa040465"},{"key":"2379_CR19","doi-asserted-by":"publisher","unstructured":"Schwartzbaum, J. A., Fisher, J. L., Aldape, K. D., and Wrensch, M., Epidemiology and molecular pathology of glioma. Nat. Clin. Pract. Neurol. 2(9):494\u2013503, 2006. https:\/\/doi.org\/10.1038\/ncpneuro0289","DOI":"10.1038\/ncpneuro0289"},{"key":"2379_CR20","doi-asserted-by":"publisher","unstructured":"Agnihotri, S., Burrell, K. E., Wolf, A., Jalali, S., Hawkins, C., Rutka, J. T., and Zadeh, G., Glioblastoma, a brief review of history, molecular genetics, animal models and novel therapeutic strategies. Archivum Immunologiae et Therapiae Experimentalis. 61:25\u201341, 2013. https:\/\/doi.org\/10.1007\/s00005-012-0203-0","DOI":"10.1007\/s00005-012-0203-0"},{"key":"2379_CR21","doi-asserted-by":"publisher","unstructured":"Messali, A., Villacorta, R., and Hay, J. W., A review of the economic burden of glioblastoma and the cost effectiveness of pharmacologic treatments. Pharmacoeconomics 32:1201\u20131212, 2014. https:\/\/doi.org\/10.1007\/s40273-014-0198-y","DOI":"10.1007\/s40273-014-0198-y"},{"key":"2379_CR22","doi-asserted-by":"publisher","unstructured":"Louis, D. N., Perry, A., Reifenberger, G., Von\u00a0Deimling, A., Figarella-Branger, D., Cavenee, W. K., Ohgaki, H., Wiestler, O. D., Kleihues, P., and Ellison, D. W., The 2016 world health organization classification of tumors of the central nervous system: a summary. Acta Neuropathologica 131:803\u2013820, 2016. https:\/\/doi.org\/10.1007\/s00401-016-1545-1","DOI":"10.1007\/s00401-016-1545-1"},{"key":"2379_CR23","doi-asserted-by":"publisher","unstructured":"Nakagaki, R., Debsarkar, S. S., Kawanaka, H., Aronow, B. J., and Prasath, V. S., Deep learning-based idh1 gene mutation prediction using histopathological imaging and clinical data. Comput Biol Med. 179:108902, 2024. https:\/\/doi.org\/10.1016\/j.compbiomed.2024.108902","DOI":"10.1016\/j.compbiomed.2024.108902"},{"key":"2379_CR24","doi-asserted-by":"publisher","unstructured":"Weller, M., Van Den\u00a0Bent, M., Tonn, J. C., Stupp, R., Preusser, M., Cohen-Jonathan-Moyal, E., Henriksson, R., Le\u00a0Rhun, E., Balana, C., Chinot, O., et al., European association for neuro-oncology (eano) guideline on the diagnosis and treatment of adult astrocytic and oligodendroglial gliomas. Lancet Oncol. 18(6):315\u2013329, 2017. https:\/\/doi.org\/10.1016\/S1470-2045(17)30194-8","DOI":"10.1016\/S1470-2045(17)30194-8"},{"key":"2379_CR25","doi-asserted-by":"publisher","unstructured":"Grimm, S. A., and Chamberlain, M. C., Anaplastic astrocytoma. CNS Oncology 5(3):145\u2013157, 2016. https:\/\/doi.org\/10.2217\/cns-2016-0002","DOI":"10.2217\/cns-2016-0002"},{"key":"2379_CR26","doi-asserted-by":"publisher","unstructured":"Van Den\u00a0Bent, M. J., Bromberg, J. E., and Buckner, J., Low-grade and anaplastic oligodendroglioma. Handbook Clin. Neurol. 134:361\u2013380, 2016. https:\/\/doi.org\/10.1016\/B978-0-12-802997-8.00022-0","DOI":"10.1016\/B978-0-12-802997-8.00022-0"},{"key":"2379_CR27","doi-asserted-by":"publisher","unstructured":"Pouratian, N., and Schiff, D., Management of low-grade glioma. Current Neurol. Neurosci. Rep. 10:224\u2013231, 2010. https:\/\/doi.org\/10.1007\/s11910-010-0105-7","DOI":"10.1007\/s11910-010-0105-7"},{"key":"2379_CR28","doi-asserted-by":"publisher","unstructured":"Berger, M. S., Hervey-Jumper, S., and Wick, W., Astrocytic gliomas who grades ii and iii. Handbook Clin. Neurol. 134:345\u2013360, 2016. https:\/\/doi.org\/10.1016\/B978-0-12-802997-8.00021-9","DOI":"10.1016\/B978-0-12-802997-8.00021-9"},{"key":"2379_CR29","doi-asserted-by":"publisher","unstructured":"Bent, M. J., Smits, M., Kros, J. M., and Chang, S. M., Diffuse infiltrating oligodendroglioma and astrocytoma. J. Clin. Oncol. 35(21):2394\u20132401, 2017. https:\/\/doi.org\/10.1200\/JCO.2017.72.6737","DOI":"10.1200\/JCO.2017.72.6737"},{"key":"2379_CR30","doi-asserted-by":"publisher","unstructured":"Le\u00a0Rhun, E., Taillibert, S., and Chamberlain, M. C. Anaplastic glioma: current treatment and management. Expert Rev. Neurotherapeut. 15(6):601\u2013620, 2015. https:\/\/doi.org\/10.1586\/14737175.2015.1042455","DOI":"10.1586\/14737175.2015.1042455"},{"key":"2379_CR31","doi-asserted-by":"publisher","unstructured":"Truong, A. H., Sharmanska, V., Limb\u00e4ck-Stanic, C., and Grech-Sollars, M., Optimization of deep learning methods for visualization of tumor heterogeneity and brain tumor grading through digital pathology. Neuro-Oncol. Adv. 2(1):110, 2020. https:\/\/doi.org\/10.1093\/noajnl\/vdaa110","DOI":"10.1093\/noajnl\/vdaa110"},{"key":"2379_CR32","doi-asserted-by":"publisher","unstructured":"Pei, L., Jones, K. A., Shboul, Z. A., Chen, J. Y., and Iftekharuddin, K. M., Deep neural network analysis of pathology images with integrated molecular data for enhanced glioma classification and grading. Front. Oncol. 11:668694, 2021. https:\/\/doi.org\/10.3389\/fonc.2021.668694","DOI":"10.3389\/fonc.2021.668694"},{"key":"2379_CR33","doi-asserted-by":"publisher","unstructured":"Siegel, R. L., Kratzer, T. B., Giaquinto, A. N., Sung, H., and Jemal, A., Cancer statistics, 2025. CA: Cancer J. Clinic. 75(1):10, 2025. https:\/\/doi.org\/10.3322\/caac.21871","DOI":"10.3322\/caac.21871"},{"key":"2379_CR34","doi-asserted-by":"publisher","unstructured":"Chang, J. T. -H., Lee, Y. M., and Huang, R. S., The impact of the cancer genome atlas on lung cancer. Translat. Res. 166(6):568\u2013585, 2015. https:\/\/doi.org\/10.1016\/j.trsl.2015.08.001","DOI":"10.1016\/j.trsl.2015.08.001"},{"key":"2379_CR35","doi-asserted-by":"publisher","unstructured":"Woodard,G. A., Jones, K. D., and Jablons, D. M. Lung cancer staging and prognosis. Lung cancer: Treatment and research, 170:47\u201375, 2016. https:\/\/doi.org\/10.1007\/978-3-319-40389-2_3","DOI":"10.1007\/978-3-319-40389-2_3"},{"key":"2379_CR36","doi-asserted-by":"publisher","unstructured":"Rekhtman, N., Paik, P. K., Arcila, M. E., Tafe, L. J., Oxnard, G. R., Moreira, A. L., Travis, W. D., Zakowski, M. F., Kris, M. G., and Ladanyi, M., Clarifying the spectrum of driver oncogene mutations in biomarker-verified squamous carcinoma of lung: Lack of egfr\/kra s and presence of pik3ca\/akt1 mutations. Clin. Cancer Res. 18(4):1167\u20131176, 2012. https:\/\/doi.org\/10.1158\/1078-0432.CCR-11-2109","DOI":"10.1158\/1078-0432.CCR-11-2109"},{"key":"2379_CR37","doi-asserted-by":"publisher","unstructured":"Coudray, N., Ocampo, P. S., Sakellaropoulos, T., Narula, N., Snuderl, M., Feny\u00f6, D., Moreira, A. L., Razavian, N., and Tsirigos, A., Classification and mutation prediction from non\u2013small cell lung cancer histopathology images using deep learning. Nat. Med. 24(10):1559\u20131567, 2018. https:\/\/doi.org\/10.1038\/s41591-018-0177-5","DOI":"10.1038\/s41591-018-0177-5"},{"key":"2379_CR38","doi-asserted-by":"publisher","unstructured":"Carrillo-Perez, F., Morales, J. C., Castillo-Secilla, D., Molina-Castro, Y., Guill\u00e9n, A., Rojas, I., and Herrera, L. J., Non-small-cell lung cancer classification via rna-seq and histology imaging probability fusion. BMC Bioinf. 22(1):454, 2021. https:\/\/doi.org\/10.1186\/s12859-021-04376-1","DOI":"10.1186\/s12859-021-04376-1"},{"key":"2379_CR39","doi-asserted-by":"publisher","unstructured":"Callahan, R., and Hurvitz, S. Human epidermal growth factor receptor-2-positive breast cancer: Current management of early, advanced, and recurrent disease. Curr. Opin. Obstet. Gynecol. 23(1):37\u201343, 2011. https:\/\/doi.org\/10.1097\/GCO.0b013e3283414e87","DOI":"10.1097\/GCO.0b013e3283414e87"},{"key":"2379_CR40","doi-asserted-by":"crossref","unstructured":"Graudenzi, A., Cava, C., Bertoli, G., Fromm, B., Flatmark, K., Mauri, G., Castiglioni, I., et al., Pathway-based classification of breast cancer subtypes. Front. Biosci. 22(10):1697\u20131712, 2017.","DOI":"10.2741\/4566"},{"key":"2379_CR41","doi-asserted-by":"publisher","unstructured":"Boudissa, S., Debsarkar, S. S., Kawanaka, H., Aronow, B., and Prasath, V. S., Vision transformers and CNN-based knowledge-distillation for histopathological image classification. In: Fuzzy Systems and Data Mining X. Matsue, Japan, pp. 231\u2013239, 2024. https:\/\/doi.org\/10.3233\/FAIA241423","DOI":"10.3233\/FAIA241423"},{"key":"2379_CR42","doi-asserted-by":"publisher","unstructured":"Tao, M., Song, T., Du, W., Han, S., Zuo, C., Li, Y., Wang, Y., and Yang, Z., Classifying breast cancer subtypes using multiple kernel learning based on omics data. Genes 10(3):200, 2019. https:\/\/doi.org\/10.3390\/genes10030200","DOI":"10.3390\/genes10030200"},{"key":"2379_CR43","doi-asserted-by":"crossref","unstructured":"Ebili, H. O., Oluwasola, A. O., and Olopade, O. I., Molecular subtypes of breast cancer. Taylor & Francis, 2014.","DOI":"10.2217\/ebo.13.374"},{"key":"2379_CR44","doi-asserted-by":"publisher","unstructured":"Nguyen, P. L., Taghian, A. G., Katz, M. S., Niemierko, A., Abi\u00a0Raad, R. F., Boon, W. L., Bellon, J. R., Wong, J. S., Smith, B. L., and Harris, J. R., Breast cancer subtype approximated by estrogen receptor, progesterone receptor, and her-2 is associated with local and distant recurrence after breast-conserving therapy. J. Clin. Oncol. 26(14):2373\u20132378, 2008. https:\/\/doi.org\/10.1200\/JCO.2007.14.4287","DOI":"10.1200\/JCO.2007.14.4287"},{"key":"2379_CR45","doi-asserted-by":"publisher","unstructured":"Lin, Y., Zhang, W., Cao, H., Li, G., and Du, W. Classifying breast cancer subtypes using deep neural networks based on multi-omics data. Genes 11(8):888, 2020. https:\/\/doi.org\/10.3390\/genes11080888","DOI":"10.3390\/genes11080888"},{"key":"2379_CR46","doi-asserted-by":"publisher","unstructured":"Hu, H., Peng, R., Tai, Y. -W., and Tang, C. -K., Network trimming: A data-driven neuron pruning approach towards efficient deep architectures, 2016. arXiv preprint arXiv:1607.03250. https:\/\/doi.org\/10.48550\/arXiv.1607.03250","DOI":"10.48550\/arXiv.1607.03250"},{"key":"2379_CR47","doi-asserted-by":"publisher","unstructured":"Xiong, Y., Zeng, Z., Chakraborty, R., Tan, M., Fung, G., Li, Y., and Singh, V., Nystr\u00f6mformer: A nystr\u00f6m-based algorithm for approximating self-attention. In: Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 35, pp. 14138\u201314148, 2021. https:\/\/doi.org\/10.1609\/aaai.v35i16.17664","DOI":"10.1609\/aaai.v35i16.17664"},{"key":"2379_CR48","doi-asserted-by":"publisher","unstructured":"Subramanian, A., Tamayo, P., Mootha, V. K., Mukherjee, S., Ebert, B. L., Gillette, M. A., Paulovich, A., Pomeroy, S. L., Golub, T. R., Lander, E. S., et al., Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles. Proceed. Nat. Acad. Sci. 102(43):15545\u201315550, 2005. https:\/\/doi.org\/10.1073\/pnas.0506580102","DOI":"10.1073\/pnas.0506580102"},{"key":"2379_CR49","doi-asserted-by":"publisher","unstructured":"Marcolini, A., Bussola, N., Arbitrio, E., Amgad, M., Jurman, G., and Furlanello, C., histolab: A python library for reproducible digital pathology preprocessing with automated testing. SoftwareX 20:101237, 2022. https:\/\/doi.org\/10.1016\/j.softx.2022.101237","DOI":"10.1016\/j.softx.2022.101237"},{"key":"2379_CR50","doi-asserted-by":"publisher","unstructured":"Chen, R. J., Lu, M. Y., Weng, W. -H., Chen, T. Y., Williamson, D. F., Manz, T., Shady, M., and Mahmood, F., Multimodal co-attention transformer for survival prediction in gigapixel whole slide images. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 4015\u20134025, 2021. https:\/\/doi.org\/10.1109\/ICCV48922.2021.00398","DOI":"10.1109\/ICCV48922.2021.00398"},{"key":"2379_CR51","doi-asserted-by":"publisher","unstructured":"Keshvarikhojasteh, H., Aubreville, M., Bertram, C. A., Pluim, J. P., and Veta, M., Beyond accuracy: Quantifying the reliability of multiple instance learning for whole slide image classification. PloS one 20(12):0337261, 2025. https:\/\/doi.org\/10.1371\/journal.pone.0337261","DOI":"10.1371\/journal.pone.0337261"},{"key":"2379_CR52","doi-asserted-by":"publisher","unstructured":"Lopardo, G., Precioso, F., and Garreau, D., Attention meets post-hoc interpretability: A mathematical perspective, 2024. arXiv preprint arXiv:2402.03485. https:\/\/doi.org\/10.48550\/arXiv.2402.03485","DOI":"10.48550\/arXiv.2402.03485"}],"container-title":["Journal of Medical Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10916-026-02379-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10916-026-02379-0","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10916-026-02379-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,15]],"date-time":"2026-04-15T15:13:57Z","timestamp":1776266037000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10916-026-02379-0"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,4,15]]},"references-count":52,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2026,12]]}},"alternative-id":["2379"],"URL":"https:\/\/doi.org\/10.1007\/s10916-026-02379-0","relation":{},"ISSN":["1573-689X"],"issn-type":[{"value":"1573-689X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,4,15]]},"assertion":[{"value":"24 November 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"4 April 2026","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"15 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":"This article does not contain any studies with human participants or animals performed by any of the authors. The research is based solely on publicly available data and literature, and no personal or sensitive information was collected or used.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical Approval"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent to Participate Declaration"}},{"value":"The authors declare no competing interests.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}},{"value":"The authors declare that there is no conflict of interest.","order":5,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of Interest"}},{"value":"Not applicable.","order":6,"name":"Ethics","group":{"name":"EthicsHeading","label":"Clinical Trial Number"}}],"article-number":"54"}}