{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,24]],"date-time":"2026-07-24T16:21:52Z","timestamp":1784910112614,"version":"3.55.0"},"publisher-location":"Cham","reference-count":61,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031736674","type":"print"},{"value":"9783031736681","type":"electronic"}],"license":[{"start":{"date-parts":[[2024,12,1]],"date-time":"2024-12-01T00:00:00Z","timestamp":1733011200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,12,1]],"date-time":"2024-12-01T00:00:00Z","timestamp":1733011200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025]]},"DOI":"10.1007\/978-3-031-73668-1_8","type":"book-chapter","created":{"date-parts":[[2024,12,1]],"date-time":"2024-12-01T02:01:50Z","timestamp":1733018510000},"page":"125-143","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":50,"title":["Attention-Challenging Multiple Instance Learning for\u00a0Whole Slide Image Classification"],"prefix":"10.1007","author":[{"given":"Yunlong","family":"Zhang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Honglin","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yunxuan","family":"Sun","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sunyi","family":"Zheng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chenglu","family":"Zhu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lin","family":"Yang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,12,1]]},"reference":[{"key":"8_CR1","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."},{"issue":"8","key":"8_CR2","doi-asserted-by":"publisher","first-page":"6391","DOI":"10.1007\/s10462-021-09975-1","volume":"54","author":"MM Bejani","year":"2021","unstructured":"Bejani, M.M., Ghatee, M.: A systematic review on overfitting control in shallow and deep neural networks. Artif. Intell. Rev. 54(8), 6391\u20136438 (2021). https:\/\/doi.org\/10.1007\/s10462-021-09975-1","journal-title":"Artif. Intell. Rev."},{"issue":"22","key":"8_CR3","doi-asserted-by":"publisher","first-page":"2199","DOI":"10.1001\/jama.2017.14585","volume":"318","author":"BE Bejnordi","year":"2017","unstructured":"Bejnordi, B.E., et al.: Diagnostic assessment of deep learning algorithms for detection of lymph node metastases in women with breast cancer. JAMA 318(22), 2199\u20132210 (2017)","journal-title":"JAMA"},{"key":"8_CR4","unstructured":"Bergner, B., Lippert, C., Mahendran, A.: Iterative patch selection for high-resolution image recognition. arXiv preprint arXiv:2210.13007 (2022)"},{"key":"8_CR5","doi-asserted-by":"crossref","unstructured":"Bontempo, G., Bolelli, F., Porrello, A., Calderara, S., Ficarra, E.: A graph-based multi-scale approach with knowledge distillation for WSI classification. TMI (2023)","DOI":"10.1109\/TMI.2023.3337549"},{"key":"8_CR6","doi-asserted-by":"crossref","unstructured":"Brancati, N., et\u00a0al.: Bracs: a dataset for breast carcinoma subtyping in H &E histology images. Database 2022, baac093 (2022)","DOI":"10.1093\/database\/baac093"},{"key":"8_CR7","doi-asserted-by":"crossref","unstructured":"Campanella, G., et al.: Clinical-grade computational pathology using weakly supervised deep learning on whole slide images. Nat. Med. 25(8), 1301\u20131309 (2019)","DOI":"10.1038\/s41591-019-0508-1"},{"key":"8_CR8","doi-asserted-by":"crossref","unstructured":"Caron, M., et al.: Emerging properties in self-supervised vision transformers. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 9650\u20139660 (2021)","DOI":"10.1109\/ICCV48922.2021.00951"},{"key":"8_CR9","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: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 15661\u201315670 (2023)","DOI":"10.1109\/CVPR52729.2023.01503"},{"key":"8_CR10","doi-asserted-by":"crossref","unstructured":"Chen, R.J., et al.: Scaling vision transformers to gigapixel images via hierarchical self-supervised learning. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 16144\u201316155 (2022)","DOI":"10.1109\/CVPR52688.2022.01567"},{"key":"8_CR11","doi-asserted-by":"crossref","unstructured":"Chen, R.J., et al.: 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)","DOI":"10.1109\/ICCV48922.2021.00398"},{"key":"8_CR12","doi-asserted-by":"crossref","unstructured":"Chen, Y.C., Lu, C.S.: Rankmix: data augmentation for weakly supervised learning of classifying whole slide images with diverse sizes and imbalanced categories. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 23936\u201323945 (2023)","DOI":"10.1109\/CVPR52729.2023.02292"},{"key":"8_CR13","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"519","DOI":"10.1007\/978-3-030-59722-1_50","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2020","author":"P Chikontwe","year":"2020","unstructured":"Chikontwe, P., Kim, M., Nam, S.J., Go, H., Park, S.H.: Multiple instance learning with center embeddings for histopathology classification. In: Martel, A.L., et al. (eds.) MICCAI 2020. LNCS, vol. 12265, pp. 519\u2013528. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-59722-1_50"},{"issue":"3","key":"8_CR14","doi-asserted-by":"publisher","first-page":"152","DOI":"10.1097\/PAP.0b013e318253459e","volume":"19","author":"TC Cornish","year":"2012","unstructured":"Cornish, T.C., Swapp, R.E., Kaplan, K.J.: Whole-slide imaging: routine pathologic diagnosis. Adv. Anat. Pathol. 19(3), 152\u2013159 (2012)","journal-title":"Adv. Anat. Pathol."},{"key":"8_CR15","unstructured":"Dauphin, Y.N., Fan, A., Auli, M., Grangier, D.: Language modeling with gated convolutional networks. In: International Conference on Machine Learning, pp. 933\u2013941. PMLR (2017)"},{"key":"8_CR16","unstructured":"Dehaene, O., Camara, A., Moindrot, O., de\u00a0Lavergne, A., Courtiol, P.: Self-supervision closes the gap between weak and strong supervision in histology. arXiv preprint arXiv:2012.03583 (2020)"},{"key":"8_CR17","unstructured":"DeVries, T., Taylor, G.W.: Improved regularization of convolutional neural networks with cutout. arXiv preprint arXiv:1708.04552 (2017)"},{"issue":"1\u20132","key":"8_CR18","doi-asserted-by":"publisher","first-page":"31","DOI":"10.1016\/S0004-3702(96)00034-3","volume":"89","author":"TG Dietterich","year":"1997","unstructured":"Dietterich, T.G., Lathrop, R.H., Lozano-P\u00e9rez, T.: Solving the multiple instance problem with axis-parallel rectangles. Artif. Intell. 89(1\u20132), 31\u201371 (1997)","journal-title":"Artif. Intell."},{"issue":"11","key":"8_CR19","doi-asserted-by":"publisher","first-page":"665","DOI":"10.1038\/s42256-020-00257-z","volume":"2","author":"R Geirhos","year":"2020","unstructured":"Geirhos, R., et al.: Shortcut learning in deep neural networks. Nat. Mach. Intell. 2(11), 665\u2013673 (2020)","journal-title":"Nat. Mach. Intell."},{"key":"8_CR20","unstructured":"Geirhos, R., Rubisch, P., Michaelis, C., Bethge, M., Wichmann, F.A., Brendel, W.: Imagenet-trained CNNs are biased towards texture; increasing shape bias improves accuracy and robustness. arXiv preprint arXiv:1811.12231 (2018)"},{"key":"8_CR21","doi-asserted-by":"crossref","unstructured":"Guan, Y., et al.: Node-aligned graph convolutional network for whole-slide image representation and classification. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 18813\u201318823 (2022)","DOI":"10.1109\/CVPR52688.2022.01825"},{"key":"8_CR22","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770\u2013778 (2016)","DOI":"10.1109\/CVPR.2016.90"},{"issue":"3","key":"8_CR23","doi-asserted-by":"publisher","first-page":"538","DOI":"10.1016\/j.cmpb.2011.12.007","volume":"107","author":"L He","year":"2012","unstructured":"He, L., Long, L.R., Antani, S., Thoma, G.R.: Histology image analysis for carcinoma detection and grading. Comput. Methods Programs Biomed. 107(3), 538\u2013556 (2012)","journal-title":"Comput. Methods Programs Biomed."},{"key":"8_CR24","unstructured":"Holdenried-Krafft, S., et al.: Dual-query multiple instance learning for dynamic meta-embedding based tumor classification. arXiv preprint arXiv:2307.07482 (2023)"},{"key":"8_CR25","doi-asserted-by":"crossref","unstructured":"Hou, W., et al.: H$$\\hat{\\,}$$2-mil: exploring hierarchical representation with heterogeneous multiple instance learning for whole slide image analysis. In: AAAI, vol.\u00a036, pp. 933\u2013941 (2022)","DOI":"10.1609\/aaai.v36i1.19976"},{"key":"8_CR26","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"124","DOI":"10.1007\/978-3-030-58536-5_8","volume-title":"Computer Vision \u2013 ECCV 2020","author":"Z Huang","year":"2020","unstructured":"Huang, Z., Wang, H., Xing, E.P., Huang, D.: Self-challenging improves cross-domain generalization. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12347, pp. 124\u2013140. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-58536-5_8"},{"key":"8_CR27","unstructured":"Ilse, M., Tomczak, J., Welling, M.: Attention-based deep multiple instance learning. In: International Conference on Machine Learning, pp. 2127\u20132136. PMLR (2018)"},{"key":"8_CR28","doi-asserted-by":"crossref","unstructured":"Kang, M., Song, H., Park, S., Yoo, D., Pereira, S.: Benchmarking self-supervised learning on diverse pathology datasets. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 3344\u20133354 (2023)","DOI":"10.1109\/CVPR52729.2023.00326"},{"key":"8_CR29","doi-asserted-by":"crossref","unstructured":"Kong, F., Henao, R.: Efficient classification of very large images with tiny objects. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 2384\u20132394 (2022)","DOI":"10.1109\/CVPR52688.2022.00242"},{"key":"8_CR30","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)","DOI":"10.1109\/CVPR46437.2021.01409"},{"key":"8_CR31","doi-asserted-by":"crossref","unstructured":"Li, H., et al.: Task-specific fine-tuning via variational information bottleneck for weakly-supervised pathology whole slide image classification. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 7454\u20137463 (2023)","DOI":"10.1109\/CVPR52729.2023.00720"},{"key":"8_CR32","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"174","DOI":"10.1007\/978-3-030-00934-2_20","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2018","author":"R Li","year":"2018","unstructured":"Li, R., Yao, J., Zhu, X., Li, Y., Huang, J.: Graph CNN for survival analysis on whole slide pathological images. In: Frangi, A.F., Schnabel, J.A., Davatzikos, C., Alberola-L\u00f3pez, C., Fichtinger, G. (eds.) MICCAI 2018. LNCS, vol. 11071, pp. 174\u2013182. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-00934-2_20"},{"key":"8_CR33","unstructured":"Li, Y., Ping, W.: Cancer metastasis detection with neural conditional random field. arXiv preprint arXiv:1806.07064 (2018)"},{"key":"8_CR34","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: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 19830\u201319839 (2023)","DOI":"10.1109\/CVPR52729.2023.01899"},{"key":"8_CR35","doi-asserted-by":"crossref","unstructured":"Litjens, G., et al.: Deep learning as a tool for increased accuracy and efficiency of histopathological diagnosis. Sci. Rep. 6(1), 26286 (2016)","DOI":"10.1038\/srep26286"},{"issue":"6","key":"8_CR36","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., Williamson, D.F., Chen, T.Y., Chen, R.J., Barbieri, M., Mahmood, F.: Data-efficient and weakly supervised computational pathology on whole-slide images. Nat. Biomed. Eng. 5(6), 555\u2013570 (2021)","journal-title":"Nat. Biomed. Eng."},{"issue":"1","key":"8_CR37","doi-asserted-by":"publisher","first-page":"7","DOI":"10.2217\/iim.09.9","volume":"1","author":"A Madabhushi","year":"2009","unstructured":"Madabhushi, A.: Digital pathology image analysis: opportunities and challenges. Imaging Med. 1(1), 7 (2009)","journal-title":"Imaging Med."},{"key":"8_CR38","unstructured":"Maron, O., Lozano-P\u00e9rez, T.: A framework for multiple-instance learning. In: Advances in Neural Information Processing Systems, vol. 10 (1997)"},{"key":"8_CR39","doi-asserted-by":"crossref","unstructured":"McInnes, L., Healy, J., Melville, J.: Umap: uniform manifold approximation and projection for dimension reduction. arXiv preprint arXiv:1802.03426 (2018)","DOI":"10.21105\/joss.00861"},{"issue":"1","key":"8_CR40","doi-asserted-by":"publisher","first-page":"36","DOI":"10.4103\/2153-3539.83746","volume":"2","author":"L Pantanowitz","year":"2011","unstructured":"Pantanowitz, L., et al.: Review of the current state of whole slide imaging in pathology. J. Pathol. Inform. 2(1), 36 (2011)","journal-title":"J. Pathol. Inform."},{"issue":"3","key":"8_CR41","doi-asserted-by":"publisher","first-page":"1581","DOI":"10.1109\/TPAMI.2020.3019563","volume":"44","author":"H Pinckaers","year":"2020","unstructured":"Pinckaers, H., Van Ginneken, B., Litjens, G.: Streaming convolutional neural networks for end-to-end learning with multi-megapixel images. IEEE Trans. Pattern Anal. Mach. Intell. 44(3), 1581\u20131590 (2020)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"8_CR42","first-page":"15368","volume":"35","author":"L Qu","year":"2022","unstructured":"Qu, L., Wang, M., Song, Z., et al.: Bi-directional weakly supervised knowledge distillation for whole slide image classification. Neurips 35, 15368\u201315381 (2022)","journal-title":"Neurips"},{"key":"8_CR43","doi-asserted-by":"crossref","unstructured":"Qu, L., et al.: Boosting whole slide image classification from the perspectives of distribution, correlation and magnification. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 21463\u201321473 (2023)","DOI":"10.1109\/ICCV51070.2023.01962"},{"key":"8_CR44","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. Adv. Neural. Inf. Process. Syst. 34, 2136\u20132147 (2021)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"issue":"1","key":"8_CR45","first-page":"1929","volume":"15","author":"N Srivastava","year":"2014","unstructured":"Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: a simple way to prevent neural networks from overfitting. J. Mach. Learn. Res. 15(1), 1929\u20131958 (2014)","journal-title":"J. Mach. Learn. Res."},{"key":"8_CR46","doi-asserted-by":"crossref","unstructured":"Tang, W., Huang, S., Zhang, X., Zhou, F., Zhang, Y., Liu, B.: Multiple instance learning framework with masked hard instance mining for whole slide image classification. arXiv preprint arXiv:2307.15254 (2023)","DOI":"10.1109\/ICCV51070.2023.00377"},{"issue":"2","key":"8_CR47","doi-asserted-by":"publisher","first-page":"567","DOI":"10.1109\/TPAMI.2019.2936841","volume":"43","author":"D Tellez","year":"2019","unstructured":"Tellez, D., Litjens, G., van der Laak, J., Ciompi, F.: Neural image compression for gigapixel histopathology image analysis. IEEE Trans. Pattern Anal. Mach. Intell. 43(2), 567\u2013578 (2019)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"8_CR48","unstructured":"Tiwari, R., Shenoy, P.: Overcoming simplicity bias in deep networks using a feature sieve (2023)"},{"key":"8_CR49","unstructured":"Wang, D., Khosla, A., Gargeya, R., Irshad, H., Beck, A.H.: Deep learning for identifying metastatic breast cancer. arXiv preprint arXiv:1606.05718 (2016)"},{"key":"8_CR50","doi-asserted-by":"crossref","unstructured":"Wang, H., et al.: Iteratively coupled multiple instance learning from instance to bag classifier for whole slide image classification. arXiv preprint arXiv:2303.15749 (2023)","DOI":"10.1007\/978-3-031-43987-2_45"},{"key":"8_CR51","first-page":"18009","volume":"35","author":"X Wang","year":"2022","unstructured":"Wang, X., et al.: SCL-WC: cross-slide contrastive learning for weakly-supervised whole-slide image classification. Adv. Neural. Inf. Process. Syst. 35, 18009\u201318021 (2022)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"issue":"19","key":"8_CR52","doi-asserted-by":"publisher","first-page":"5115","DOI":"10.1158\/0008-5472.CAN-21-0482","volume":"81","author":"Y Wang","year":"2021","unstructured":"Wang, Y., et al.: Predicting molecular phenotypes from histopathology images: a transcriptome-wide expression-morphology analysis in breast cancer. Can. Res. 81(19), 5115\u20135126 (2021)","journal-title":"Can. Res."},{"key":"8_CR53","doi-asserted-by":"crossref","unstructured":"Xiong, C., Chen, H., Sung, J., King, I.: Diagnose like a pathologist: transformer-enabled hierarchical attention-guided multiple instance learning for whole slide image classification. arXiv preprint arXiv:2301.08125 (2023)","DOI":"10.24963\/ijcai.2023\/176"},{"key":"8_CR54","series-title":"LNCS","doi-asserted-by":"publisher","first-page":"35","DOI":"10.1007\/978-3-031-16434-7_4","volume-title":"MICCAI 2022","author":"J Yang","year":"2022","unstructured":"Yang, J., et al.: Remix: a general and efficient framework for multiple instance learning based whole slide image classification. In: Wang, L., Dou, Q., Fletcher, P.T., Speidel, S., Li, S. (eds.) MICCAI 2022. LNCS, vol. 13432, pp. 35\u201345. Springer, Cham (2022). https:\/\/doi.org\/10.1007\/978-3-031-16434-7_4"},{"key":"8_CR55","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2020.101789","volume":"65","author":"J Yao","year":"2020","unstructured":"Yao, J., Zhu, X., Jonnagaddala, J., Hawkins, N., Huang, J.: Whole slide images based cancer survival prediction using attention guided deep multiple instance learning networks. Med. Image Anal. 65, 101789 (2020)","journal-title":"Med. Image Anal."},{"key":"8_CR56","unstructured":"Yufei, C., et al.: Bayes-mil: a new probabilistic perspective on attention-based multiple instance learning for whole slide images. In: The Eleventh International Conference on Learning Representations (2022)"},{"key":"8_CR57","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: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 18802\u201318812 (2022)","DOI":"10.1109\/CVPR52688.2022.01824"},{"key":"8_CR58","series-title":"LNCS","doi-asserted-by":"publisher","first-page":"242","DOI":"10.1007\/978-3-031-16434-7_24","volume-title":"MICCAI 2022","author":"Y Zhang","year":"2022","unstructured":"Zhang, Y., Sun, Y., Li, H., Zheng, S., Zhu, C., Yang, L.: Benchmarking the robustness of deep neural networks to common corruptions in digital pathology. In: Wang, L., Dou, Q., Fletcher, P.T., Speidel, S., Li, S. (eds.) MICCAI 2022. LNCS, vol. 13432, pp. 242\u2013252. Springer, Cham (2022). https:\/\/doi.org\/10.1007\/978-3-031-16434-7_24"},{"key":"8_CR59","doi-asserted-by":"crossref","unstructured":"Zhao, Y., et\u00a0al.: Predicting lymph node metastasis using histopathological images based on multiple instance learning with deep graph convolution. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 4837\u20134846 (2020)","DOI":"10.1109\/CVPR42600.2020.00489"},{"key":"8_CR60","doi-asserted-by":"crossref","unstructured":"Zhong, Z., Zheng, L., Kang, G., Li, S., Yang, Y.: Random erasing data augmentation. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol.\u00a034, pp. 13001\u201313008 (2020)","DOI":"10.1609\/aaai.v34i07.7000"},{"key":"8_CR61","doi-asserted-by":"crossref","unstructured":"Zhu, X., Yao, J., Zhu, F., Huang, J.: Wsisa: making survival prediction from whole slide histopathological images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 7234\u20137242 (2017)","DOI":"10.1109\/CVPR.2017.725"}],"container-title":["Lecture Notes in Computer Science","Computer Vision \u2013 ECCV 2024"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-73668-1_8","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,12,1]],"date-time":"2024-12-01T02:07:03Z","timestamp":1733018823000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-73668-1_8"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,12,1]]},"ISBN":["9783031736674","9783031736681"],"references-count":61,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-73668-1_8","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,12,1]]},"assertion":[{"value":"1 December 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ECCV","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"European Conference on Computer Vision","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Milan","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Italy","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2024","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"29 September 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"4 October 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"18","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"eccv2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/eccv2024.ecva.net\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}