{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,19]],"date-time":"2025-03-19T13:02:02Z","timestamp":1742389322391},"publisher-location":"Cham","reference-count":16,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030203504"},{"type":"electronic","value":"9783030203511"}],"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\/978-3-030-20351-1_56","type":"book-chapter","created":{"date-parts":[[2019,5,22]],"date-time":"2019-05-22T11:53:24Z","timestamp":1558526004000},"page":"718-730","update-policy":"http:\/\/dx.doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["Efficient Interpretation of Deep Learning Models Using Graph Structure and Cooperative Game Theory: Application to ASD Biomarker Discovery"],"prefix":"10.1007","author":[{"given":"Xiaoxiao","family":"Li","sequence":"first","affiliation":[]},{"given":"Nicha C.","family":"Dvornek","sequence":"additional","affiliation":[]},{"given":"Yuan","family":"Zhou","sequence":"additional","affiliation":[]},{"given":"Juntang","family":"Zhuang","sequence":"additional","affiliation":[]},{"given":"Pamela","family":"Ventola","sequence":"additional","affiliation":[]},{"given":"James S.","family":"Duncan","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2019,5,22]]},"reference":[{"key":"56_CR1","doi-asserted-by":"publisher","first-page":"100","DOI":"10.3389\/fpsyt.2014.00100","volume":"5","author":"AAS Goldani","year":"2014","unstructured":"Goldani, A.A.S., Downs, S.R., Widjaja, F., Lawton, B., Hendren, R.L.: Biomarkers in autism. Front. Psychiatry 5, 100 (2014)","journal-title":"Front. Psychiatry"},{"key":"56_CR2","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"206","DOI":"10.1007\/978-3-030-00931-1_24","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2018","author":"X Li","year":"2018","unstructured":"Li, X., Dvornek, N.C., Zhuang, J., Ventola, P., Duncan, J.S.: Brain biomarker interpretation in ASD using deep learning and fMRI. In: Frangi, A.F., Schnabel, J.A., Davatzikos, C., Alberola-L\u00f3pez, C., Fichtinger, G. (eds.) MICCAI 2018. LNCS, vol. 11072, pp. 206\u2013214. Springer, Cham (2018). \n                      https:\/\/doi.org\/10.1007\/978-3-030-00931-1_24"},{"issue":"49","key":"56_CR3","doi-asserted-by":"publisher","first-page":"21223","DOI":"10.1073\/pnas.1010412107","volume":"107","author":"MD Kaiser","year":"2010","unstructured":"Kaiser, M.D., et al.: Neural signatures of autism. Proc. Natl. Acad. Sci. 107(49), 21223\u201321228 (2010)","journal-title":"Proc. Natl. Acad. Sci."},{"key":"56_CR4","unstructured":"Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Advances in Neural Information Processing Systems, pp. 4765\u20134774 (2017)"},{"key":"56_CR5","unstructured":"Chen, J., Song, L., Wainwright, M.J., Jordan, M.I.: L-shapley and C-shapley: efficient model interpretation for structured data. arXiv preprint \n                      arXiv:1808.02610\n                      \n                     (2018)"},{"issue":"Jan","key":"56_CR6","first-page":"1","volume":"11","author":"I Kononenko","year":"2010","unstructured":"Kononenko, I., Strumbelj, E.: An efficient explanation of individual classifications using game theory. J. Mach. Learn. Res. 11(Jan), 1\u201318 (2010)","journal-title":"J. Mach. Learn. Res."},{"issue":"28","key":"56_CR7","first-page":"307","volume":"2","author":"LS Shapley","year":"1953","unstructured":"Shapley, L.S.: A value for n-person games. Contrib. Theory Games 2(28), 307\u2013317 (1953)","journal-title":"Contrib. Theory Games"},{"key":"56_CR8","unstructured":"Zintgraf, L.M., Cohen, T.S., Adel, T., Welling, M.: Visualizing deep neural network decisions: prediction difference analysis. arXiv preprint \n                      arXiv:1702.04595\n                      \n                     (2017)"},{"issue":"6","key":"56_CR9","doi-asserted-by":"publisher","first-page":"066111","DOI":"10.1103\/PhysRevE.70.066111","volume":"70","author":"A Clauset","year":"2004","unstructured":"Clauset, A., Newman, M.E., Moore, C.: Finding community structure in very large networks. Phys. Rev. E 70(6), 066111 (2004)","journal-title":"Phys. Rev. E"},{"issue":"11","key":"56_CR10","doi-asserted-by":"publisher","first-page":"2278","DOI":"10.1109\/5.726791","volume":"86","author":"Y LeCun","year":"1998","unstructured":"LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proc. IEEE 86(11), 2278\u20132324 (1998)","journal-title":"Proc. IEEE"},{"issue":"11","key":"56_CR11","doi-asserted-by":"publisher","first-page":"2274","DOI":"10.1109\/TPAMI.2012.120","volume":"34","author":"R Achanta","year":"2012","unstructured":"Achanta, R., Shaji, A., Smith, K., Lucchi, A., Fua, P., S\u00fcsstrunk, S.: Slic superpixels compared to state-of-the-art superpixel methods. IEEE Trans. Pattern Anal. Mach. Intell. 34(11), 2274\u20132282 (2012)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"56_CR12","doi-asserted-by":"publisher","first-page":"273","DOI":"10.1006\/nimg.2001.0978","volume":"15","author":"N Tzourio-Mazoyer","year":"2002","unstructured":"Tzourio-Mazoyer, N., et al.: Automated anatomical labeling of activations in SPM using a macroscopic anatomical parcellation of the MNI MRI single-subject brain. Neuroimage 15, 273\u2013289 (2002)","journal-title":"Neuroimage"},{"key":"56_CR13","doi-asserted-by":"publisher","DOI":"10.1093\/oso\/9780198805090.001.0001","volume-title":"Networks","author":"M Newman","year":"2018","unstructured":"Newman, M.: Networks. Oxford University Press, Oxford (2018)"},{"key":"56_CR14","doi-asserted-by":"crossref","unstructured":"Li, X., et al.: 2-channel convolutional 3D deep neural network (2CC3D) for fMRI analysis: ASD classification and feature learning. In: 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018), pp. 1252\u20131255. IEEE (2018)","DOI":"10.1109\/ISBI.2018.8363798"},{"issue":"5","key":"56_CR15","doi-asserted-by":"publisher","first-page":"429","DOI":"10.1192\/bjp.133.5.429","volume":"133","author":"RC Young","year":"1978","unstructured":"Young, R.C., Biggs, J.T., Ziegler, V.E., Meyer, D.A.: A rating scale for mania: reliability, validity and sensitivity. Br. J. Psychiatry 133(5), 429\u2013435 (1978)","journal-title":"Br. J. Psychiatry"},{"issue":"8","key":"56_CR16","doi-asserted-by":"publisher","first-page":"665","DOI":"10.1038\/nmeth.1635","volume":"8","author":"T Yarkoni","year":"2011","unstructured":"Yarkoni, T., Poldrack, R.A., Nichols, T.E., Van Essen, D.C., Wager, T.D.: Large-scale automated synthesis of human functional neuroimaging data. Nat. Methods 8(8), 665 (2011)","journal-title":"Nat. Methods"}],"container-title":["Lecture Notes in Computer Science","Information Processing in Medical Imaging"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-20351-1_56","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2019,5,22]],"date-time":"2019-05-22T11:58:18Z","timestamp":1558526298000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/978-3-030-20351-1_56"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019]]},"ISBN":["9783030203504","9783030203511"],"references-count":16,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-20351-1_56","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":"22 May 2019","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"IPMI","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Information Processing in Medical Imaging","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Hong Kong","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China","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":"2 June 2019","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"7 June 2019","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"26","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ipmi2019","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/ipmi2019.cse.ust.hk\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}