{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,16]],"date-time":"2025-11-16T20:51:21Z","timestamp":1763326281694,"version":"3.45.0"},"reference-count":22,"publisher":"Springer Science and Business Media LLC","issue":"4","license":[{"start":{"date-parts":[[2025,8,7]],"date-time":"2025-08-07T00:00:00Z","timestamp":1754524800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,8,7]],"date-time":"2025-08-07T00:00:00Z","timestamp":1754524800000},"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":["Artif Life Robotics"],"published-print":{"date-parts":[[2025,11]]},"DOI":"10.1007\/s10015-025-01051-z","type":"journal-article","created":{"date-parts":[[2025,8,7]],"date-time":"2025-08-07T16:57:09Z","timestamp":1754585829000},"page":"643-652","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Pre-training Deep Neural Networks Using 3D Formula-Driven Supervised Learning for COPD Staging"],"prefix":"10.1007","volume":"30","author":[{"given":"Yasumasa","family":"Tamura","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kohei","family":"Harada","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wataru","family":"Noguchi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kaoruko","family":"Shimizu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Satoshi","family":"Konno","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Masahito","family":"Yamamoto","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,8,7]]},"reference":[{"issue":"1","key":"1051_CR1","doi-asserted-by":"publisher","first-page":"121","DOI":"10.1038\/s41597-021-00900-3","volume":"8","author":"P Afshar","year":"2021","unstructured":"Afshar P, Heidarian S, Enshaei N, Naderkhani F, Rafiee MJ, Oikonomou A, Fard FB, Samimi K, Plataniotis KN, Mohammadi A (2021) COVID-CT-MD, COVID-19 computed tomography scan dataset applicable in machine learning and deep learning. Sci Data 8(1):121. https:\/\/doi.org\/10.1038\/s41597-021-00900-3","journal-title":"Sci Data"},{"key":"1051_CR2","doi-asserted-by":"publisher","unstructured":"Arnab A, Dehghani M, Heigold G, Sun C, Lucic M, Schmid C (2021) ViViT: a video vision transformer. In: 2021 IEEE\/CVF international conference on computer vision (ICCV), 6816\u20136826. https:\/\/doi.org\/10.1109\/ICCV48922.2021.00676","DOI":"10.1109\/ICCV48922.2021.00676"},{"issue":"3","key":"1051_CR3","doi-asserted-by":"publisher","first-page":"286","DOI":"10.1164\/rccm.201807-1351SO","volume":"199","author":"SP Bhatt","year":"2019","unstructured":"Bhatt SP, Washko GR, Hoffman EA, Newell JD Jr, Bodduluri S, Diaz AA, Galban CJ, Silverman EK, Est\u00e9par RSJ, Lynch DA (2019) Imaging advances in chronic obstructive pulmonary disease. insights from the genetic epidemiology of chronic obstructive pulmonary disease (COPDGene) study. Am J Respir Crit Care Med 199(3):286\u2013301. https:\/\/doi.org\/10.1164\/rccm.201807-1351SO","journal-title":"Am J Respir Crit Care Med"},{"key":"1051_CR4","doi-asserted-by":"publisher","first-page":"9","DOI":"10.1016\/j.ejmp.2021.02.006","volume":"83","author":"I Castiglioni","year":"2021","unstructured":"Castiglioni I, Rundo L, Codari M, Di Leo G, Salvatore C, Interlenghi M, Gallivanone F, Cozzi A, D\u2019Amico NC, Sardanelli F (2021) AI applications to medical images: from machine learning to deep learning. Physica Med 83:9\u201324. https:\/\/doi.org\/10.1016\/j.ejmp.2021.02.006","journal-title":"Physica Med"},{"issue":"2","key":"1051_CR5","doi-asserted-by":"publisher","first-page":"192","DOI":"10.4103\/ijph.ijph_726_23","volume":"67","author":"PP Doke","year":"2023","unstructured":"Doke PP (2023) Chronic respiratory disease: a rapidly emerging public health menace. Indian J Public Health 67(2):192\u2013196. https:\/\/doi.org\/10.4103\/ijph.ijph_726_23","journal-title":"Indian J Public Health"},{"key":"1051_CR6","doi-asserted-by":"publisher","first-page":"105464","DOI":"10.1016\/j.compbiomed.2022.105464","volume":"145","author":"M Fallahpoor","year":"2022","unstructured":"Fallahpoor M, Chakraborty S, Heshejin MT, Chegeni H, Horry MJ, Pradhan B (2022) Generalizability assessment of COVID-19 3D CT data for deep learning-based disease detection. Comput Biol Med 145:105464. https:\/\/doi.org\/10.1016\/j.compbiomed.2022.105464","journal-title":"Comput Biol Med"},{"issue":"4","key":"1051_CR7","doi-asserted-by":"publisher","first-page":"279","DOI":"10.1016\/j.jormas.2019.06.002","volume":"120","author":"A Fourcade","year":"2019","unstructured":"Fourcade A, Khonsari R (2019) Deep learning in medical image analysis: a third eye for doctors. J Stomatol Oral Maxillofac Surg 120(4):279\u2013288. https:\/\/doi.org\/10.1016\/j.jormas.2019.06.002","journal-title":"J Stomatol Oral Maxillofac Surg"},{"key":"1051_CR8","doi-asserted-by":"publisher","unstructured":"Gao X, Khan MHM, Hui R, Tian Z, Qian Y, Gao A, Baichoo S (2022) COVID-VIT: classification of covid-19 from 3D CT chest images based on vision transformer model. In: 2022 3rd international conference on next generation computing applications (NextComp), 1\u20134. https:\/\/doi.org\/10.1109\/NextComp55567.2022.9932246","DOI":"10.1109\/NextComp55567.2022.9932246"},{"key":"1051_CR9","doi-asserted-by":"publisher","first-page":"101,936","DOI":"10.1016\/j.eclinm.2023.101936","volume":"59","author":"GBD 2019 Chronic Respiratory Diseases Collaborators","year":"2023","unstructured":"GBD 2019 Chronic Respiratory Diseases Collaborators (2023) Global burden of chronic respiratory diseases and risk factors, 1990 2019: an update from the global burden of disease study 2019. eClinicalMedicine 59:101,936. https:\/\/doi.org\/10.1016\/j.eclinm.2023.101936","journal-title":"eClinicalMedicine"},{"key":"1051_CR10","unstructured":"Harada K, Noguchi W, Tamura Y, Shimizu K, Konno S, Yamamoto M (2025) Pre-training deep neural networks with 3D fractal structures for COPD stage classification. In: 30th international symposium on artificial life and robotics (AROB 30th 2025), 640\u2013645"},{"key":"1051_CR11","doi-asserted-by":"publisher","unstructured":"He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: 2016 IEEE conference on computer vision and pattern recognition, 770\u2013778. https:\/\/doi.org\/10.1109\/CVPR.2016.90","DOI":"10.1109\/CVPR.2016.90"},{"key":"1051_CR12","doi-asserted-by":"publisher","unstructured":"Huang G, Liu Z, van der Maaten L, Weinberger KQ (2017) Densely connected convolutional networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, 2261\u20132269. https:\/\/doi.org\/10.1109\/CVPR.2017.243","DOI":"10.1109\/CVPR.2017.243"},{"issue":"C","key":"1051_CR13","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2022.106137","volume":"150","author":"Y Huang","year":"2022","unstructured":"Huang Y, Si Y, Hu B, Zhang Y, Wu S, Wu D, Wang Q (2022) Transformer-based factorized encoder for classification of pneumoconiosis on 3D CT images. Comput Biol Med 150(C):106137. https:\/\/doi.org\/10.1016\/j.compbiomed.2022.106137","journal-title":"Comput Biol Med"},{"issue":"5","key":"1051_CR14","doi-asserted-by":"publisher","first-page":"713","DOI":"10.1512\/iumj.1981.30.30055","volume":"30","author":"JE Hutchison","year":"1981","unstructured":"Hutchison JE (1981) Fractals and self similarity. Indian Univ Math J 30(5):713\u2013747","journal-title":"Indian Univ Math J"},{"key":"1051_CR15","doi-asserted-by":"publisher","first-page":"990 1007","DOI":"10.1007\/s11263-021-01555-8","volume":"130","author":"H Kataoka","year":"2022","unstructured":"Kataoka H, Okayasu K, Matsumoto A, Yamagata E, Yamada R, Inoue N, Nakamura A, Satoh Y (2022) Pre-training without natural images. Int J Comput Vision (IJCV) 130:990 1007. https:\/\/doi.org\/10.1007\/s11263-021-01555-8","journal-title":"Int J Comput Vision (IJCV)"},{"key":"1051_CR16","doi-asserted-by":"publisher","unstructured":"Kingma DP, Ba J (2015) Adam: a method for stochastic optimization. In: 3rd International conference on learning representations, ICLR 2015. https:\/\/doi.org\/10.48550\/arXiv.1412.6980","DOI":"10.48550\/arXiv.1412.6980"},{"key":"1051_CR17","doi-asserted-by":"publisher","unstructured":"Liu Z, Ning J, Cao Y, Wei Y, Zhang Z, Lin S, Hu H (2022) Video swin transformer. In: 2022 IEEE\/CVF conference on computer vision and pattern recognition (CVPR), 3192\u20133201. https:\/\/doi.org\/10.1109\/CVPR52688.2022.00320","DOI":"10.1109\/CVPR52688.2022.00320"},{"issue":"1","key":"1051_CR18","doi-asserted-by":"publisher","first-page":"192","DOI":"10.1148\/radiol.2015141579","volume":"277","author":"DA Lynch","year":"2015","unstructured":"Lynch DA, Austin JHM, Hogg JC, Grenier PA, Kauczor HU, Bankier AA, Barr RG, Colby TV, Galvin JR, Gevenois PA, Coxson HO, Hoffman EA, Newell JD Jr, Pistolesi M, Silverman EK, Crapo JDC (2015) CT-Definable subtypes of chronic obstructive pulmonary disease: a statement of the fleischner society. Radiology 277(1):192\u2013205. https:\/\/doi.org\/10.1148\/radiol.2015141579","journal-title":"Radiology"},{"issue":"3","key":"1051_CR19","doi-asserted-by":"publisher","first-page":"287","DOI":"10.1145\/325165.325247","volume":"19","author":"K Perlin","year":"1985","unstructured":"Perlin K (1985) An image synthesizer. SIGGRAPH Comput Graph 19(3):287\u2013296. https:\/\/doi.org\/10.1145\/325165.325247","journal-title":"SIGGRAPH Comput Graph"},{"key":"1051_CR20","doi-asserted-by":"publisher","first-page":"21","DOI":"10.1186\/s41235-019-0171-6","volume":"4","author":"LH Williams","year":"2019","unstructured":"Williams LH, Drew T (2019) What do we know about volumetric medical image interpretation?: A review of the basic science and medical image perception literatures. Cognitive Res Princ Implic 4:21. https:\/\/doi.org\/10.1186\/s41235-019-0171-6","journal-title":"Cognitive Res Princ Implic"},{"key":"1051_CR21","doi-asserted-by":"publisher","unstructured":"Yamada R, Kataoka H, Chiba N, Domae Y, Ogata T (2022) Point cloud pre-training with natural 3D structures. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition (CVPR), 21,283\u201321,293. https:\/\/doi.org\/10.1109\/CVPR52688.2022.02060","DOI":"10.1109\/CVPR52688.2022.02060"},{"key":"1051_CR22","doi-asserted-by":"publisher","unstructured":"Yamada R, Takahashi R, Suzuki R, Nakamura A, Yoshiyasu Y, Sagawa R, Kataoka H (2021) MV-FractalDB: formula-driven supervised learning for multi-view image recognition. In: 2021 IEEE\/RSJ international conference on intelligent robots and systems (IROS), 2076\u20132083. https:\/\/doi.org\/10.1109\/IROS51168.2021.9635946","DOI":"10.1109\/IROS51168.2021.9635946"}],"container-title":["Artificial Life and Robotics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10015-025-01051-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10015-025-01051-z\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10015-025-01051-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,11,16]],"date-time":"2025-11-16T20:48:08Z","timestamp":1763326088000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10015-025-01051-z"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,8,7]]},"references-count":22,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2025,11]]}},"alternative-id":["1051"],"URL":"https:\/\/doi.org\/10.1007\/s10015-025-01051-z","relation":{},"ISSN":["1433-5298","1614-7456"],"issn-type":[{"type":"print","value":"1433-5298"},{"type":"electronic","value":"1614-7456"}],"subject":[],"published":{"date-parts":[[2025,8,7]]},"assertion":[{"value":"14 April 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"14 July 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"7 August 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}