{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,11]],"date-time":"2026-03-11T07:43:29Z","timestamp":1773215009959,"version":"3.50.1"},"reference-count":49,"publisher":"Springer Science and Business Media LLC","issue":"8","license":[{"start":{"date-parts":[[2023,11,1]],"date-time":"2023-11-01T00:00:00Z","timestamp":1698796800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,11,1]],"date-time":"2023-11-01T00:00:00Z","timestamp":1698796800000},"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":["Vis Comput"],"published-print":{"date-parts":[[2024,8]]},"DOI":"10.1007\/s00371-023-03131-2","type":"journal-article","created":{"date-parts":[[2023,11,1]],"date-time":"2023-11-01T20:01:47Z","timestamp":1698868907000},"page":"5717-5732","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Multiple instance learning-based two-stage metric learning network for whole slide image classification"],"prefix":"10.1007","volume":"40","author":[{"given":"Xiaoyu","family":"Li","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5418-4314","authenticated-orcid":false,"given":"Bei","family":"Yang","sequence":"additional","affiliation":[]},{"given":"Tiandong","family":"Chen","sequence":"additional","affiliation":[]},{"given":"Zheng","family":"Gao","sequence":"additional","affiliation":[]},{"given":"Huijie","family":"Li","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,11,1]]},"reference":[{"key":"3131_CR1","unstructured":"biennial Report 2020\u20132021. Technical report, International Agency for Research on Cancer., Lyon, France. https:\/\/publications.iarc.fr\/607"},{"issue":"3","key":"3131_CR2","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. Anatom. Pathol. 19(3), 152\u2013159 (2012). https:\/\/doi.org\/10.1097\/PAP.0b013e318253459e","journal-title":"Adv. Anatom. Pathol."},{"key":"3131_CR3","doi-asserted-by":"publisher","first-page":"128613","DOI":"10.1109\/ACCESS.2020.3008868","volume":"8","author":"L Duran-Lopez","year":"2020","unstructured":"Duran-Lopez, L., Dominguez-Morales, J.P., Conde-Martin, A.F., Vicente-Diaz, S., Linares-Barranco, A.: PROMETEO: A CNN-based computer-aided diagnosis system for WSI prostate cancer detection. IEEE Access 8, 128613\u2013128628 (2020). https:\/\/doi.org\/10.1109\/ACCESS.2020.3008868","journal-title":"IEEE Access"},{"issue":"6","key":"3131_CR4","doi-asserted-by":"publisher","first-page":"4809","DOI":"10.1007\/s10462-021-10121-0","volume":"55","author":"X Li","year":"2022","unstructured":"Li, X., Li, C., Rahaman, M.M., Sun, H., Li, X., Wu, J., Yao, Y., Grzegorzek, M.: A comprehensive review of computer-aided whole-slide image analysis: from datasets to feature extraction, segmentation, classification and detection approaches. Artif. Intell. Rev. 55(6), 4809\u20134878 (2022). https:\/\/doi.org\/10.1007\/s10462-021-10121-0","journal-title":"Artif. Intell. Rev."},{"issue":"3","key":"3131_CR5","doi-asserted-by":"publisher","first-page":"1581","DOI":"10.53555\/sfs.v10i1.1214","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). https:\/\/doi.org\/10.53555\/sfs.v10i1.1214","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"3131_CR6","doi-asserted-by":"crossref","unstructured":"Xie, X., Cheng, G., Wang, J., Yao, X., Han, J.: Oriented R-CNN for object detection. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 3520\u20133529 (2021)","DOI":"10.1109\/ICCV48922.2021.00350"},{"key":"3131_CR7","doi-asserted-by":"publisher","unstructured":"Khan, S.S., Sengupta, D., Ghosh, A., Chaudhuri, A.: MTCNN++: a CNN-based face detection algorithm inspired by MTCNN. The Visual Computer, pp. 1\u201319, Springer: Berlin (2023). https:\/\/doi.org\/10.1007\/s00371-023-02822-0","DOI":"10.1007\/s00371-023-02822-0"},{"key":"3131_CR8","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."},{"key":"3131_CR9","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2020.101813","volume":"67","author":"CL Srinidhi","year":"2021","unstructured":"Srinidhi, C.L., Ciga, O., Martel, A.L.: Deep neural network models for computational histopathology: a survey. Med. Image Anal. 67, 101813 (2021). https:\/\/doi.org\/10.1016\/j.media.2020.101813","journal-title":"Med. Image Anal."},{"key":"3131_CR10","doi-asserted-by":"crossref","unstructured":"Lu, M.Y., Chen, R.J., Wang, J., Dillon, D., Mahmood, F.: Semi-supervised histology classification using deep multiple instance learning and contrastive predictive coding. arXiv preprint arXiv:1910.10825 (2019)","DOI":"10.1117\/12.2549627"},{"key":"3131_CR11","doi-asserted-by":"crossref","unstructured":"Meng, Y., Zhang, H., Zhao, Y., Yang, X., Qian, X., Huang, X., Zheng, Y.: Spatial uncertainty-aware semi-supervised crowd counting. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 15549\u201315559 (2021)","DOI":"10.1109\/ICCV48922.2021.01526"},{"issue":"9","key":"3131_CR12","doi-asserted-by":"publisher","first-page":"3950","DOI":"10.1109\/TCYB.2019.2935141","volume":"50","author":"X Wang","year":"2019","unstructured":"Wang, X., Chen, H., Gan, C., Lin, H., Dou, Q., Tsougenis, E., Huang, Q., Cai, M., Heng, P.-A.: Weakly supervised deep learning for whole slide lung cancer image analysis. IEEE Trans. Cybern. 50(9), 3950\u20133962 (2019). https:\/\/doi.org\/10.1109\/TCYB.2019.2935141","journal-title":"IEEE Trans. Cybern."},{"issue":"1","key":"3131_CR13","doi-asserted-by":"publisher","first-page":"6111","DOI":"10.1038\/s41598-022-09985-1","volume":"12","author":"W-W Hsu","year":"2022","unstructured":"Hsu, W.-W., Guo, J.-M., Pei, L., Chiang, L.-A., Li, Y.-F., Hsiao, J.-C., Colen, R., Liu, P.: A weakly supervised deep learning-based method for glioma subtype classification using WSI and mpMRIs. Sci. Rep. 12(1), 6111 (2022). https:\/\/doi.org\/10.1038\/s41598-022-09985-1","journal-title":"Sci. Rep."},{"issue":"1","key":"3131_CR14","doi-asserted-by":"publisher","first-page":"9297","DOI":"10.1038\/s41598-020-66333-x","volume":"10","author":"F Kanavati","year":"2020","unstructured":"Kanavati, F., Toyokawa, G., Momosaki, S., Rambeau, M., Kozuma, Y., Shoji, F., Yamazaki, K., Takeo, S., Iizuka, O., Tsuneki, M.: Weakly-supervised learning for lung carcinoma classification using deep learning. Sci. Rep. 10(1), 9297 (2020). https:\/\/doi.org\/10.1038\/s41598-020-66333-x","journal-title":"Sci. Rep."},{"key":"3131_CR15","doi-asserted-by":"publisher","first-page":"101861","DOI":"10.1016\/j.compmedimag.2021.101861","volume":"88","author":"C Zhou","year":"2021","unstructured":"Zhou, C., Jin, Y., Chen, Y., Huang, S., Huang, R., Wang, Y., Zhao, Y., Chen, Y., Guo, L., Liao, J.: Histopathology classification and localization of colorectal cancer using global labels by weakly supervised deep learning. Comput. Med. Imaging Graphi. 88, 101861 (2021). https:\/\/doi.org\/10.1016\/j.compmedimag.2021.101861","journal-title":"Comput. Med. Imaging Graphi."},{"key":"3131_CR16","doi-asserted-by":"publisher","unstructured":"Chen, H., Han, X., Fan, X., Lou, X., Liu, H., Huang, J., Yao, J.: Rectified cross-entropy and upper transition loss for weakly supervised whole slide image classifier. In: Medical Image Computing and Computer Assisted Intervention\u2013MICCAI 2019: 22nd International Conference, Shenzhen, China, October 13\u201317, 2019, Proceedings, Part I 22, pp. 351\u2013359 (2019). https:\/\/doi.org\/10.1007\/978-3-030-32239-7_39","DOI":"10.1007\/978-3-030-32239-7_39"},{"key":"3131_CR17","doi-asserted-by":"publisher","first-page":"102407","DOI":"10.1016\/j.artmed.2022.102407","volume":"133","author":"AB Hamida","year":"2022","unstructured":"Hamida, A.B., Devanne, M., Weber, J., Truntzer, C., Derang\u00e9re, V., Ghiringhelli, F., Forestier, G., Wemmert, C.: Weakly Supervised Learning using Attention gates for colon cancer histopathological image segmentation. Artif. icial Intell. Med. 133, 102407 (2022)","journal-title":"Artif. icial Intell. Med."},{"key":"3131_CR18","doi-asserted-by":"publisher","unstructured":"Chikontwe, P., Kim, M., Nam, S.J., Go, H., Park, S.H.: Multiple instance learning with center embeddings for histopathology classification. In: Medical Image Computing and Computer Assisted Intervention\u2013MICCAI 2020: 23rd International Conference, Lima, Peru, October 4\u20138, 2020, Proceedings, Part V 23, pp. 519\u2013528 (2020). https:\/\/doi.org\/10.1007\/978-3-030-59722-1_50","DOI":"10.1007\/978-3-030-59722-1_50"},{"key":"3131_CR19","doi-asserted-by":"publisher","unstructured":"Cruz-Roa, A., Basavanhally, A., Gonzalez, F., Gilmore, H., Feldman, M., Ganesan, S., Shih, N., Tomaszewski, J., Madabhushi, A.: Automatic detection of invasive ductal carcinoma in whole slide images with convolutional neural networks. In: Medical Imaging 2014: Digital Pathology, vol. 9041, p. 904103 (2014). https:\/\/doi.org\/10.1117\/12.2043872","DOI":"10.1117\/12.2043872"},{"key":"3131_CR20","unstructured":"Sharma, Y., Shrivastava, A., Ehsan, L., Moskaluk, C.A., Syed, S., Brown, D.: Cluster-to-conquer: A framework for end-to-end multi-instance learning for whole slide image classification. In: Medical Imaging with Deep Learning, pp. 682\u2013698 (2021)"},{"key":"3131_CR21","doi-asserted-by":"publisher","first-page":"101789","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). https:\/\/doi.org\/10.1016\/j.media.2020.101789","journal-title":"Med. Image Anal."},{"issue":"8","key":"3131_CR22","doi-asserted-by":"publisher","first-page":"1301","DOI":"10.1038\/s41591-019-0508-1","volume":"25","author":"G Campanella","year":"2019","unstructured":"Campanella, G., Hanna, M.G., Geneslaw, L., Miraflor, A., Werneck Krauss Silva, V., Busam, K.J., Brogi, E., Reuter, V.E., Klimstra, D.S., Fuchs, T.J.: Clinical grade computational pathology using weakly supervised deep learning on whole slide images. Nat. Med. 25(8), 1301\u20131309 (2019). https:\/\/doi.org\/10.1038\/s41591-019-0508-1","journal-title":"Nat. Med."},{"key":"3131_CR23","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.: Node-aligned graph convolutional network for wholeslide 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":"3131_CR24","doi-asserted-by":"publisher","unstructured":"Lerousseau, M., Vakalopoulou, M., Classe, M., Adam, J., Battistella, E., Carr\u00e9, A., Estienne, T., Henry, T., Deutsch, E., Paragios, N.: Weakly supervised multiple instance learning histopathological tumor segmentation. In: Medical Image Computing and Computer Assisted Intervention\u2013MICCAI 2020: 23rd International Conference, Lima, Peru, October 4\u20138, 2020, Proceedings, Part V 23, pp. 470\u2013479 (2020). https:\/\/doi.org\/10.1007\/978-3-030-59722-1_45","DOI":"10.1007\/978-3-030-59722-1_45"},{"issue":"8","key":"3131_CR25","doi-asserted-by":"publisher","first-page":"2751","DOI":"10.1007\/s00371-021-02153-y","volume":"38","author":"N Ahmad","year":"2022","unstructured":"Ahmad, N., Asghar, S., Gillani, S.A.: Transfer learning-assisted multi-resolution breast cancer histopathological images classification. Visual Comput. 38(8), 2751\u20132770 (2022). https:\/\/doi.org\/10.1007\/s00371-021-02153-y","journal-title":"Visual Comput."},{"key":"3131_CR26","doi-asserted-by":"publisher","first-page":"15","DOI":"10.1016\/j.patcog.2017.08.026","volume":"74","author":"X Wang","year":"2018","unstructured":"Wang, X., Yan, Y., Tang, P., Bai, X., Liu, W.: Revisiting multiple instance neural networks. Pattern Recogn. 74, 15\u201324 (2018). https:\/\/doi.org\/10.1016\/j.patcog.2017.08.026","journal-title":"Pattern Recogn."},{"key":"3131_CR27","doi-asserted-by":"crossref","unstructured":"Zhang, H., Meng, Y., Zhao, Y., Qiao, Y., Yang, X., Coupland, S.E., Zheng, Y.: 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":"3131_CR28","unstructured":"Ilse, M., Tomczak, J., Welling, M.: Attention-based deep multiple instance learning. In: International Conference on Machine Learning, pp. 2127\u20132136 (2018)"},{"key":"3131_CR29","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":"3131_CR30","unstructured":"Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, t., Polosukhin, I.: Attention is all you need. In: Advances in neural information processing systems vol 30 (2017)"},{"issue":"6","key":"3131_CR31","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). https:\/\/doi.org\/10.1038\/s41551-020-00682-w","journal-title":"Nat. Biomed. Eng."},{"key":"3131_CR32","doi-asserted-by":"publisher","first-page":"102559","DOI":"10.1016\/j.media.2022.102559","volume":"81","author":"X Wang","year":"2022","unstructured":"Wang, X., Yang, S., Zhang, J., Wang, M., Zhang, J., Yang, W., Huang, J., Han, X.: Transformer-based unsupervised contrastive learning for histopathological image classification. Med. Image Anal. 81, 102559 (2022)","journal-title":"Med. Image Anal."},{"issue":"1","key":"3131_CR33","doi-asserted-by":"publisher","first-page":"50","DOI":"10.1016\/j.media.2022.102559","volume":"14","author":"T Cover","year":"1968","unstructured":"Cover, T.: Estimation by the nearest neighbor rule. IEEE Trans. Inf. Theory 14(1), 50\u201355 (1968). https:\/\/doi.org\/10.1016\/j.media.2022.102559","journal-title":"IEEE Trans. Inf. Theory"},{"issue":"1","key":"3131_CR34","doi-asserted-by":"publisher","first-page":"100","DOI":"10.2307\/2346830","volume":"28","author":"JA Hartigan","year":"1979","unstructured":"Hartigan, J.A., Wong, M.A., et al.: A k-means clustering algorithm. Appl. Stat. 28(1), 100\u2013108 (1979). https:\/\/doi.org\/10.2307\/2346830","journal-title":"Appl. Stat."},{"issue":"6","key":"3131_CR35","doi-asserted-by":"publisher","first-page":"1219","DOI":"10.1007\/s00371-019-01730-6","volume":"36","author":"S Tian","year":"2020","unstructured":"Tian, S., Shen, S., Tian, G., Liu, X., Yin, B.: End-to-end deep metric network for visual tracking. Vis. Comput. 36(6), 1219\u20131232 (2020). https:\/\/doi.org\/10.1007\/s00371-019-01730-6","journal-title":"Vis. Comput."},{"issue":"12","key":"3131_CR36","doi-asserted-by":"publisher","first-page":"4083","DOI":"10.1007\/s00371-022-02666-0","volume":"38","author":"T Amemiya","year":"2022","unstructured":"Amemiya, T., Leow, C.S., Buayai, P., Makino, K., Mao, X., Nishizaki, H.: Appropriate grape color estimation based on metric learning for judging harvest timing. Visual Comput. 38(12), 4083\u20134094 (2022). https:\/\/doi.org\/10.1007\/s00371-022-02666-0","journal-title":"Visual Comput."},{"key":"3131_CR37","doi-asserted-by":"crossref","unstructured":"Liu, W., Wen, Y., Yu, Z., Li, M., Raj, B., Song, L.: Sphereface: deep hypersphere embedding for face recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 212\u2013220 (2017)","DOI":"10.1109\/CVPR.2017.713"},{"key":"3131_CR38","doi-asserted-by":"crossref","unstructured":"Deng, J., Guo, J., Xue, N., Zafeiriou, S.: Arcface: Additive angular margin loss for deep face recognition. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 4690\u20134699 (2019)","DOI":"10.1109\/CVPR.2019.00482"},{"key":"3131_CR39","doi-asserted-by":"crossref","unstructured":"Sun, Y., Cheng, C., Zhang, Y., Zhang, C., Zheng, L., Wang, Z., Wei, Y.: Circle loss: a unified perspective of pair similarity optimization. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 6398\u20136407 (2020)","DOI":"10.1109\/CVPR42600.2020.00643"},{"key":"3131_CR40","doi-asserted-by":"crossref","unstructured":"Wang, H., Wang, Y., Zhou, Z., Ji, X., Gong, D., Zhou, J., Li, Z., Liu, W.: Cosface: large margin cosine loss for deep face recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5265\u20135274 (2018)","DOI":"10.1109\/CVPR.2018.00552"},{"key":"3131_CR41","doi-asserted-by":"crossref","unstructured":"Schroff, F., Kalenichenko, D., Philbin, J.: Facenet: A unified embedding for face recognition and clustering. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 815\u2013823 (2015)","DOI":"10.1109\/CVPR.2015.7298682"},{"key":"3131_CR42","doi-asserted-by":"publisher","unstructured":"Hoffer, E., Ailon, N.: Deep metric learning using triplet network. In: SimilarityBased Pattern Recognition: Third International Workshop, SIMBAD 2015, Copenhagen, Denmark, October 12\u201314, 2015. Proceedings 3, pp. 84\u201392 (2015). https:\/\/doi.org\/10.1007\/978-3-319-24261-3_7","DOI":"10.1007\/978-3-319-24261-3_7"},{"key":"3131_CR43","doi-asserted-by":"publisher","unstructured":"Gao, Y., Liu, W., Arjun, S., Zhu, L., Ratner, V., Kurc, T., Saltz, J., Tannenbaum, A.: Multi-scale learning based segmentation of glands in digital colonrectal pathology images. In: Medical Imaging 2016: Digital Pathology, vol. 9791, pp. 175\u2013180 (2016). https:\/\/doi.org\/10.1117\/12.2216790","DOI":"10.1117\/12.2216790"},{"key":"3131_CR44","doi-asserted-by":"crossref","unstructured":"Tokunaga, H., Teramoto, Y., Yoshizawa, A., Bise, R.: Adaptive weighting multifield-of-view CNN for semantic segmentation in pathology. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 12597\u201312606 (2019)","DOI":"10.1109\/CVPR.2019.01288"},{"issue":"7861","key":"3131_CR45","doi-asserted-by":"publisher","first-page":"106","DOI":"10.1038\/s41586-021-03512-4","volume":"594","author":"MY Lu","year":"2021","unstructured":"Lu, M.Y., Chen, T.Y., Williamson, D.F., Zhao, M., Shady, M., Lipkova, J., Mahmood, F.: AI-based pathology predicts origins for cancers of unknown primary. Nature 594(7861), 106\u2013110 (2021). https:\/\/doi.org\/10.1038\/s41586-021-03512-4","journal-title":"Nature"},{"key":"3131_CR46","doi-asserted-by":"publisher","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Identity mappings in deep residual networks. In: Computer Vision\u2013ECCV 2016: 14th European Conference, Amsterdam, The Netherlands, October 11\u201314, 2016, Proceedings, Part IV 14, pp. 630\u2013645 (2016). https:\/\/doi.org\/10.1007\/978-3-319-46493-0_38","DOI":"10.1007\/978-3-319-46493-0_38"},{"key":"3131_CR47","doi-asserted-by":"publisher","first-page":"211","DOI":"10.1007\/s11263-015-0816-y","volume":"115","author":"O Russakovsky","year":"2015","unstructured":"Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., et al.: Imagenet large scale visual recognition challenge. Int. J. Comput. Vision 115, 211\u2013252 (2015). https:\/\/doi.org\/10.1007\/s11263-015-0816-y","journal-title":"Int. J. Comput. Vision"},{"issue":"1","key":"3131_CR48","doi-asserted-by":"publisher","first-page":"62","DOI":"10.1109\/TSMC.1979.4310076","volume":"9","author":"N Otsu","year":"1979","unstructured":"Otsu, N.: A threshold selection method from gray-level histograms. IEEE Trans. Syst. Man Cybern. 9(1), 62\u201366 (1979)","journal-title":"IEEE Trans. Syst. Man Cybern."},{"issue":"1","key":"3131_CR49","doi-asserted-by":"publisher","first-page":"44","DOI":"10.1038\/s41379-021-00911-w","volume":"35","author":"S Farahmand","year":"2022","unstructured":"Farahmand, S., Fernandez, A.I., Ahmed, F.S., Rimm, D.L., Chuang, J.H., Reisenbichler, E., Zarringhalam, K.: Deep learning trained on hematoxylin and eosin tumor region of Interest predicts HER2 status and trastuzumab treatment response in HER2+ breast cancer. Mod. Pathol. 35(1), 44\u201351 (2022). https:\/\/doi.org\/10.1038\/s41379-021-00911-w","journal-title":"Mod. Pathol."}],"container-title":["The Visual Computer"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00371-023-03131-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00371-023-03131-2\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00371-023-03131-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,7,24]],"date-time":"2024-07-24T13:34:46Z","timestamp":1721828086000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00371-023-03131-2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,11,1]]},"references-count":49,"journal-issue":{"issue":"8","published-print":{"date-parts":[[2024,8]]}},"alternative-id":["3131"],"URL":"https:\/\/doi.org\/10.1007\/s00371-023-03131-2","relation":{},"ISSN":["0178-2789","1432-2315"],"issn-type":[{"value":"0178-2789","type":"print"},{"value":"1432-2315","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,11,1]]},"assertion":[{"value":"29 September 2023","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"1 November 2023","order":2,"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 that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}