{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,11]],"date-time":"2025-11-11T13:31:27Z","timestamp":1762867887787,"version":"3.40.3"},"publisher-location":"Cham","reference-count":32,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030299071"},{"type":"electronic","value":"9783030299088"}],"license":[{"start":{"date-parts":[[2019,1,1]],"date-time":"2019-01-01T00:00:00Z","timestamp":1546300800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2019]]},"DOI":"10.1007\/978-3-030-29908-8_19","type":"book-chapter","created":{"date-parts":[[2019,8,23]],"date-time":"2019-08-23T01:03:32Z","timestamp":1566522212000},"page":"230-243","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["MIDCN: A Multiple Instance Deep Convolutional Network for Image Classification"],"prefix":"10.1007","author":[{"given":"Kelei","family":"He","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jing","family":"Huo","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yinghuan","family":"Shi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yang","family":"Gao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dinggang","family":"Shen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2019,8,23]]},"reference":[{"key":"19_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). https:\/\/doi.org\/10.1016\/j.artint.2013.06.003","journal-title":"Artif. Intell."},{"unstructured":"Andrews, S., Tsochantaridis, I., Hofmann, T.: Support vector machines for multiple-instance learning. In: NIPS, Vancouver, BC, Canada, 9\u201314 December 2002, pp. 561\u2013568 (2002)","key":"19_CR2"},{"unstructured":"Babenko, B., Verma, N., Doll\u00e1r, P., Belongie, S.J.: Multiple instance learning with manifold bags. In: ICML 2011, Bellevue, WA, USA, 28 June\u20132 July 2011, pp. 81\u201388 (2011)","key":"19_CR3"},{"issue":"3","key":"19_CR4","doi-asserted-by":"publisher","first-page":"27","DOI":"10.1145\/1961189.1961199","volume":"2","author":"C Chang","year":"2011","unstructured":"Chang, C., Lin, C.: LIBSVM: a library for support vector machines. ACM TIST 2(3), 27 (2011). https:\/\/doi.org\/10.1145\/1961189.1961199","journal-title":"ACM TIST"},{"doi-asserted-by":"crossref","unstructured":"Chatfield, K., Simonyan, K., Vedaldi, A., Zisserman, A.: Return of the devil in the details: delving deep into convolutional nets. In: British Machine Vision Conference (2014)","key":"19_CR5","DOI":"10.5244\/C.28.6"},{"doi-asserted-by":"publisher","unstructured":"Cheng, M., Zhang, Z., Lin, W., Torr, P.H.S.: BING: binarized normed gradients for objectness estimation at 300 fps. In: CVPR 2014, Columbus, OH, USA, 23\u201328 June 2014, pp. 3286\u20133293 (2014). https:\/\/doi.org\/10.1109\/CVPR.2014.414","key":"19_CR6","DOI":"10.1109\/CVPR.2014.414"},{"issue":"1","key":"19_CR7","doi-asserted-by":"publisher","first-page":"98","DOI":"10.1007\/s11263-014-0733-5","volume":"111","author":"M Everingham","year":"2015","unstructured":"Everingham, M., Eslami, S.M.A., Gool, L.J.V., Williams, C.K.I., Winn, J.M., Zisserman, A.: The Pascal visual object classes challenge: a retrospective. Int. J. Comput. Vis. 111(1), 98\u2013136 (2015). https:\/\/doi.org\/10.1007\/s11263-014-0733-5","journal-title":"Int. J. Comput. Vis."},{"doi-asserted-by":"crossref","unstructured":"Feng, J., Zhou, Z.H.: Deep MIML network. In: AAAI, pp. 1884\u20131890 (2017)","key":"19_CR8","DOI":"10.1609\/aaai.v31i1.10890"},{"doi-asserted-by":"publisher","unstructured":"Girshick, R.B., Donahue, J., Darrell, T., Malik, J.: Rich feature hierarchies for accurate object detection and semantic segmentation. In: CVPR 2014, Columbus, OH, USA, 23\u201328 June 2014, pp. 580\u2013587 (2014). https:\/\/doi.org\/10.1109\/CVPR.2014.81","key":"19_CR9","DOI":"10.1109\/CVPR.2014.81"},{"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)","key":"19_CR10","DOI":"10.1109\/CVPR.2016.90"},{"doi-asserted-by":"publisher","unstructured":"Hoffman, J., Pathak, D., Darrell, T., Saenko, K.: Detector discovery in the wild: joint multiple instance and representation learning. In: CVPR 2015, Boston, MA, USA, 7\u201312 June 2015, pp. 2883\u20132891 (2015). https:\/\/doi.org\/10.1109\/CVPR.2015.7298906","key":"19_CR11","DOI":"10.1109\/CVPR.2015.7298906"},{"doi-asserted-by":"crossref","unstructured":"Huang, G., Liu, Z., Van Der Maaten, L., Weinberger, K.Q.: Densely connected convolutional networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4700\u20134708 (2017)","key":"19_CR12","DOI":"10.1109\/CVPR.2017.243"},{"doi-asserted-by":"publisher","unstructured":"Jia, Y., et al.: Caffe: convolutional architecture for fast feature embedding. In: MM 2014, Orlando, FL, USA, 03\u201307 November 2014, pp. 675\u2013678 (2014). https:\/\/doi.org\/10.1145\/2647868.2654889","key":"19_CR13","DOI":"10.1145\/2647868.2654889"},{"doi-asserted-by":"publisher","unstructured":"Karpathy, A., Li, F.: Deep visual-semantic alignments for generating image descriptions. In: CVPR 2015, Boston, MA, USA, 7\u201312 June 2015, pp. 3128\u20133137 (2015). https:\/\/doi.org\/10.1109\/CVPR.2015.7298932","key":"19_CR14","DOI":"10.1109\/CVPR.2015.7298932"},{"unstructured":"Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. In: NIPS, Lake Tahoe, NV, USA, 3\u20136 December 2012, pp. 1106\u20131114 (2012)","key":"19_CR15"},{"issue":"4","key":"19_CR16","doi-asserted-by":"publisher","first-page":"541","DOI":"10.1162\/neco.1989.1.4.541","volume":"1","author":"Y LeCun","year":"1989","unstructured":"LeCun, Y., et al.: Backpropagation applied to handwritten zip code recognition. Neural Comput. 1(4), 541\u2013551 (1989). https:\/\/doi.org\/10.1162\/neco.1989.1.4.541","journal-title":"Neural Comput."},{"issue":"2","key":"19_CR17","doi-asserted-by":"publisher","first-page":"1106","DOI":"10.1016\/j.neuroimage.2012.01.055","volume":"60","author":"M Liu","year":"2012","unstructured":"Liu, M., Zhang, D., Shen, D.: Ensemble sparse classification of Alzheimer\u2019s disease. NeuroImage 60(2), 1106\u20131116 (2012). https:\/\/doi.org\/10.1016\/j.neuroimage.2012.01.055","journal-title":"NeuroImage"},{"doi-asserted-by":"publisher","unstructured":"Mittelman, R., Lee, H., Kuipers, B., Savarese, S.: Weakly supervised learning of mid-level features with Beta-Bernoulli process restricted Boltzmann machines. In: CVPR, Portland, OR, USA, 23\u201328 June 2013, pp. 476\u2013483 (2013). https:\/\/doi.org\/10.1109\/CVPR.2013.68","key":"19_CR18","DOI":"10.1109\/CVPR.2013.68"},{"doi-asserted-by":"publisher","unstructured":"Oquab, M., Bottou, L., Laptev, I., Sivic, J.: Learning and transferring mid-level image representations using convolutional neural networks. In: CVPR 2014, Columbus, OH, USA, 23\u201328 June 2014, pp. 1717\u20131724 (2014). https:\/\/doi.org\/10.1109\/CVPR.2014.222","key":"19_CR19","DOI":"10.1109\/CVPR.2014.222"},{"doi-asserted-by":"crossref","unstructured":"Oquab, M., Bottou, L., Laptev, I., Sivic, J.: Is object localization for free? Weakly-supervised learning with convolutional neural networks. In: CVPR, Boston, USA, June 2015","key":"19_CR20","DOI":"10.1109\/CVPR.2015.7298668"},{"unstructured":"Paszke, A., et al.: Automatic differentiation in PyTorch. In: NIPS-W (2017)","key":"19_CR21"},{"doi-asserted-by":"publisher","unstructured":"Pathak, D., Kr\u00e4henb\u00fchl, P., Darrell, T.: Constrained convolutional neural networks for weakly supervised segmentation. In: ICCV 2015, Santiago, Chile, 7\u201313 December 2015, pp. 1796\u20131804 (2015). https:\/\/doi.org\/10.1109\/ICCV.2015.209","key":"19_CR22","DOI":"10.1109\/ICCV.2015.209"},{"doi-asserted-by":"publisher","unstructured":"Pinheiro, P.H.O., Collobert, R.: From image-level to pixel-level labeling with convolutional networks. In: CVPR 2015, Boston, MA, USA, 7\u201312 June 2015, pp. 1713\u20131721 (2015). https:\/\/doi.org\/10.1109\/CVPR.2015.7298780","key":"19_CR23","DOI":"10.1109\/CVPR.2015.7298780"},{"unstructured":"Ren, S., He, K., Girshick, R., Sun, J.: Faster R-CNN: towards real-time object detection with region proposal networks. In: Advances in Neural Information Processing Systems, pp. 91\u201399 (2015)","key":"19_CR24"},{"unstructured":"Sermanet, P., Eigen, D., Zhang, X., Mathieu, M., Fergus, R., LeCun, Y.: Overfeat: integrated recognition, localization and detection using convolutional networks. CoRR abs\/1312.6229 (2013)","key":"19_CR25"},{"issue":"10","key":"19_CR26","doi-asserted-by":"publisher","first-page":"2675","DOI":"10.1109\/TBME.2013.2262099","volume":"60","author":"Y Shi","year":"2013","unstructured":"Shi, Y., Gao, Y., Yang, Y., Zhang, Y., Wang, D.: Multimodal sparse representation-based classification for lung needle biopsy images. IEEE Trans. Biomed. Eng. 60(10), 2675\u20132685 (2013). https:\/\/doi.org\/10.1109\/TBME.2013.2262099","journal-title":"IEEE Trans. Biomed. Eng."},{"doi-asserted-by":"crossref","unstructured":"Sun, M., Han, T.X., Liu, M.C., Khodayari-Rostamabad, A.: Multiple instance learning convolutional neural networks for object recognition. In: 2016 International Conference on Pattern Recognition, pp. 3270\u20133275. IEEE (2016)","key":"19_CR27","DOI":"10.1109\/ICPR.2016.7900139"},{"doi-asserted-by":"publisher","unstructured":"Taigman, Y., Yang, M., Ranzato, M., Wolf, L.: Deepface: closing the gap to human-level performance in face verification. In: CVPR 2014, Columbus, OH, USA, 23\u201328 June 2014, pp. 1701\u20131708 (2014). https:\/\/doi.org\/10.1109\/CVPR.2014.220","key":"19_CR28","DOI":"10.1109\/CVPR.2014.220"},{"unstructured":"Wei, Y., et al.: CNN: single-label to multi-label. CoRR abs\/1406.5726 (2014)","key":"19_CR29"},{"doi-asserted-by":"publisher","unstructured":"Wu, J., Yu, Y., Huang, C., Yu, K.: Deep multiple instance learning for image classification and auto-annotation. In: CVPR 2015, Boston, MA, USA, 7\u201312 June 2015, pp. 3460\u20133469 (2015). https:\/\/doi.org\/10.1109\/CVPR.2015.7298968","key":"19_CR30","DOI":"10.1109\/CVPR.2015.7298968"},{"doi-asserted-by":"publisher","unstructured":"Xu, Y., Mo, T., Feng, Q., Zhong, P., Lai, M., Chang, E.I.: Deep learning of feature representation with multiple instance learning for medical image analysis. In: ICASSP 2014, Florence, Italy, 4\u20139 May 2014, pp. 1626\u20131630 (2014). https:\/\/doi.org\/10.1109\/ICASSP.2014.6853873","key":"19_CR31","DOI":"10.1109\/ICASSP.2014.6853873"},{"issue":"4","key":"19_CR32","doi-asserted-by":"publisher","first-page":"1684","DOI":"10.1109\/TSP.2011.2179539","volume":"60","author":"L Zhang","year":"2012","unstructured":"Zhang, L., et al.: Kernel sparse representation-based classifier. IEEE Trans. Signal Process. 60(4), 1684\u20131695 (2012). https:\/\/doi.org\/10.1109\/TSP.2011.2179539","journal-title":"IEEE Trans. Signal Process."}],"container-title":["Lecture Notes in Computer Science","PRICAI 2019: Trends in Artificial Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-29908-8_19","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,7]],"date-time":"2024-03-07T15:20:47Z","timestamp":1709824847000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-29908-8_19"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019]]},"ISBN":["9783030299071","9783030299088"],"references-count":32,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-29908-8_19","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2019]]},"assertion":[{"value":"23 August 2019","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"PRICAI","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Pacific Rim International Conference on Artificial Intelligence","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Cuvu, Yanuka Island","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Fiji","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2019","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"26 August 2019","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"30 August 2019","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"16","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"pricai2019","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/www.pricai.org\/2019\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}