{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,22]],"date-time":"2025-12-22T18:34:41Z","timestamp":1766428481875,"version":"3.44.0"},"publisher-location":"Cham","reference-count":21,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783032060037","type":"print"},{"value":"9783032060044","type":"electronic"}],"license":[{"start":{"date-parts":[[2025,9,22]],"date-time":"2025-09-22T00:00:00Z","timestamp":1758499200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,9,22]],"date-time":"2025-09-22T00:00:00Z","timestamp":1758499200000},"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\/978-3-032-06004-4_21","type":"book-chapter","created":{"date-parts":[[2025,9,22]],"date-time":"2025-09-22T17:21:28Z","timestamp":1758561688000},"page":"206-214","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Application of\u00a0CoTNet-50 in\u00a0the\u00a0Classification of\u00a0Alzheimer\u2019s Disease"],"prefix":"10.1007","author":[{"given":"Jaehoon","family":"Go","sequence":"first","affiliation":[]},{"given":"Junyeong","family":"Kim","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,9,22]]},"reference":[{"key":"21_CR1","unstructured":"Adam, K.D.B.J., et\u00a0al.: A method for stochastic optimization. arXiv preprint arXiv:1412.69801412(6) (2014)"},{"key":"21_CR2","doi-asserted-by":"crossref","unstructured":"Arevalo-Rodriguez, I., et al.: Mini-mental state examination (MMSE) for the detection of Alzheimer\u2019s disease and other dementias in people with mild cognitive impairment (MCI). Cochrane Database Syst. Rev. (3) (2015)","DOI":"10.1002\/14651858.CD010783.pub2"},{"key":"21_CR3","doi-asserted-by":"publisher","unstructured":"Brosch, T., Tam, R.: Manifold learning of brain MRIs by deep learning. In: Mori, K., Sakuma, I., Sato, Y., Barillot, C., Navab, N. (eds.) MICCAI 2013. LNCS, vol. 8150, pp. 633\u2013640. Springer, Heidelberg (2013). https:\/\/doi.org\/10.1007\/978-3-642-40763-5_78","DOI":"10.1007\/978-3-642-40763-5_78"},{"key":"21_CR4","unstructured":"Foret, P., Kleiner, A., Mobahi, H., Neyshabur, B.: Sharpness-aware minimization for efficiently improving generalization. arXiv preprint arXiv:2010.01412 (2020)"},{"key":"21_CR5","unstructured":"Gupta, A., Ayhan, M., Maida, A.: Natural image bases to represent neuroimaging data. In: International Conference on Machine Learning, pp. 987\u2013994. PMLR (2013)"},{"key":"21_CR6","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)","DOI":"10.1109\/CVPR.2016.90"},{"key":"21_CR7","doi-asserted-by":"crossref","unstructured":"He, Y., Chen, Z., Evans, A.: Structural insights into aberrant topological patterns of large-scale cortical networks in Alzheimer\u2019s disease. J. Neurosci. 28(18), 4756\u20134766 (2008)","DOI":"10.1523\/JNEUROSCI.0141-08.2008"},{"key":"21_CR8","doi-asserted-by":"crossref","unstructured":"Hosseini-Asl, E., Keynton, R., El-Baz, A.: Alzheimer\u2019s disease diagnostics by adaptation of 3D convolutional network. In: 2016 IEEE International Conference on Image Processing (ICIP), pp. 126\u2013130. IEEE (2016)","DOI":"10.1109\/ICIP.2016.7532332"},{"key":"21_CR9","doi-asserted-by":"crossref","unstructured":"Jia, J., et al.: The cost of Alzheimer\u2019s disease in china and re-estimation of costs worldwide. Alzheimer\u2019s Dementia 14(4), 483\u2013491 (2018)","DOI":"10.1016\/j.jalz.2017.12.006"},{"key":"21_CR10","unstructured":"Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems, vol. 25 (2012)"},{"key":"21_CR11","doi-asserted-by":"publisher","first-page":"101","DOI":"10.1016\/j.compmedimag.2018.09.009","volume":"70","author":"F Li","year":"2018","unstructured":"Li, F., Liu, M., Initiative, A.D.N., et al.: Alzheimer\u2019s disease diagnosis based on multiple cluster dense convolutional networks. Comput. Med. Imaging Graph. 70, 101\u2013110 (2018)","journal-title":"Comput. Med. Imaging Graph."},{"issue":"2","key":"21_CR12","doi-asserted-by":"publisher","first-page":"1489","DOI":"10.1109\/TPAMI.2022.3164083","volume":"45","author":"Y Li","year":"2022","unstructured":"Li, Y., Yao, T., Pan, Y., Mei, T.: Contextual transformer networks for visual recognition. IEEE Trans. Pattern Anal. Mach. Intell. 45(2), 1489\u20131500 (2022)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"issue":"11","key":"21_CR13","doi-asserted-by":"publisher","first-page":"2126","DOI":"10.1002\/pro.3275","volume":"26","author":"B Nizynski","year":"2017","unstructured":"Nizynski, B., Dzwolak, W., Nieznanski, K.: Amyloidogenesis of tau protein. Protein Sci. 26(11), 2126\u20132150 (2017)","journal-title":"Protein Sci."},{"key":"21_CR14","unstructured":"Parmar, H., Walden, E.: Towards practical application of deep learning in diagnosis of Alzheimer\u2019s disease. arXiv preprint arXiv:2212.04528 (2022)"},{"issue":"3","key":"21_CR15","doi-asserted-by":"publisher","first-page":"303","DOI":"10.1001\/archneur.56.3.303","volume":"56","author":"RC Petersen","year":"1999","unstructured":"Petersen, R.C., Smith, G.E., Waring, S.C., Ivnik, R.J., Tangalos, E.G., Kokmen, E.: Mild cognitive impairment: clinical characterization and outcome. Arch. Neurol. 56(3), 303\u2013308 (1999)","journal-title":"Arch. Neurol."},{"key":"21_CR16","doi-asserted-by":"crossref","unstructured":"Pini, L., et al.: Brain atrophy in Alzheimer\u2019s disease and aging. Ageing Res. Rev. 30, 25\u201348 (2016)","DOI":"10.1016\/j.arr.2016.01.002"},{"key":"21_CR17","unstructured":"Prince, M., Wimo, A., Guerchet, M., Ali, G.C., Wu, Y.T., Prina, M.: World Alzheimer report 2015. The global impact of dementia: an analysis of prevalence, incidence, cost and trends. Ph.D. thesis, Alzheimer\u2019s Disease International (2015)"},{"key":"21_CR18","unstructured":"Raschka, S.: An overview of general performance metrics of binary classifier systems. arXiv preprint arXiv:1410.5330 (2014)"},{"key":"21_CR19","doi-asserted-by":"crossref","unstructured":"Szegedy, C., et al.: Going deeper with convolutions. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp.\u00a01\u20139 (2015)","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"21_CR20","unstructured":"Vaswani, A., et al.: Attention is all you need. In: Advances in Neural Information Processing Systems, vol. 30 (2017)"},{"key":"21_CR21","unstructured":"Zhang, Z., Sabuncu, M.: Generalized cross entropy loss for training deep neural networks with noisy labels. In: Advances in Neural Information Processing Systems, vol. 31 (2018)"}],"container-title":["Lecture Notes in Computer Science","AI for Clinical Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-032-06004-4_21","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,22]],"date-time":"2025-09-22T17:21:38Z","timestamp":1758561698000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-032-06004-4_21"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,9,22]]},"ISBN":["9783032060037","9783032060044"],"references-count":21,"URL":"https:\/\/doi.org\/10.1007\/978-3-032-06004-4_21","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,9,22]]},"assertion":[{"value":"22 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 that are relevant to the content of this article.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Disclosure of Interests"}},{"value":"CREATE","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Workshop on Clinical-Driven Robotics and Embodied AI Technology","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":"27 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":"1","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"create2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/sites.google.com\/view\/create-2025\/home","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}