{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,19]],"date-time":"2025-09-19T11:35:02Z","timestamp":1758281702081,"version":"3.44.0"},"publisher-location":"Cham","reference-count":24,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783032051615","type":"print"},{"value":"9783032051622","type":"electronic"}],"license":[{"start":{"date-parts":[[2025,9,19]],"date-time":"2025-09-19T00:00:00Z","timestamp":1758240000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,9,19]],"date-time":"2025-09-19T00:00:00Z","timestamp":1758240000000},"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\/978-3-032-05162-2_53","type":"book-chapter","created":{"date-parts":[[2025,9,18]],"date-time":"2025-09-18T23:26:35Z","timestamp":1758237995000},"page":"554-563","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["PRGNN: Pyramidal Region Graph Neural Network for\u00a0Region-Based Brain PET Classification"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0009-0006-7966-947X","authenticated-orcid":false,"given":"Daesung","family":"Kim","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-9779-3757","authenticated-orcid":false,"given":"Seungbeom","family":"Seo","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Boosung","family":"Kim","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kyobin","family":"Choo","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Youngjun","family":"Jun","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mijin","family":"Yun","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,9,19]]},"reference":[{"key":"53_CR1","doi-asserted-by":"crossref","unstructured":"Perovnik, M., Rus, T., Schindlbeck, K.A., Eidelberg, D.: Functional brain networks in the evaluation of patients with neurodegenerative disorders. Nat. Rev. Neurol. 19(2), 73\u201390 (2023)","DOI":"10.1038\/s41582-022-00753-3"},{"key":"53_CR2","doi-asserted-by":"crossref","unstructured":"Minoshima, S., Cross, D., Thientunyakit, T., Foster, N.L., Drzezga, A.: 18F-FDG pet imaging in neurodegenerative dementing disorders: insights into subtype classification, emerging disease categories, and mixed dementia with copathologies. J. Nuclear Med. 63(Supplement 1), 2S\u201312S (2022)","DOI":"10.2967\/jnumed.121.263194"},{"key":"53_CR3","unstructured":"Collij, L.E., et\u00a0al.: Spatial-temporal patterns of $$\\beta $$-amyloid accumulation: a subtype and stage inference model analysis. Neurology 98(17), e1692\u2013e1703 (2022)"},{"key":"53_CR4","doi-asserted-by":"crossref","unstructured":"Fan, S., et\u00a0al.: AmyloidPETNet: classification of amyloid positivity in brain pet imaging using end-to-end deep learning. Radiology 311(3), e231442 (2024)","DOI":"10.1148\/radiol.231442"},{"key":"53_CR5","doi-asserted-by":"crossref","unstructured":"Etminani, K., et\u00a0al.: A 3D deep learning model to predict the diagnosis of dementia with Lewy bodies, Alzheimer\u2019s disease, and mild cognitive impairment using brain 18F-FDG pet. Eur. J. Nuclear Med. Mol. Imaging, 1\u201322 (2022)","DOI":"10.1007\/s00259-021-05483-0"},{"key":"53_CR6","doi-asserted-by":"crossref","unstructured":"Jang, J., Hwang, D.: M3T: three-dimensional medical image classifier using multi-plane and multi-slice transformer. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 20718\u201320729 (2022)","DOI":"10.1109\/CVPR52688.2022.02006"},{"key":"53_CR7","doi-asserted-by":"publisher","unstructured":"Jiang, H., Miao, C.: Anatomy-aware gating network for explainable Alzheimer\u2019s disease diagnosis. In: Linguraru, M.G., et al. (eds.) MICCAI 2024. LNCS, vol. 15005, pp. 90\u2013100. Springer, Cham (2024). https:\/\/doi.org\/10.1007\/978-3-031-72086-4_9","DOI":"10.1007\/978-3-031-72086-4_9"},{"key":"53_CR8","doi-asserted-by":"crossref","unstructured":"Hill, B.G., Koback, F.L., Schilling, P.L.: The risk of shortcutting in deep learning algorithms for medical imaging research. Sci. Rep. 14(1), 29224 (2024)","DOI":"10.1038\/s41598-024-79838-6"},{"key":"53_CR9","unstructured":"Dosovitskiy, A., et\u00a0al.: An image is worth 16x16 words: transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020)"},{"issue":"1","key":"53_CR10","doi-asserted-by":"publisher","first-page":"84","DOI":"10.1007\/s10916-024-02105-8","volume":"48","author":"S Takahashi","year":"2024","unstructured":"Takahashi, S., et al.: Comparison of vision transformers and convolutional neural networks in medical image analysis: a systematic review. J. Med. Syst. 48(1), 84 (2024)","journal-title":"J. Med. Syst."},{"key":"53_CR11","doi-asserted-by":"crossref","unstructured":"Li, X., et al.: BrainGNN: interpretable brain graph neural network for fMRI analysis. Med. Image Anal. 74, 102233 (2021)","DOI":"10.1016\/j.media.2021.102233"},{"key":"53_CR12","doi-asserted-by":"crossref","unstructured":"Mueller, K., Meyer-Baese, A., Erlebacher, G.: Combining graph neural networks and ROI-based convolutional neural networks to infer individualized graphs for Alzheimer\u2019s prediction. In: Medical Imaging 2023: Biomedical Applications in Molecular, Structural, and Functional Imaging, vol. 12468, pp. 25\u201330. SPIE (2023)","DOI":"10.1117\/12.2654445"},{"key":"53_CR13","doi-asserted-by":"publisher","unstructured":"Sim, J., Lee, M., Wu, G., Kim, W.H.: Multi-modal graph neural network with transformer-guided adaptive diffusion for preclinical Alzheimer classification. In: Linguraru, M.G., et al. (eds.) MICCAI 2024. LNCS, vol. 15005, pp. 511\u2013521. Springer, Cham (2024). https:\/\/doi.org\/10.1007\/978-3-031-72086-4_48","DOI":"10.1007\/978-3-031-72086-4_48"},{"issue":"1","key":"53_CR14","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(1), 273\u2013289 (2002)","journal-title":"Neuroimage"},{"key":"53_CR15","unstructured":"Han, K., Wang, Y., Guo, J., Tang, Y., Enhua, W.: Vision GNN: an image is worth graph of nodes. In: Advances in Neural Information Processing Systems, vol. 35, pp. 8291\u20138303 (2022)"},{"key":"53_CR16","doi-asserted-by":"crossref","unstructured":"Li, G., Muller, M., Thabet, A., Ghanem, B.: DeepGCNs: can GCNs go as deep as CNNs? In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 9267\u20139276 (2019)","DOI":"10.1109\/ICCV.2019.00936"},{"key":"53_CR17","doi-asserted-by":"crossref","unstructured":"McKhann, G.M., et\u00a0al.: The diagnosis of dementia due to Alzheimer\u2019s disease: recommendations from the national institute on aging-Alzheimer\u2019s association workgroups on diagnostic guidelines for Alzheimer\u2019s disease. Alzheimer\u2019s Dementia 7(3), 263\u2013269 (2011)","DOI":"10.1016\/j.jalz.2011.03.005"},{"key":"53_CR18","doi-asserted-by":"crossref","unstructured":"McKeith, I.G., et\u00a0al.: Diagnosis and management of dementia with Lewy bodies: fourth consensus report of the DLB consortium. Neurology 89(1), 88\u2013100 (2017)","DOI":"10.1212\/WNL.0000000000004058"},{"key":"53_CR19","doi-asserted-by":"crossref","unstructured":"H\u00f6glinger, G.U., et\u00a0al.: Clinical diagnosis of progressive supranuclear palsy: the movement disorder society criteria. Mov. Disorders 32(6), 853\u2013864 (2017)","DOI":"10.1002\/mds.26973"},{"key":"53_CR20","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":"53_CR21","doi-asserted-by":"crossref","unstructured":"Jacobs, H.I.L., Van\u00a0Boxtel, M.P.J., Jolles, J., Verhey, F.R.J., Uylings, H.B.M.: Parietal cortex matters in Alzheimer\u2019s disease: an overview of structural, functional and metabolic findings. Neurosci. Biobehav. Rev. 36(1), 297\u2013309 (2012)","DOI":"10.1016\/j.neubiorev.2011.06.009"},{"key":"53_CR22","doi-asserted-by":"crossref","unstructured":"Whitwell, J.L., et al.: 18F-FDG pet in posterior cortical atrophy and dementia with Lewy bodies. J. Nuclear Med. 58(4), 632\u2013638 (2017)","DOI":"10.2967\/jnumed.116.179903"},{"key":"53_CR23","doi-asserted-by":"crossref","unstructured":"Meyer, P.T., Frings, L., R\u00fccker, G., Hellwig, S.: 18F-FDG pet in parkinsonism: differential diagnosis and evaluation of cognitive impairment. J. Nuclear Med. 58(12), 1888\u20131898 (2017)","DOI":"10.2967\/jnumed.116.186403"},{"key":"53_CR24","doi-asserted-by":"crossref","unstructured":"de\u00a0Jong, L.W.: Strongly reduced volumes of putamen and thalamus in Alzheimer\u2019s disease: an MRI study. Brain 131(12), 3277\u20133285 (2008)","DOI":"10.1093\/brain\/awn278"}],"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\/978-3-032-05162-2_53","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,18]],"date-time":"2025-09-18T23:26:43Z","timestamp":1758238003000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-032-05162-2_53"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,9,19]]},"ISBN":["9783032051615","9783032051622"],"references-count":24,"URL":"https:\/\/doi.org\/10.1007\/978-3-032-05162-2_53","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,9,19]]},"assertion":[{"value":"19 September 2025","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"The authors have no competing interests.","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"}}]}}