{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,15]],"date-time":"2025-11-15T10:33:16Z","timestamp":1763202796100,"version":"3.40.3"},"publisher-location":"Cham","reference-count":26,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031431524"},{"type":"electronic","value":"9783031431531"}],"license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"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":[[2023]]},"DOI":"10.1007\/978-3-031-43153-1_1","type":"book-chapter","created":{"date-parts":[[2023,9,4]],"date-time":"2023-09-04T21:01:55Z","timestamp":1693861315000},"page":"1-12","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Buffer-MIL: Robust Multi-instance Learning with\u00a0a\u00a0Buffer-Based Approach"],"prefix":"10.1007","author":[{"given":"Gianpaolo","family":"Bontempo","sequence":"first","affiliation":[]},{"given":"Luca","family":"Lumetti","sequence":"additional","affiliation":[]},{"given":"Angelo","family":"Porrello","sequence":"additional","affiliation":[]},{"given":"Federico","family":"Bolelli","sequence":"additional","affiliation":[]},{"given":"Simone","family":"Calderara","sequence":"additional","affiliation":[]},{"given":"Elisa","family":"Ficarra","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,9,5]]},"reference":[{"key":"1_CR1","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"39","DOI":"10.1007\/978-3-030-29888-3_4","volume-title":"Computer Analysis of Images and Patterns","author":"S Allegretti","year":"2019","unstructured":"Allegretti, S., Bolelli, F., Cancilla, M., Pollastri, F., Canalini, L., Grana, C.: How does connected components labeling with decision trees perform on GPUs? In: Vento, M., Percannella, G. (eds.) CAIP 2019. LNCS, vol. 11678, pp. 39\u201351. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-29888-3_4"},{"issue":"22","key":"1_CR2","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":"1_CR3","doi-asserted-by":"crossref","unstructured":"Bontempo, G., Porrello, A., Bolelli, F., Calderara, S., Ficarra, E.: DAS-MIL: distilling across scales for MIL classification of histological WSIs. In: Medical Image Computing and Computer Assisted Intervention - MICCAI 2023 (2023)","DOI":"10.1007\/978-3-031-43907-0_24"},{"key":"1_CR4","doi-asserted-by":"publisher","unstructured":"Bruno, P., Amoroso, R., Cornia, M., Cascianelli, S., Baraldi, L., Cucchiara, R.: Investigating bidimensional downsampling in vision transformer models. In: Sclaroff, S., Distante, C., Leo, M., Farinella, G.M., Tombari, F. (eds.) Image Analysis and Processing - ICIAP 2022, pp. 287\u2013299. Springer, Cham (2022). https:\/\/doi.org\/10.1007\/978-3-031-06430-2_24","DOI":"10.1007\/978-3-031-06430-2_24"},{"key":"1_CR5","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 (ICCV), pp. 9650\u20139660 (2021)","DOI":"10.1109\/ICCV48922.2021.00951"},{"key":"1_CR6","doi-asserted-by":"crossref","unstructured":"Chen, R.J., et al.: Scaling vision transformers to gigapixel images via hierarchical self-supervised learning. In: IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 16144\u201316155 (2022)","DOI":"10.1109\/CVPR52688.2022.01567"},{"issue":"4","key":"1_CR7","doi-asserted-by":"publisher","first-page":"757","DOI":"10.1109\/TMI.2020.3021387","volume":"41","author":"RJ Chen","year":"2020","unstructured":"Chen, R.J., et al.: Pathomic fusion: an integrated framework for fusing histopathology and genomic features for cancer diagnosis and prognosis. IEEE Trans. Med. Imaging 41(4), 757\u2013770 (2020)","journal-title":"IEEE Trans. Med. Imaging"},{"issue":"2","key":"1_CR8","doi-asserted-by":"publisher","first-page":"111","DOI":"10.3233\/AIC-210172","volume":"35","author":"M Cornia","year":"2022","unstructured":"Cornia, M., Baraldi, L., Cucchiara, R.: Explaining transformer-based image captioning models: an empirical analysis. AI Commun. 35(2), 111\u2013129 (2022)","journal-title":"AI Commun."},{"issue":"1","key":"1_CR9","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), 31\u201371 (1997)","journal-title":"Artif. Intell."},{"key":"1_CR10","doi-asserted-by":"crossref","unstructured":"Huang, J., Gretton, A., Borgwardt, K., Sch\u00f6lkopf, B., Smola, A.: Correcting sample selection bias by unlabeled data. In: Advances in Neural Information Processing Systems, vol. 19 (NIPS) (2006)","DOI":"10.7551\/mitpress\/7503.003.0080"},{"key":"1_CR11","unstructured":"Ilse, M., Tomczak, J., Welling, M.: Attention-based deep multiple instance learning. In: International Conference on Machine Learning, vol. 80, pp. 2127\u20132136. PMLR, July 2018"},{"issue":"4","key":"1_CR12","doi-asserted-by":"publisher","first-page":"1034","DOI":"10.1007\/s10278-020-00351-z","volume":"33","author":"N Kumar","year":"2020","unstructured":"Kumar, N., Gupta, R., Gupta, S.: Whole Slide Imaging (WSI) in pathology: current perspectives and future directions. J. Digit. Imaging 33(4), 1034\u20131040 (2020)","journal-title":"J. Digit. Imaging"},{"key":"1_CR13","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: IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 14318\u201314328 (2021)","DOI":"10.1109\/CVPR46437.2021.01409"},{"key":"1_CR14","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"32","DOI":"10.1007\/978-3-030-60802-6_4","volume-title":"Intelligent Computing Theories and Application","author":"M Lovino","year":"2020","unstructured":"Lovino, M., Bontempo, G., Cirrincione, G., Ficarra, E.: Multi-omics classification on kidney samples exploiting uncertainty-aware models. In: Huang, D.-S., Jo, K.-H. (eds.) ICIC 2020. LNCS, vol. 12464, pp. 32\u201342. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-60802-6_4"},{"issue":"6","key":"1_CR15","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."},{"key":"1_CR16","doi-asserted-by":"crossref","unstructured":"Maksoud, S., Zhao, K., Hobson, P., Jennings, A., Lovell, B.C.: SOS: selective objective switch for rapid immunofluorescence whole slide image classification. In: IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 3862\u20133871 (2020)","DOI":"10.1109\/CVPR42600.2020.00392"},{"key":"1_CR17","doi-asserted-by":"publisher","unstructured":"Panariello, A., Porrello, A., Calderara, S., Cucchiara, R.: Consistency-based self-supervised learning for temporal anomaly localization. In: Karlinsky, L., Michaeli, T., Nishino, K. (eds.) Computer Vision - ECCV 2022 Workshops, vol. 13805, pp. 338\u2013349. Springer, Cham (2022). https:\/\/doi.org\/10.1007\/978-3-031-25072-9_22","DOI":"10.1007\/978-3-031-25072-9_22"},{"key":"1_CR18","doi-asserted-by":"crossref","unstructured":"Ponzio, F., Urgese, G., Ficarra, E., Di Cataldo, S.: Dealing with lack of training data for convolutional neural networks: the case of digital pathology. Electronics 8(3) (2019)","DOI":"10.3390\/electronics8030256"},{"issue":"8","key":"1_CR19","doi-asserted-by":"publisher","first-page":"2035","DOI":"10.3390\/ijms20082035","volume":"20","author":"I Roberti","year":"2019","unstructured":"Roberti, I., Lovino, M., Di Cataldo, S., Ficarra, E., Urgese, G.: Exploiting gene expression profiles for the automated prediction of connectivity between brain regions. Int. J. Mol. Sci. 20(8), 2035 (2019)","journal-title":"Int. J. Mol. Sci."},{"key":"1_CR20","unstructured":"Shao, Z., et al.: TransMIL: transformer based correlated multiple instance learning for whole slide image classification. In: Advances in Neural Information Processing Systems (NeurIPS), vol. 34, pp. 2136\u20132147 (2021)"},{"issue":"2","key":"1_CR21","doi-asserted-by":"publisher","first-page":"227","DOI":"10.1016\/S0378-3758(00)00115-4","volume":"90","author":"H Shimodaira","year":"2000","unstructured":"Shimodaira, H.: Improving predictive inference under covariate shift by weighting the log-likelihood function. J. Stat. Plann. Inference 90(2), 227\u2013244 (2000)","journal-title":"J. Stat. Plann. Inference"},{"key":"1_CR22","unstructured":"Sugiyama, M., Nakajima, S., Kashima, H., Buenau, P., Kawanabe, M.: Direct importance estimation with model selection and its application to covariate shift adaptation. In: Advances in Neural Information Processing Systems (NIPS), vol. 20 (2007)"},{"key":"1_CR23","unstructured":"Tu, M., Huang, J., He, X., Zhou, B.: Multiple instance learning with graph neural networks. In: ICML Workshop on Learning and Reasoning with Graph-Structured Representations (2019)"},{"issue":"1","key":"1_CR24","doi-asserted-by":"publisher","first-page":"37","DOI":"10.1145\/3147.3165","volume":"11","author":"JS Vitter","year":"1985","unstructured":"Vitter, J.S.: Random sampling with a reservoir. ACM Trans. Math. Softw. 11(1), 37\u201357 (1985)","journal-title":"ACM Trans. Math. Softw."},{"key":"1_CR25","doi-asserted-by":"crossref","unstructured":"Zhang, J., Hu, J.: Image segmentation based on 2D Otsu method with histogram analysis. In: International Conference on Computer Science and Software Engineering, vol. 6, pp. 105\u2013108. IEEE (2008)","DOI":"10.1109\/CSSE.2008.206"},{"key":"1_CR26","unstructured":"Zhang, W., Li, J., Liu, L.: Robust multi-instance learning with stable instances. In: ECAI 2020: 24th European Conference on Artificial Intelligence (2019)"}],"container-title":["Lecture Notes in Computer Science","Image Analysis and Processing \u2013 ICIAP 2023"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-43153-1_1","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,13]],"date-time":"2024-03-13T11:32:40Z","timestamp":1710329560000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-43153-1_1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031431524","9783031431531"],"references-count":26,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-43153-1_1","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"5 September 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICIAP","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Image Analysis and Processing","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Udine","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":"2023","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"11 September 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"15 September 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"22","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"iciap2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/www.iciap2023.org\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"CMT","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"144","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"85","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"7","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"59% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"https:\/\/iciap2023.org\/satellite-event\/workshops\/","order":10,"name":"additional_info_on_review_process","label":"Additional Info on Review Process","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}