{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,22]],"date-time":"2025-12-22T18:35:10Z","timestamp":1766428510501,"version":"3.40.4"},"publisher-location":"Singapore","reference-count":26,"publisher":"Springer Nature Singapore","isbn-type":[{"value":"9789819658831","type":"print"},{"value":"9789819658848","type":"electronic"}],"license":[{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"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":[[2025]]},"DOI":"10.1007\/978-981-96-5884-8_5","type":"book-chapter","created":{"date-parts":[[2025,4,25]],"date-time":"2025-04-25T13:29:50Z","timestamp":1745587790000},"page":"59-74","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Vital-Net: Vision Integrated Transformer and\u00a0Attention Network for\u00a0Lung Nodule Segmentation on\u00a0Full-Scale Images"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0009-0009-8890-6774","authenticated-orcid":false,"given":"Devin","family":"Lautan","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yi-Yu","family":"Hsu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,4,17]]},"reference":[{"issue":"3","key":"5_CR1","doi-asserted-by":"publisher","first-page":"229","DOI":"10.3322\/caac.21834","volume":"74","author":"F Bray","year":"2024","unstructured":"Bray, F., et al.: Global cancer statistics 2022: Globocan estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA: Cancer J. Clini. 74(3), 229\u2013263 (2024). https:\/\/doi.org\/10.3322\/caac.21834","journal-title":"CA: Cancer J. Clini."},{"issue":"3","key":"5_CR2","doi-asserted-by":"publisher","first-page":"2043","DOI":"10.3390\/biomedinformatics4030111","volume":"4","author":"I Marinakis","year":"2024","unstructured":"Marinakis, I., Karampidis, K., Papadourakis, G.: Pulmonary nodule detection, segmentation and classification using deep learning: a comprehensive literature review. BioMedInformatics 4(3), 2043\u20132106 (2024). https:\/\/doi.org\/10.3390\/biomedinformatics4030111","journal-title":"BioMedInformatics"},{"key":"5_CR3","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"234","DOI":"10.1007\/978-3-319-24574-4_28","volume-title":"Medical Image Computing and Computer-Assisted Intervention \u2013 MICCAI 2015","author":"O Ronneberger","year":"2015","unstructured":"Ronneberger, O., Fischer, P., Brox, T.: U-Net: convolutional networks for biomedical image segmentation. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9351, pp. 234\u2013241. Springer, Cham (2015). https:\/\/doi.org\/10.1007\/978-3-319-24574-4_28"},{"key":"5_CR4","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1007\/978-3-030-00889-5_1","volume-title":"Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support","author":"Z Zhou","year":"2018","unstructured":"Zhou, Z., Rahman Siddiquee, M.M., Tajbakhsh, N., Liang, J.: UNet++: a nested U-Net architecture for medical image segmentation. In: Stoyanov, D., et al. (eds.) DLMIA\/ML-CDS -2018. LNCS, vol. 11045, pp. 3\u201311. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-00889-5_1"},{"key":"5_CR5","doi-asserted-by":"publisher","first-page":"94","DOI":"10.1016\/j.isprsjprs.2020.01.013","volume":"162","author":"FI Diakogiannis","year":"2020","unstructured":"Diakogiannis, F.I., Waldner, F., Caccetta, P., Wu, C.: ResUNet-a: a deep learning framework for semantic segmentation of remotely sensed data. ISPRS J. Photogramm. Remote. Sens. 162, 94\u2013114 (2020). https:\/\/doi.org\/10.1016\/j.isprsjprs.2020.01.013","journal-title":"ISPRS J. Photogramm. Remote. Sens."},{"issue":"3","key":"5_CR6","doi-asserted-by":"publisher","first-page":"1616","DOI":"10.1007\/s10489-020-01914-x","volume":"51","author":"MH Hesamian","year":"2020","unstructured":"Hesamian, M.H., Jia, W., He, X., Wang, Q., Kennedy, P.J.: Synthetic CT images for semi-sequential detection and segmentation of lung nodules. Appl. Intell. 51(3), 1616\u20131628 (2020). https:\/\/doi.org\/10.1007\/s10489-020-01914-x","journal-title":"Appl. Intell."},{"key":"5_CR7","doi-asserted-by":"publisher","DOI":"10.1016\/j.cmpb.2020.105792","volume":"198","author":"G Pezzano","year":"2020","unstructured":"Pezzano, G., Ripoll, V.R., Radeva, P.: CoLe-CNN: context-learning convolutional neural network with adaptive loss function for lung nodule segmentation. Comput. Methods Programs Biomed. 198, 105792 (2020). https:\/\/doi.org\/10.1016\/j.cmpb.2020.105792","journal-title":"Comput. Methods Programs Biomed."},{"key":"5_CR8","doi-asserted-by":"publisher","unstructured":"Wu, Z., Li, X., Zuo, J.: RAD-UNet: research on an improved lung nodule semantic segmentation algorithm based on deep learning. Front. Oncol. 13 (2023). https:\/\/doi.org\/10.3389\/fonc.2023.1084096","DOI":"10.3389\/fonc.2023.1084096"},{"issue":"4","key":"5_CR9","doi-asserted-by":"publisher","first-page":"834","DOI":"10.1109\/tpami.2017.2699184","volume":"40","author":"L Chen","year":"2017","unstructured":"Chen, L., Papandreou, G., Kokkinos, I., Murphy, K., Yuille, A.L.: DeepLab: semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected CRFs. IEEE Trans. Pattern Anal. Mach. Intell. 40(4), 834\u2013848 (2017). https:\/\/doi.org\/10.1109\/tpami.2017.2699184","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"issue":"12","key":"5_CR10","doi-asserted-by":"publisher","first-page":"2481","DOI":"10.1109\/tpami.2016.2644615","volume":"39","author":"V Badrinarayanan","year":"2017","unstructured":"Badrinarayanan, V., Kendall, A., Cipolla, R.: SEGNet: a deep convolutional Encoder-Decoder architecture for image segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 39(12), 2481\u20132495 (2017). https:\/\/doi.org\/10.1109\/tpami.2016.2644615","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"5_CR11","doi-asserted-by":"publisher","first-page":"31","DOI":"10.1016\/j.patrec.2019.03.004","volume":"123","author":"R Roy","year":"2019","unstructured":"Roy, R., Chakraborti, T., Chowdhury, A.S.: A deep learning-shape driven level set synergism for pulmonary nodule segmentation. Pattern Recogn. Lett. 123, 31\u201338 (2019). https:\/\/doi.org\/10.1016\/j.patrec.2019.03.004","journal-title":"Pattern Recogn. Lett."},{"issue":"3","key":"5_CR12","doi-asserted-by":"publisher","first-page":"678","DOI":"10.1007\/s10278-019-00301-4","volume":"33","author":"G Singadkar","year":"2020","unstructured":"Singadkar, G., Mahajan, A., Thakur, M., Talbar, S.: Deep deconvolutional residual network based automatic lung nodule segmentation. J. Digit. Imaging 33(3), 678\u2013684 (2020). https:\/\/doi.org\/10.1007\/s10278-019-00301-4","journal-title":"J. Digit. Imaging"},{"issue":"4","key":"5_CR13","doi-asserted-by":"publisher","first-page":"1989","DOI":"10.3390\/s23041989","volume":"23","author":"M Usman","year":"2023","unstructured":"Usman, M., Shin, Y.: DEHA-Net: a dual-encoder-based hard attention network with an Adaptive ROI mechanism for lung nodule segmentation. Sensors 23(4), 1989 (2023). https:\/\/doi.org\/10.3390\/s23041989","journal-title":"Sensors"},{"key":"5_CR14","doi-asserted-by":"publisher","unstructured":"Yadav DP, Sharma B, Webber JL, Mehbodniya A, Chauhan S. EDTNet: a spatial aware attention-based transformer for the pulmonary nodule segmentation. PLoS One 15,19(11), e0311080 (2024 ). https:\/\/doi.org\/10.1371\/journal.pone.0311080., PMID: 39546546; PMCID: PMC11567627","DOI":"10.1371\/journal.pone.0311080."},{"key":"5_CR15","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2022.106330","volume":"151","author":"S Wang","year":"2022","unstructured":"Wang, S., Jiang, A., Li, X., Qiu, Y., Li, M., Li, F.: DPBET: a dual-path lung nodules segmentation model based on boundary enhancement and hybrid transformer. Comput. Biol. Med. 151, 106330 (2022). https:\/\/doi.org\/10.1016\/j.compbiomed.2022.106330","journal-title":"Comput. Biol. Med."},{"issue":"8","key":"5_CR16","doi-asserted-by":"publisher","first-page":"1929","DOI":"10.1007\/s11517-023-02852-9","volume":"61","author":"X Li","year":"2023","unstructured":"Li, X., Jiang, A., Qiu, Y., Li, M., Zhang, X., Yan, S.: TPFR-Net: U-shaped model for lung nodule segmentation based on transformer pooling and dual-attention feature reorganization. Med. Biol. Eng. Comput. 61(8), 1929\u20131946 (2023). https:\/\/doi.org\/10.1007\/s11517-023-02852-9","journal-title":"Med. Biol. Eng. Comput."},{"key":"5_CR17","unstructured":"Dosovitskiy, A., et al.: An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale. ArXiv, abs\/ arXiv: 2010.11929 (2020)"},{"key":"5_CR18","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 770\u2013778 (2015)","DOI":"10.1109\/CVPR.2016.90"},{"key":"5_CR19","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"421","DOI":"10.1007\/978-3-030-00928-1_48","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2018","author":"AG Roy","year":"2018","unstructured":"Roy, A.G., Navab, N., Wachinger, C.: Concurrent spatial and channel Squeeze & excitation\u2019 in fully convolutional networks. In: Frangi, A.F., Schnabel, J.A., Davatzikos, C., Alberola-L\u00f3pez, C., Fichtinger, G. (eds.) MICCAI 2018. LNCS, vol. 11070, pp. 421\u2013429. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-00928-1_48"},{"key":"5_CR20","unstructured":"Vaswani, A., et al.: Attention is All you Need, vol 30, pp. 5998-6008. arXiv (Cornell University), arxiv:1706.03762v5 (2017)"},{"key":"5_CR21","unstructured":"Lai, T.: Lung Nodule Detection Based on The Residual U-Net with Multi-attention and Multi-scale Feature Fusion. https:\/\/hdl.handle.net\/11296\/wrw32s"},{"issue":"2","key":"5_CR22","doi-asserted-by":"publisher","first-page":"915","DOI":"10.1118\/1.3528204","volume":"38","author":"SG Armato","year":"2011","unstructured":"Armato, S.G., et al.: The lung image database consortium (LIDC) and image database resource initiative (IDRI): a completed reference database of lung nodules on CT scans. Med. Phys. 38(2), 915\u2013931 (2011). https:\/\/doi.org\/10.1118\/1.3528204","journal-title":"Med. Phys."},{"issue":"5","key":"5_CR23","doi-asserted-by":"publisher","first-page":"e93S","DOI":"10.1378\/chest.12-2351","volume":"143","author":"MK Gould","year":"2013","unstructured":"Gould, M.K., et al.: Evaluation of individuals with pulmonary nodules: when is it lung cancer? Chest J. 143(5), e93S-e120S (2013). https:\/\/doi.org\/10.1378\/chest.12-2351","journal-title":"Chest J."},{"issue":"146","key":"5_CR24","doi-asserted-by":"publisher","DOI":"10.1183\/16000617.0025-2017","volume":"26","author":"AR Larici","year":"2017","unstructured":"Larici, A.R., et al.: Lung nodules: size still matters. Eur. Respir. Rev. 26(146), 170025 (2017). https:\/\/doi.org\/10.1183\/16000617.0025-2017","journal-title":"Eur. Respir. Rev."},{"key":"5_CR25","doi-asserted-by":"publisher","unstructured":"Azad, R., Asadi-Aghbolaghi, M., Fathy, M., Escalera, S.: Bi-directional ConvLSTM U-net with densley connected convolutions. In: 2019 IEEE\/CVF International Conference on Computer Vision Workshop (ICCVW), Seoul, Korea (South), pp. 406-415 (2019). https:\/\/doi.org\/10.1109\/ICCVW.2019.00052.","DOI":"10.1109\/ICCVW.2019.00052."},{"key":"5_CR26","doi-asserted-by":"publisher","unstructured":"Chen, J., et al.: TransUNET: Transformers make strong encoders for medical image segmentation. arXiv (Cornell University) (2021). https:\/\/doi.org\/10.48550\/arxiv.2102.04306","DOI":"10.48550\/arxiv.2102.04306"}],"container-title":["Communications in Computer and Information Science","Recent Challenges in Intelligent Information and Database Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-96-5884-8_5","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,4,25]],"date-time":"2025-04-25T13:29:53Z","timestamp":1745587793000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-96-5884-8_5"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025]]},"ISBN":["9789819658831","9789819658848"],"references-count":26,"URL":"https:\/\/doi.org\/10.1007\/978-981-96-5884-8_5","relation":{},"ISSN":["1865-0929","1865-0937"],"issn-type":[{"value":"1865-0929","type":"print"},{"value":"1865-0937","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025]]},"assertion":[{"value":"17 April 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":"ACIIDS","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Asian Conference on Intelligent Information and Database Systems","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Kitakyushu","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Japan","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 April 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"25 April 2025","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"17","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"aciids2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/aciids.pwr.edu.pl\/2025\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}