{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,27]],"date-time":"2025-03-27T07:29:07Z","timestamp":1743060547082,"version":"3.40.3"},"publisher-location":"Cham","reference-count":27,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030594152"},{"type":"electronic","value":"9783030594169"}],"license":[{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2020]]},"DOI":"10.1007\/978-3-030-59416-9_7","type":"book-chapter","created":{"date-parts":[[2020,9,21]],"date-time":"2020-09-21T16:08:14Z","timestamp":1600704494000},"page":"107-124","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Instance Explainable Multi-instance Learning for ROI of Various Data"],"prefix":"10.1007","author":[{"given":"Xu","family":"Zhao","sequence":"first","affiliation":[]},{"given":"Zihao","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Yong","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Chunxiao","family":"Xing","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2020,9,22]]},"reference":[{"key":"7_CR1","unstructured":"Andrews, S., Tsochantaridis, I., Hofmann, T.: Support vector machines for multiple-instance learning. In: NIPS, pp. 561\u2013568 (2002)"},{"issue":"1","key":"7_CR2","first-page":"105","volume":"17","author":"J Askel\u00f6f","year":"2002","unstructured":"Askel\u00f6f, J., Carlander, M.L., Christopoulos, C.: Region of interest coding in JPEG 2000. Sig. Process.: Image Commun. 17(1), 105\u2013111 (2002)","journal-title":"Sig. Process.: Image Commun."},{"key":"7_CR3","unstructured":"Brett, M., Anton, J.L., Valabregue, R., Poline, J.B., et al.: Region of interest analysis using an SPM toolbox. In: 8th International Conference on Functional Mapping of the Human Brain, vol. 16, p. 497. Sendai, Japan (2002)"},{"issue":"1","key":"7_CR4","doi-asserted-by":"publisher","first-page":"264","DOI":"10.1016\/j.patcog.2014.07.022","volume":"48","author":"V Cheplygina","year":"2015","unstructured":"Cheplygina, V., Tax, D.M.J., Loog, M.: Multiple instance learning with bag dissimilarities. Pattern Recogn. 48(1), 264\u2013275 (2015)","journal-title":"Pattern Recogn."},{"issue":"1\u20132","key":"7_CR5","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."},{"key":"7_CR6","doi-asserted-by":"crossref","unstructured":"Feng, J., Zhou, Z.: Deep MIML network. In: AAAI, pp. 1884\u20131890 (2017)","DOI":"10.1609\/aaai.v31i1.10890"},{"issue":"1","key":"7_CR7","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1017\/S026988890999035X","volume":"25","author":"J Foulds","year":"2010","unstructured":"Foulds, J., Frank, E.: A review of multi-instance learning assumptions. Knowl. Eng. Rev. 25(1), 1\u201325 (2010)","journal-title":"Knowl. Eng. Rev."},{"key":"7_CR8","unstructured":"G\u00e4rtner, T., Flach, P.A., Kowalczyk, A., Smola, A.J.: Multi-instance kernels. In: ICML, pp. 179\u2013186 (2002)"},{"key":"7_CR9","unstructured":"Ilse, M., Tomczak, J.M., Welling, M.: Attention-based deep multiple instance learning. In: ICML, pp. 2132\u20132141 (2018)"},{"key":"7_CR10","unstructured":"Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) ICLR (2015). http:\/\/arxiv.org\/abs\/1412.6980"},{"issue":"12","key":"7_CR11","doi-asserted-by":"publisher","first-page":"52","DOI":"10.1093\/bioinformatics\/btw252","volume":"32","author":"OZ Kraus","year":"2016","unstructured":"Kraus, O.Z., Ba, L.J., Frey, B.J.: Classifying and segmenting microscopy images with deep multiple instance learning. Bioinformatics 32(12), 52\u201359 (2016)","journal-title":"Bioinformatics"},{"key":"7_CR12","doi-asserted-by":"crossref","unstructured":"Pinheiro, P.H.O., Collobert, R.: From image-level to pixel-level labeling with convolutional networks. In: CVPR, pp. 1713\u20131721 (2015)","DOI":"10.1109\/CVPR.2015.7298780"},{"key":"7_CR13","unstructured":"Qi, C.R., Su, H., Mo, K., Guibas, L.J.: Pointnet: deep learning on point sets for 3D classification and segmentation. In: CVPR, pp. 77\u201385 (2017)"},{"key":"7_CR14","unstructured":"Ramon, J., Raedt, L.D.: Multi-instance neural networks (2000)"},{"key":"7_CR15","unstructured":"Sabour, S., Frosst, N., Hinton, G.E.: Dynamic routing between capsules. In: NIPS, pp. 3859\u20133869 (2017)"},{"key":"7_CR16","doi-asserted-by":"crossref","unstructured":"Sirinukunwattana, K., e Ahmed Raza, S., Tsang, Y., Snead, D.R.J., Cree, I.A., Rajpoot, N.M.: Locality sensitive deep learning for detection and classification of nuclei in routine colon cancer histology images. IEEE Trans. Med. Imaging 35(5), 1196\u20131206 (2016)","DOI":"10.1109\/TMI.2016.2525803"},{"key":"7_CR17","doi-asserted-by":"publisher","unstructured":"Tian, B., Zhang, Y., Wang, J., Xing, C.: Hierarchical inter-attention network for document classification with multi-task learning. In: Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence, IJCAI 2019, Macao, China, 10\u201316 August 2019, pp. 3569\u20133575 (2019). https:\/\/doi.org\/10.24963\/ijcai.2019\/495","DOI":"10.24963\/ijcai.2019\/495"},{"key":"7_CR18","unstructured":"Vaswani, A., et al.: Attention is all you need. In: NIPS, pp. 6000\u20136010 (2017)"},{"key":"7_CR19","doi-asserted-by":"crossref","unstructured":"Wang, F., et al.: Residual attention network for image classification. In: CVPR, pp. 6450\u20136458 (2017)","DOI":"10.1109\/CVPR.2017.683"},{"key":"7_CR20","doi-asserted-by":"publisher","unstructured":"Wang, J., Wang, Z., Zhang, D., Yan, J.: Combining knowledge with deep convolutional neural networks for short text classification. In: Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence, IJCAI 2017, Melbourne, Australia, 19\u201325 August 2017, pp. 2915\u20132921 (2017). https:\/\/doi.org\/10.24963\/ijcai.2017\/406","DOI":"10.24963\/ijcai.2017\/406"},{"key":"7_CR21","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)","journal-title":"Pattern Recogn."},{"key":"7_CR22","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"77","DOI":"10.1007\/978-3-030-26072-9_6","volume-title":"Web and Big Data","author":"Z Wang","year":"2019","unstructured":"Wang, Z., Zhang, Y., Xing, C.: Reducing wrong labels for distant supervision relation extraction with selective capsule network. In: Shao, J., Yiu, M.L., Toyoda, M., Zhang, D., Wang, W., Cui, B. (eds.) APWeb-WAIM 2019. LNCS, vol. 11641, pp. 77\u201392. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-26072-9_6"},{"issue":"4","key":"7_CR23","doi-asserted-by":"publisher","first-page":"975","DOI":"10.1109\/TNNLS.2016.2519102","volume":"28","author":"X Wei","year":"2017","unstructured":"Wei, X., Wu, J., Zhou, Z.: Scalable algorithms for multi-instance learning. IEEE Trans. Neural Netw. Learn. Syst. 28(4), 975\u2013987 (2017)","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"7_CR24","unstructured":"Zaheer, M., Kottur, S., Ravanbakhsh, S., P\u00f3czos, B., Salakhutdinov, R.R., Smola, A.J.: Deep sets. In: NIPS, pp. 3394\u20133404 (2017)"},{"key":"7_CR25","unstructured":"Zhang, Q., Goldman, S.A.: EM-DD: an improved multiple-instance learning technique. In: NIPS, pp. 1073\u20131080 (2001)"},{"key":"7_CR26","doi-asserted-by":"publisher","unstructured":"Zhao, K., et al.: Modeling patient visit using electronic medical records for cost profile estimation. In: Database Systems for Advanced Applications - 23rd International Conference, DASFAA 2018, Gold Coast, QLD, Australia, 21\u201324 May 2018, Proceedings, Part II, pp. 20\u201336 (2018). https:\/\/doi.org\/10.1007\/978-3-319-91458-9_2","DOI":"10.1007\/978-3-319-91458-9_2"},{"key":"7_CR27","doi-asserted-by":"crossref","unstructured":"Zhou, Z., Sun, Y., Li, Y.: Multi-instance learning by treating instances as non-I.I.D. samples. In: ICML, pp. 1249\u20131256 (2009)","DOI":"10.1145\/1553374.1553534"}],"container-title":["Lecture Notes in Computer Science","Database Systems for Advanced Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-59416-9_7","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,7]],"date-time":"2024-03-07T11:41:27Z","timestamp":1709811687000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-59416-9_7"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020]]},"ISBN":["9783030594152","9783030594169"],"references-count":27,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-59416-9_7","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2020]]},"assertion":[{"value":"22 September 2020","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"DASFAA","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Database Systems for Advanced Applications","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Jeju","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Korea (Republic of)","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2020","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"24 September 2020","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"27 September 2020","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"25","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"dasfaa2020","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/db.pknu.ac.kr\/dasfaa2020\/","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":"EasyChair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"487","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":"119","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":"23","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":"24% - 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":"3.11","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":"6.81","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":"15 demo papers and 4 industrial papers","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)"}}]}}