{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,8]],"date-time":"2026-07-08T16:01:01Z","timestamp":1783526461519,"version":"3.55.0"},"publisher-location":"Cham","reference-count":33,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031448577","type":"print"},{"value":"9783031448584","type":"electronic"}],"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\/978-3-031-44858-4_12","type":"book-chapter","created":{"date-parts":[[2023,10,7]],"date-time":"2023-10-07T05:01:55Z","timestamp":1696654915000},"page":"123-132","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":26,"title":["Pretraining is All You Need: A Multi-Atlas Enhanced Transformer Framework for\u00a0Autism Spectrum Disorder Classification"],"prefix":"10.1007","author":[{"given":"Lucas","family":"Mahler","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qi","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Julius","family":"Steiglechner","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Florian","family":"Birk","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Samuel","family":"Heczko","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Klaus","family":"Scheffler","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Gabriele","family":"Lohmann","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2023,10,1]]},"reference":[{"key":"12_CR1","doi-asserted-by":"publisher","first-page":"635657","DOI":"10.3389\/fninf.2021.635657","volume":"15","author":"MS Ahammed","year":"2021","unstructured":"Ahammed, M.S., Niu, S., Ahmed, M.R., Dong, J., Gao, X., Chen, Y.: DarkASDNet: classification of ASD on functional MRI using deep neural network. Front. Neuroinf. 15, 635657 (2021)","journal-title":"Front. Neuroinf."},{"issue":"1","key":"12_CR2","doi-asserted-by":"publisher","first-page":"95","DOI":"10.1016\/j.neuroimage.2007.07.007","volume":"38","author":"J Ashburner","year":"2007","unstructured":"Ashburner, J.: A fast diffeomorphic image registration algorithm. Neuroimage 38(1), 95\u2013113 (2007)","journal-title":"Neuroimage"},{"key":"12_CR3","unstructured":"Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization (2016)"},{"issue":"4","key":"12_CR4","doi-asserted-by":"publisher","first-page":"537","DOI":"10.1002\/mrm.1910340409","volume":"34","author":"B Biswal","year":"1995","unstructured":"Biswal, B., Yetkin, F.Z., Haughton, V.M., Hyde, J.S.: Functional connectivity in the motor cortex of resting human brain using echo-planar MRI. Magn. Reson. Med. 34(4), 537\u2013541 (1995)","journal-title":"Magn. Reson. Med."},{"issue":"8","key":"12_CR5","doi-asserted-by":"publisher","first-page":"1161","DOI":"10.3844\/jcssp.2019.1161.1183","volume":"15","author":"G Brihadiswaran","year":"2019","unstructured":"Brihadiswaran, G., Haputhanthri, D., Gunathilaka, S., Meedeniya, D., Jayarathna, S.: EEG-based processing and classification methodologies for autism spectrum disorder: a review. J. Comput. Sci. 15(8), 1161\u20131183 (2019)","journal-title":"J. Comput. Sci."},{"key":"12_CR6","first-page":"27","volume":"7","author":"C Craddock","year":"2013","unstructured":"Craddock, C., et al.: The neuro bureau preprocessing initiative: open sharing of preprocessed neuroimaging data and derivatives. Front. Neuroinform. 7, 27 (2013)","journal-title":"Front. Neuroinform."},{"issue":"8","key":"12_CR7","doi-asserted-by":"publisher","first-page":"1914","DOI":"10.1002\/hbm.21333","volume":"33","author":"RC Craddock","year":"2012","unstructured":"Craddock, R.C., James, G., Holtzheimer, P.E., III., Hu, X.P., Mayberg, H.S.: A whole brain fMRI atlas generated via spatially constrained spectral clustering. Hum. Brain Mapp. 33(8), 1914\u20131928 (2012)","journal-title":"Hum. Brain Mapp."},{"key":"12_CR8","doi-asserted-by":"crossref","unstructured":"Deng, J., Hasan, M.R., Mahmud, M., Hasan, M.M., Ahmed, K.A., Hossain, M.Z.: Diagnosing autism spectrum disorder using ensemble 3D-CNN: a preliminary study. In: 2022 IEEE International Conference on Image Processing (ICIP), October 2022. IEEE (2022)","DOI":"10.1109\/ICIP46576.2022.9897628"},{"key":"12_CR9","unstructured":"Devlin, J., Chang, M.W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding (2019)"},{"issue":"5997","key":"12_CR10","doi-asserted-by":"publisher","first-page":"1358","DOI":"10.1126\/science.1194144","volume":"329","author":"NUF Dosenbach","year":"2010","unstructured":"Dosenbach, N.U.F., et al.: Prediction of individual brain maturity using fMRI. Science 329(5997), 1358\u20131361 (2010)","journal-title":"Science"},{"key":"12_CR11","doi-asserted-by":"publisher","first-page":"1","DOI":"10.3389\/fnins.2018.00525","volume":"12","author":"Y Du","year":"2018","unstructured":"Du, Y., Fu, Z., Calhoun, V.D.: Classification and prediction of brain disorders using functional connectivity: promising but challenging. Front. Neurosci. 12, 1\u201329 (2018)","journal-title":"Front. Neurosci."},{"key":"12_CR12","doi-asserted-by":"publisher","first-page":"107375","DOI":"10.1016\/j.asoc.2021.107375","volume":"107","author":"TM Epalle","year":"2021","unstructured":"Epalle, T.M., Song, Y., Liu, Z., Lu, H.: Multi-atlas classification of autism spectrum disorder with hinge loss trained deep architectures: abide i results. Appl. Soft Comput. 107, 107375 (2021)","journal-title":"Appl. Soft Comput."},{"key":"12_CR13","doi-asserted-by":"publisher","unstructured":"Gerloff, C., Konrad, K., Kruppa, J., Schulte-R\u00fcther, M., Reindl, V.: Autism spectrum disorder classification based on interpersonal neural synchrony: can classification be improved by dyadic neural biomarkers using unsupervised graph representation learning? In: Abdulkadir, A., et al. (eds.) Machine Learning in Clinical Neuroimaging, MLCN 2022. LNCS, vol. 13596, pp. 147\u2013157. Springer, Cham (2022). https:\/\/doi.org\/10.1007\/978-3-031-17899-3_15","DOI":"10.1007\/978-3-031-17899-3_15"},{"key":"12_CR14","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Delving deep into rectifiers: surpassing human-level performance on ImageNet classification (2015)","DOI":"10.1109\/ICCV.2015.123"},{"key":"12_CR15","unstructured":"Hendrycks, D., Gimpel, K.: Gaussian error linear units (GELUs) (2023)"},{"key":"12_CR16","doi-asserted-by":"publisher","first-page":"55","DOI":"10.1016\/j.cortex.2014.08.011","volume":"63","author":"T Iidaka","year":"2015","unstructured":"Iidaka, T.: Resting state functional magnetic resonance imaging and neural network classified autism and control. Cortex 63, 55\u201367 (2015)","journal-title":"Cortex"},{"key":"12_CR17","doi-asserted-by":"publisher","first-page":"948704","DOI":"10.3389\/fnagi.2022.948704","volume":"14","author":"W Jiang","year":"2022","unstructured":"Jiang, W., et al.: CNNG: a convolutional neural networks with gated recurrent units for autism spectrum disorder classification. Front. Aging Neurosci. 14, 948704 (2022)","journal-title":"Front. Aging Neurosci."},{"key":"12_CR18","doi-asserted-by":"crossref","unstructured":"Kang, L., Gong, Z., Huang, J., Xu, J.: Autism spectrum disorder recognition based on machine learning with ROI time-series. NeuroImage Clin. (2023)","DOI":"10.2139\/ssrn.4457272"},{"issue":"3","key":"12_CR19","doi-asserted-by":"publisher","first-page":"213","DOI":"10.1007\/s42979-022-01617-9","volume":"4","author":"MR Lamani","year":"2023","unstructured":"Lamani, M.R., Benadit, P.J., Vaithinathan, K.: Multi-atlas graph convolutional networks and convolutional recurrent neural networks-based ensemble learning for classification of autism spectrum disorders. SN Comput. Sci. 4(3), 213 (2023)","journal-title":"SN Comput. Sci."},{"key":"12_CR20","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). https:\/\/doi.org\/10.1007\/978-3-030-00931-1_24"},{"key":"12_CR21","unstructured":"Loshchilov, I., Hutter, F.: Decoupled weight decay regularization (2019)"},{"issue":"6","key":"12_CR22","doi-asserted-by":"publisher","first-page":"659","DOI":"10.1038\/mp.2013.78","volume":"19","author":"AD Martino","year":"2013","unstructured":"Martino, A.D., Yan, C.G., et al.: The autism brain imaging data exchange: towards a large-scale evaluation of the intrinsic brain architecture in autism. Mol. Psychiatry 19(6), 659\u2013667 (2013)","journal-title":"Mol. Psychiatry"},{"key":"12_CR23","doi-asserted-by":"publisher","unstructured":"Qayyum, A., et al.: An efficient 1DCNN-LSTM deep learning model for assessment and classification of fMRI-based autism spectrum disorder. In: Raj, J.S., Kamel, K., Lafata, P. (eds.) Innovative Data Communication Technologies and Application, vol. 96, pp. 1039\u20131048. Springer, Singapore (2022). https:\/\/doi.org\/10.1007\/978-981-16-7167-8_77","DOI":"10.1007\/978-981-16-7167-8_77"},{"key":"12_CR24","unstructured":"Radford, A., Narasimhan, K., Salimans, T., Sutskever, I., et al.: Improving language understanding by generative pre-training (2018)"},{"key":"12_CR25","doi-asserted-by":"publisher","first-page":"116189","DOI":"10.1016\/j.neuroimage.2019.116189","volume":"206","author":"ET Rolls","year":"2020","unstructured":"Rolls, E.T., Huang, C.C., Lin, C.P., Feng, J., Joliot, M.: Automated anatomical labelling atlas 3. Neuroimage 206, 116189 (2020)","journal-title":"Neuroimage"},{"key":"12_CR26","doi-asserted-by":"publisher","first-page":"1","DOI":"10.3389\/fpsyt.2020.00440","volume":"11","author":"RM Thomas","year":"2020","unstructured":"Thomas, R.M., Gallo, S., Cerliani, L., Zhutovsky, P., El-Gazzar, A., van Wingen, G.: Classifying autism spectrum disorder using the temporal statistics of resting-state functional MRI data with 3D convolutional neural networks. Front. Psychiatry 11, 1\u201312 (2020)","journal-title":"Front. Psychiatry"},{"issue":"6","key":"12_CR27","first-page":"AR26","volume":"1","author":"S Timimi","year":"2019","unstructured":"Timimi, S., Milton, D., Bovell, V., Kapp, S., Russell, G.: Deconstructing diagnosis: four commentaries on a diagnostic tool to assess individuals for autism spectrum disorders. Autonomy (Birmingham, England) 1(6), AR26 (2019)","journal-title":"Autonomy (Birmingham, England)"},{"key":"12_CR28","unstructured":"Vaswani, A., et al.: Attention is all you need. In: Advances in Neural Information Processing Systems, vol. 30 (2017)"},{"key":"12_CR29","doi-asserted-by":"publisher","first-page":"346","DOI":"10.1016\/j.neucom.2020.06.152","volume":"469","author":"Y Wang","year":"2022","unstructured":"Wang, Y., Liu, J., Xiang, Y., Wang, J., Chen, Q., Chong, J.: MAGE: automatic diagnosis of autism spectrum disorders using multi-atlas graph convolutional networks and ensemble learning. Neurocomputing 469, 346\u2013353 (2022)","journal-title":"Neurocomputing"},{"key":"12_CR30","doi-asserted-by":"publisher","first-page":"108840","DOI":"10.1016\/j.jneumeth.2020.108840","volume":"343","author":"Y Wang","year":"2020","unstructured":"Wang, Y., Wang, J., Wu, F.X., Hayrat, R., Liu, J.: AIMAFE: autism spectrum disorder identification with multi-atlas deep feature representation and ensemble learning. J. Neurosci. Meth. 343, 108840 (2020)","journal-title":"J. Neurosci. Meth."},{"key":"12_CR31","first-page":"1","volume":"4","author":"C Yan","year":"2010","unstructured":"Yan, C., Zang, Y.: DPARSF: a MATLAB toolbox for \u201cpipeline\u2019\u2019 data analysis of resting-state fMRI. Front. Syst. Neurosci. 4, 1\u201317 (2010)","journal-title":"Front. Syst. Neurosci."},{"key":"12_CR32","doi-asserted-by":"crossref","unstructured":"Zerveas, G., Jayaraman, S., Patel, D., Bhamidipaty, A., Eickhoff, C.: A transformer-based framework for multivariate time series representation learning. In: Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, August 2021. ACM (2021)","DOI":"10.1145\/3447548.3467401"},{"key":"12_CR33","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"172","DOI":"10.1007\/978-3-030-00931-1_20","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2018","author":"Y Zhao","year":"2018","unstructured":"Zhao, Y., Ge, F., Zhang, S., Liu, T.: 3D deep convolutional neural network revealed the value of brain network overlap in differentiating autism spectrum disorder from healthy controls. In: Frangi, A.F., Schnabel, J.A., Davatzikos, C., Alberola-L\u00f3pez, C., Fichtinger, G. (eds.) MICCAI 2018. LNCS, vol. 11072, pp. 172\u2013180. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-00931-1_20"}],"container-title":["Lecture Notes in Computer Science","Machine Learning in Clinical Neuroimaging"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-44858-4_12","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,12]],"date-time":"2024-03-12T16:45:10Z","timestamp":1710261910000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-44858-4_12"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031448577","9783031448584"],"references-count":33,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-44858-4_12","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"1 October 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"MLCN","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Workshop on Machine Learning in Clinical Neuroimaging","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Vancouver, BC","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Canada","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":"8 October 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"12 October 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"6","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"mlcn2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/mlcnworkshop.github.io\/","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":"27","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":"16","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":"0","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":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}