{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,17]],"date-time":"2026-07-17T23:03:16Z","timestamp":1784329396037,"version":"3.55.0"},"publisher-location":"Singapore","reference-count":25,"publisher":"Springer Nature Singapore","isbn-type":[{"value":"9789819233939","type":"print"},{"value":"9789819233946","type":"electronic"}],"license":[{"start":{"date-parts":[[2026,7,18]],"date-time":"2026-07-18T00:00:00Z","timestamp":1784332800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,7,18]],"date-time":"2026-07-18T00:00:00Z","timestamp":1784332800000},"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":[[2027]]},"DOI":"10.1007\/978-981-92-3394-6_47","type":"book-chapter","created":{"date-parts":[[2026,7,17]],"date-time":"2026-07-17T22:20:28Z","timestamp":1784326828000},"page":"557-568","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["TGC: A Hybrid Transformer-Gated Convolutional Network with Multi-Dimensional Feature Calibration for Pulmonary Disease Classification in Chest X-ray Images"],"prefix":"10.1007","author":[{"given":"Xiangfu","family":"Meng","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xingbo","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiatong","family":"Cai","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Linlin","family":"Ding","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,7,18]]},"reference":[{"key":"47_CR1","doi-asserted-by":"publisher","first-page":"1459","DOI":"10.2147\/JMDH.S456167","volume":"17","author":"K Alshamrani","year":"2024","unstructured":"Alshamrani, K., Alshamrani, H.A.: Classification of chest CT lung nodules using collaborative deep learning model. J. Multidiscip. Healthc. 17, 1459\u20131472 (2024)","journal-title":"J. Multidiscip. Healthc."},{"issue":"1","key":"47_CR2","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-024-80826-z","volume":"14","author":"CT Yen","year":"2024","unstructured":"Yen, C.T., Tsao, C.Y.: Lightweight convolutional neural network for chest X-ray images classification. Sci. Rep. 14(1), 29759 (2024)","journal-title":"Sci. Rep."},{"issue":"1","key":"47_CR3","doi-asserted-by":"publisher","DOI":"10.1186\/s12911-024-02591-3","volume":"24","author":"J Ko","year":"2024","unstructured":"Ko, J., Park, S., Woo, H.G.: Optimization of vision transformer-based detection of lung diseases from chest X-ray images. BMC Med. Inform. Decis. Mak. 24(1), 191 (2024)","journal-title":"BMC Med. Inform. Decis. Mak."},{"key":"47_CR4","doi-asserted-by":"publisher","DOI":"10.1016\/j.cmpb.2022.107141","volume":"226","author":"GI Okolo","year":"2022","unstructured":"Okolo, G.I., Katsigiannis, S., Ramzan, N.: IEViT: an enhanced vision transformer architecture for chest X-ray image classification. Comput. Methods Prog. Biomed. 226, 107141 (2022)","journal-title":"Comput. Methods Prog. Biomed."},{"issue":"4","key":"47_CR5","doi-asserted-by":"publisher","first-page":"1625","DOI":"10.1007\/s10278-024-01005-0","volume":"37","author":"U Chutia","year":"2024","unstructured":"Chutia, U., Tewari, A.S., Singh, J.P., et al.: Classification of lung diseases using an attention-based modified DenseNet model. J. Imaging Inform. Med. 37(4), 1625\u20131641 (2024)","journal-title":"J. Imaging Inform. Med."},{"key":"47_CR6","doi-asserted-by":"publisher","first-page":"191","DOI":"10.1016\/j.jare.2022.08.021","volume":"48","author":"CC Ukwuoma","year":"2023","unstructured":"Ukwuoma, C.C., et al.: A hybrid explainable ensemble transformer encoder for pneumonia identification from chest X-ray images. J. Adv. Res. 48, 191\u2013211 (2023)","journal-title":"J. Adv. Res."},{"issue":"1","key":"47_CR7","doi-asserted-by":"publisher","first-page":"887","DOI":"10.1038\/s41598-023-28079-0","volume":"13","author":"J Devasia","year":"2023","unstructured":"Devasia, J., et al.: Deep learning classification of active tuberculosis lung zones wise manifestations using chest X-rays: a multi label approach. Sci. Rep. 13(1), 887 (2023)","journal-title":"Sci. Rep."},{"key":"47_CR8","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2021.102125","volume":"72","author":"E \u00c7all\u0131","year":"2021","unstructured":"\u00c7all\u0131, E., et al.: Deep learning for chest X-ray analysis: a survey. Med. Image Anal. 72, 102125 (2021)","journal-title":"Med. Image Anal."},{"key":"47_CR9","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2023.103000","volume":"91","author":"R Azad","year":"2024","unstructured":"Azad, R., et al.: Advances in medical image analysis with vision transformers: a comprehensive review. Med. Image Anal. 91, 103000 (2024)","journal-title":"Med. Image Anal."},{"key":"47_CR10","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2023.102762","volume":"85","author":"J Li","year":"2023","unstructured":"Li, J., et al.: Transforming medical imaging with transformers? A comparative review of key properties, current progresses, and future perspectives. Med. Image Anal. 85, 102762 (2023)","journal-title":"Med. Image Anal."},{"issue":"1","key":"47_CR11","doi-asserted-by":"publisher","DOI":"10.1038\/s41467-023-40260-7","volume":"14","author":"X Zhang","year":"2023","unstructured":"Zhang, X., et al.: Knowledge-enhanced visual-language pre-training on chest radiology images. Nat. Commun. 14(1), 4542 (2023)","journal-title":"Nat. Commun."},{"issue":"1","key":"47_CR12","doi-asserted-by":"publisher","DOI":"10.1038\/s41467-024-51749-0","volume":"15","author":"W Huang","year":"2024","unstructured":"Huang, W., et al.: Enhancing representation in radiography-reports foundation model: a granular alignment algorithm using masked contrastive learning. Nat. Commun. 15(1), 7620 (2024)","journal-title":"Nat. Commun."},{"issue":"1","key":"47_CR13","doi-asserted-by":"publisher","DOI":"10.1038\/s41746-025-02032-z","volume":"8","author":"J Yao","year":"2025","unstructured":"Yao, J., et al.: Eva-x: a foundation model for general chest x-ray analysis with self-supervised learning. NPJ Digit. Med. 8(1), 678 (2025)","journal-title":"NPJ Digit. Med."},{"issue":"1","key":"47_CR14","doi-asserted-by":"publisher","first-page":"3848","DOI":"10.1038\/s41467-022-31514-x","volume":"13","author":"S Park","year":"2022","unstructured":"Park, S., et al.: Self-evolving vision transformer for chest X-ray diagnosis through knowledge distillation. Nat. Commun. 13(1), 3848 (2022)","journal-title":"Nat. Commun."},{"issue":"1","key":"47_CR15","doi-asserted-by":"publisher","DOI":"10.1038\/s41467-023-37758-5","volume":"14","author":"J Weiss","year":"2023","unstructured":"Weiss, J., et al.: Deep learning to estimate lung disease mortality from chest radiographs. Nat. Commun. 14(1), 2797 (2023)","journal-title":"Nat. Commun."},{"issue":"8","key":"47_CR16","doi-asserted-by":"publisher","first-page":"e580","DOI":"10.1016\/S2589-7500(24)00113-4","volume":"6","author":"D Ueda","year":"2024","unstructured":"Ueda, D., et al.: A deep learning-based model to estimate pulmonary function from chest x-rays: multi-institutional model development and validation study in Japan. Lancet Digit. Health. 6(8), e580\u2013e588 (2024)","journal-title":"Lancet Digit. Health"},{"key":"47_CR17","first-page":"9","volume":"7","author":"SD Jorshery","year":"2025","unstructured":"Jorshery, S.D., et al.: Leveraging deep learning applied to chest radiograph images to identify individuals at high risk of chronic obstructive pulmonary disease: a retrospective model validation study. Lancet Digit. Health. 7, 9 (2025)","journal-title":"Lancet Digit. Health"},{"issue":"3","key":"47_CR18","doi-asserted-by":"publisher","DOI":"10.1148\/radiol.231236","volume":"308","author":"L Plesner","year":"2023","unstructured":"Plesner, L., et al.: Commercially available chest radiograph AI tools for detecting airspace disease, pneumothorax, and pleural effusion. Radiology. 308(3), e231236 (2023)","journal-title":"Radiology"},{"issue":"2","key":"47_CR19","doi-asserted-by":"publisher","first-page":"454","DOI":"10.1148\/radiol.212482","volume":"305","author":"AB Sellergren","year":"2022","unstructured":"Sellergren, A.B., et al.: Simplified transfer learning for chest radiography models using less data. Radiology. 305(2), 454\u2013465 (2022)","journal-title":"Radiology"},{"issue":"3","key":"47_CR20","doi-asserted-by":"publisher","DOI":"10.1148\/radiol.222268","volume":"307","author":"LL Plesner","year":"2023","unstructured":"Plesner, L.L., et al.: Autonomous chest radiograph reporting using AI: estimation of clinical impact. Radiology. 307(3), e222268 (2023)","journal-title":"Radiology"},{"issue":"1","key":"47_CR21","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-024-76608-2","volume":"14","author":"PG Anderson","year":"2024","unstructured":"Anderson, P.G., et al.: Deep learning improves physician accuracy in the comprehensive detection of abnormalities on chest X-rays. Sci. Rep. 14(1), 25151 (2024)","journal-title":"Sci. Rep."},{"issue":"10","key":"47_CR22","doi-asserted-by":"publisher","DOI":"10.1001\/jamanetworkopen.2023.36100","volume":"6","author":"J Huang","year":"2023","unstructured":"Huang, J., et al.: Generative artificial intelligence for chest radiograph interpretation in the emergency department. JAMA Netw. Open. 6(10), e2336100 (2023)","journal-title":"JAMA Netw. Open"},{"issue":"1","key":"47_CR23","doi-asserted-by":"publisher","first-page":"e44","DOI":"10.1016\/S2589-7500(23)00218-2","volume":"6","author":"YD Cid","year":"2024","unstructured":"Cid, Y.D., et al.: Development and validation of open-source deep neural networks for comprehensive chest x-ray reading: a retrospective, multicentre study. Lancet Digit. Health. 6(1), e44\u2013e57 (2024)","journal-title":"Lancet Digit. Health"},{"issue":"1","key":"47_CR24","doi-asserted-by":"publisher","first-page":"511","DOI":"10.1038\/s41597-024-03358-1","volume":"11","author":"N Gaggion","year":"2024","unstructured":"Gaggion, N., et al.: CheXmask: a large-scale dataset of anatomical segmentation masks for multi-center chest x-ray images. Sci. Data. 11(1), 511 (2024)","journal-title":"Sci. Data"},{"key":"47_CR25","volume-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition","author":"J Guo","year":"2022","unstructured":"Guo, J., et al.: Cmt: Convolutional neural networks meet vision transformers. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (2022)"}],"container-title":["Lecture Notes in Computer Science","Advanced Intelligent Computing Technology and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-92-3394-6_47","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,7,17]],"date-time":"2026-07-17T22:20:29Z","timestamp":1784326829000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-92-3394-6_47"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,7,18]]},"ISBN":["9789819233939","9789819233946"],"references-count":25,"URL":"https:\/\/doi.org\/10.1007\/978-981-92-3394-6_47","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,7,18]]},"assertion":[{"value":"18 July 2026","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICIC","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Intelligent Computing","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Toronto","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":"2026","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"22 July 2026","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"26 July 2026","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"22","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"icic2026a","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/www.ic-icc.cn\/2026\/index.htm","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}