{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,20]],"date-time":"2025-09-20T09:15:29Z","timestamp":1758359729242,"version":"3.44.0"},"publisher-location":"Cham","reference-count":29,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783032049834"},{"type":"electronic","value":"9783032049841"}],"license":[{"start":{"date-parts":[[2025,9,20]],"date-time":"2025-09-20T00:00:00Z","timestamp":1758326400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,9,20]],"date-time":"2025-09-20T00:00:00Z","timestamp":1758326400000},"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":[[2026]]},"DOI":"10.1007\/978-3-032-04984-1_18","type":"book-chapter","created":{"date-parts":[[2025,9,19]],"date-time":"2025-09-19T16:24:29Z","timestamp":1758299069000},"page":"181-191","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Explainable ADHD Diagnostic Framework Using Weakly-Supervised Action Recognition"],"prefix":"10.1007","author":[{"given":"Ninghan","family":"Fan","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ming","family":"Kong","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jing","family":"Huang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Bingdi","family":"Chen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qiang","family":"Zhu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,9,20]]},"reference":[{"key":"18_CR1","doi-asserted-by":"crossref","unstructured":"Amado-Caballero, P., et al.: Objective adhd diagnosis using convolutional neural networks over daily-life activity records. IEEE J. Biomed. Health Inf. 24(9), 2690\u20132700 (2020)","DOI":"10.1109\/JBHI.2020.2964072"},{"issue":"9","key":"18_CR2","doi-asserted-by":"publisher","first-page":"907","DOI":"10.1016\/0028-3932(93)90147-R","volume":"31","author":"C Bench","year":"1993","unstructured":"Bench, C., et al.: Investigations of the functional anatomy of attention using the stroop test. Neuropsychologia 31(9), 907\u2013922 (1993)","journal-title":"Neuropsychologia"},{"issue":"11","key":"18_CR3","doi-asserted-by":"publisher","first-page":"1547","DOI":"10.1177\/1087054716649666","volume":"24","author":"JC Bledsoe","year":"2020","unstructured":"Bledsoe, J.C., et al.: Diagnostic classification of adhd versus control: support vector machine classification using brief neuropsychological assessment. J. Atten. Disord. 24(11), 1547\u20131556 (2020)","journal-title":"J. Atten. Disord."},{"key":"18_CR4","doi-asserted-by":"publisher","first-page":"69","DOI":"10.3389\/fnsys.2012.00069","volume":"6","author":"MR Brown","year":"2012","unstructured":"Brown, M.R., et al.: Adhd-200 global competition: diagnosing adhd using personal characteristic data can outperform resting state fmri measurements. Front. Syst. Neurosci. 6, 69 (2012)","journal-title":"Front. Syst. Neurosci."},{"issue":"9","key":"18_CR5","doi-asserted-by":"publisher","first-page":"657","DOI":"10.1080\/08839514.2021.1933761","volume":"35","author":"T Chen","year":"2021","unstructured":"Chen, T., Antoniou, G., Adamou, M., Tachmazidis, I., Su, P.: Automatic diagnosis of attention deficit hyperactivity disorder using machine learning. Appl. Artif. Intell. 35(9), 657\u2013669 (2021)","journal-title":"Appl. Artif. Intell."},{"key":"18_CR6","volume-title":"Handbook of Cognition and Emotion","author":"T Dalgleish","year":"2000","unstructured":"Dalgleish, T., Power, M.: Handbook of Cognition and Emotion. John Wiley & Sons, Hoboken (2000)"},{"key":"18_CR7","unstructured":"Devlin, J., Chang, M.W., Lee, K., Toutanova, K.: Bert: pre-training of deep bidirectional transformers for language understanding. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, vol. 1 (long and short papers), pp. 4171\u20134186 (2019)"},{"issue":"5","key":"18_CR8","doi-asserted-by":"publisher","first-page":"e1133","DOI":"10.1038\/tp.2017.86","volume":"7","author":"M Duda","year":"2017","unstructured":"Duda, M., Haber, N., Daniels, J., Wall, D.: Crowdsourced validation of a machine-learning classification system for autism and adhd. Transl. Psychiat. 7(5), e1133\u2013e1133 (2017)","journal-title":"Transl. Psychiat."},{"issue":"21","key":"18_CR9","first-page":"591","volume":"21","author":"F Edition","year":"2013","unstructured":"Edition, F., et al.: Diagnostic and statistical manual of mental disorders. Am. Psychiatric Assoc. 21(21), 591\u2013643 (2013)","journal-title":"Am. Psychiatric Assoc."},{"key":"18_CR10","unstructured":"Huang, J., Kong, M., Chen, L., Liang, T., Zhu, Q.: Temporal rpn learning for weakly-supervised temporal action localization. In: Asian Conference on Machine Learning, pp. 470\u2013485. PMLR (2024)"},{"key":"18_CR11","unstructured":"Huang, Z., Xu, W., Yu, K.: Bidirectional lstm-crf models for sequence tagging. arXiv preprint arXiv:1508.01991 (2015)"},{"key":"18_CR12","unstructured":"Jang, E., Gu, S., Poole, B.: Categorical reparameterization with gumbel-softmax. arXiv preprint arXiv:1611.01144 (2016)"},{"key":"18_CR13","doi-asserted-by":"crossref","unstructured":"Kao, G.S., Thomas, H.M.: Test review: C. keith conners conners 3rd edition toronto, Ontario, Canada: multi-health systems, 2008. J. Psychoeduc. Assess. 28(6), 598\u2013602 (2010)","DOI":"10.1177\/0734282909360011"},{"issue":"3","key":"18_CR14","doi-asserted-by":"publisher","first-page":"e233502","DOI":"10.1001\/jamanetworkopen.2023.3502","volume":"6","author":"WP Kim","year":"2023","unstructured":"Kim, W.P., et al.: Machine learning-based prediction of attention-deficit\/hyperactivity disorder and sleep problems with wearable data in children. JAMA Netw. Open 6(3), e233502\u2013e233502 (2023)","journal-title":"JAMA Netw. Open"},{"key":"18_CR15","doi-asserted-by":"publisher","first-page":"241","DOI":"10.1007\/s12402-010-0045-8","volume":"2","author":"KW Lange","year":"2010","unstructured":"Lange, K.W., Reichl, S., Lange, K.M., Tucha, L., Tucha, O.: The history of attention deficit hyperactivity disorder. ADHD Attent. Deficit Hyperact. Disord. 2, 241\u2013255 (2010)","journal-title":"ADHD Attent. Deficit Hyperact. Disord."},{"issue":"11","key":"18_CR16","doi-asserted-by":"publisher","first-page":"3993","DOI":"10.3390\/s18113993","volume":"18","author":"M Leo","year":"2018","unstructured":"Leo, M., et al.: Computational assessment of facial expression production in asd children. Sensors 18(11), 3993 (2018)","journal-title":"Sensors"},{"key":"18_CR17","doi-asserted-by":"crossref","unstructured":"Li, Y., Nair, R., Naqvi, S.M.: Video-based skeleton data analysis for ADHD detection. SSCI (2023)","DOI":"10.1109\/SSCI52147.2023.10372062"},{"key":"18_CR18","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"740","DOI":"10.1007\/978-3-319-10602-1_48","volume-title":"Computer Vision \u2013 ECCV 2014","author":"T-Y Lin","year":"2014","unstructured":"Lin, T.-Y., et al.: Microsoft COCO: common objects in context. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8693, pp. 740\u2013755. Springer, Cham (2014). https:\/\/doi.org\/10.1007\/978-3-319-10602-1_48"},{"issue":"19","key":"18_CR19","doi-asserted-by":"publisher","first-page":"7733","DOI":"10.1523\/JNEUROSCI.21-19-07733.2001","volume":"21","author":"O Monchi","year":"2001","unstructured":"Monchi, O., Petrides, M., Petre, V., Worsley, K., Dagher, A.: Wisconsin card sorting revisited: distinct neural circuits participating in different stages of the task identified by event-related functional magnetic resonance imaging. J. Neurosci. 21(19), 7733\u20137741 (2001)","journal-title":"J. Neurosci."},{"key":"18_CR20","doi-asserted-by":"publisher","first-page":"86297","DOI":"10.1109\/ACCESS.2023.3304236","volume":"11","author":"C Nash","year":"2023","unstructured":"Nash, C., Nair, R., Naqvi, S.M.: Machine learning in adhd and depression mental health diagnosis: a survey. IEEE Access 11, 86297\u201386317 (2023)","journal-title":"IEEE Access"},{"key":"18_CR21","doi-asserted-by":"crossref","unstructured":"Nash, C., Nair, R., Naqvi, S.M.: Insights into detecting adult adhd symptoms through advanced dual-stream machine learning. IEEE Trans. Neural Syst. Rehabil. Eng. (2024)","DOI":"10.1109\/TNSRE.2024.3450848"},{"key":"18_CR22","doi-asserted-by":"publisher","first-page":"04009","DOI":"10.7189\/jogh.11.04009","volume":"11","author":"P Song","year":"2021","unstructured":"Song, P., Zha, M., Yang, Q., Zhang, Y., Li, X., Rudan, I.: The prevalence of adult attention-deficit hyperactivity disorder: a global systematic review and meta-analysis. J. Glob. Health 11, 04009 (2021)","journal-title":"J. Glob. Health"},{"issue":"1","key":"18_CR23","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s13755-020-00123-7","volume":"9","author":"I Tachmazidis","year":"2020","unstructured":"Tachmazidis, I., Chen, T., Adamou, M., Antoniou, G.: A hybrid AI approach for supporting clinical diagnosis of attention deficit hyperactivity disorder (adhd) in adults. Health Inf. Sci. Syst. 9(1), 1 (2020)","journal-title":"Health Inf. Sci. Syst."},{"key":"18_CR24","unstructured":"Team, Q.: Qwen2.5-vl (2025). https:\/\/qwenlm.github.io\/blog\/qwen2.5-vl\/"},{"key":"18_CR25","doi-asserted-by":"crossref","unstructured":"Thomas, R., Sanders, S., Doust, J., Beller, E., Glasziou, P.: Prevalence of attention-deficit\/hyperactivity disorder: a systematic review and meta-analysis. Pediatrics 135(4), e994\u2013e1001 (2015)","DOI":"10.1542\/peds.2014-3482"},{"key":"18_CR26","doi-asserted-by":"crossref","unstructured":"Wang, L., Xiong, Y., Lin, D., Van\u00a0Gool, L.: Untrimmednets for weakly supervised action recognition and detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4325\u20134334 (2017)","DOI":"10.1109\/CVPR.2017.678"},{"key":"18_CR27","doi-asserted-by":"crossref","unstructured":"Yan, S., Xiong, Y., Lin, D.: Spatial temporal graph convolutional networks for skeleton-based action recognition. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol.\u00a032 (2018)","DOI":"10.1609\/aaai.v32i1.12328"},{"issue":"3","key":"18_CR28","doi-asserted-by":"publisher","first-page":"400","DOI":"10.1631\/FITEE.1900729","volume":"22","author":"Y Zhang","year":"2021","unstructured":"Zhang, Y., et al.: Auxiliary diagnostic system for adhd in children based on ai technology. Front. Inf. Technol. Electron. Eng. 22(3), 400\u2013414 (2021)","journal-title":"Front. Inf. Technol. Electron. Eng."},{"key":"18_CR29","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Kong, M., Zhao, T., Hong, W., Zhu, Q., Wu, F.: Adhd intelligent auxiliary diagnosis system based on multimodal information fusion. In: Proceedings of the 28th ACM International Conference on Multimedia, pp. 4494\u20134496 (2020)","DOI":"10.1145\/3394171.3414359"}],"container-title":["Lecture Notes in Computer Science","Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2025"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-032-04984-1_18","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,19]],"date-time":"2025-09-19T16:24:34Z","timestamp":1758299074000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-032-04984-1_18"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,9,20]]},"ISBN":["9783032049834","9783032049841"],"references-count":29,"URL":"https:\/\/doi.org\/10.1007\/978-3-032-04984-1_18","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2025,9,20]]},"assertion":[{"value":"20 September 2025","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"The authors have no competing interests to declare.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Disclosure of Interests"}},{"value":"MICCAI","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Medical Image Computing and Computer-Assisted Intervention","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Daejeon","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":"2025","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"23 September 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"27 September 2025","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"28","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"miccai2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/conferences.miccai.org\/2025\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}