{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,13]],"date-time":"2026-10-13T18:13:03Z","timestamp":1791915183501,"version":"4.3.4"},"publisher-location":"Cham","reference-count":19,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783032114013","type":"print"},{"value":"9783032114020","type":"electronic"}],"license":[{"start":{"date-parts":[[2025,11,24]],"date-time":"2025-11-24T00:00:00Z","timestamp":1763942400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,11,24]],"date-time":"2025-11-24T00:00:00Z","timestamp":1763942400000},"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\/978-3-032-11402-0_16","type":"book-chapter","created":{"date-parts":[[2025,11,23]],"date-time":"2025-11-23T16:03:12Z","timestamp":1763913792000},"page":"213-226","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["MTL-LungNet: A Unified Model for\u00a0Nodule Segmentation and\u00a0Malignancy Classification"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3260-9705","authenticated-orcid":false,"given":"Mihai","family":"Nan","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-5391-8088","authenticated-orcid":false,"given":"C\u0103t\u0103lin\u2013Mihail","family":"Chiru","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7249-1871","authenticated-orcid":false,"given":"Adina Magda","family":"Florea","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,11,24]]},"reference":[{"issue":"16","key":"16_CR1","doi-asserted-by":"publisher","first-page":"19724","DOI":"10.1007\/s10489-023-04552-1","volume":"53","author":"MH Alshayeji","year":"2023","unstructured":"Alshayeji, M.H., Abed, S.: Lung cancer classification and identification framework with automatic nodule segmentation screening using machine learning. Appl. Intell. 53(16), 19724\u201319741 (2023)","journal-title":"Appl. Intell."},{"key":"16_CR2","unstructured":"Antonelli, M., et al.: The medical segmentation decathlon. Nature Commun. 13(1), 4128 (2022)"},{"key":"16_CR3","unstructured":"Armato\u00a0III, S.G., et\u00a0al.: The lung image database consortium (lidc) and image database resource initiative (idri): a completed reference database of lung nodules on CT scans. https:\/\/www.cancerimagingarchive.net\/collection\/lidc-idri\/ (2011)"},{"key":"16_CR4","doi-asserted-by":"crossref","unstructured":"Chollet, F.: Xception: deep learning with depthwise separable convolutions. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1251\u20131258 (2017)","DOI":"10.1109\/CVPR.2017.195"},{"key":"16_CR5","doi-asserted-by":"crossref","unstructured":"Hatamizadeh, A., Nath, V., Tang, Y., Yang, D., Roth, H.R., Xu, D.: Swin unetr: swin transformers for semantic segmentation of brain tumors in MRI images. In: International MICCAI brainlesion workshop, pp. 272\u2013284. Springer (2021)","DOI":"10.1007\/978-3-031-08999-2_22"},{"key":"16_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":"16_CR7","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"677","DOI":"10.1007\/978-3-030-78191-0_52","volume-title":"Information Processing in Medical Imaging","author":"M Islam","year":"2021","unstructured":"Islam, M., Glocker, B.: Spatially varying label smoothing: capturing uncertainty from expert annotations. In: Feragen, A., Sommer, S., Schnabel, J., Nielsen, M. (eds.) IPMI 2021. LNCS, vol. 12729, pp. 677\u2013688. Springer, Cham (2021). https:\/\/doi.org\/10.1007\/978-3-030-78191-0_52"},{"key":"16_CR8","unstructured":"Kim, J.: https:\/\/github.com\/jaeho3690\/LIDC-IDRI-Preprocessing. Accessed: 27 June 2025"},{"key":"16_CR9","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2024.124270","volume":"253","author":"Y Lai","year":"2024","unstructured":"Lai, Y., Liu, X., Cheng, Y., Liu, S., Wu, Y., Zheng, W., et al.: Transformer based multiple superpixel-instance learning for weakly supervised segmenting lesions of interstitial lung disease. Expert Syst. Appl. 253, 124270 (2024)","journal-title":"Expert Syst. Appl."},{"key":"16_CR10","doi-asserted-by":"crossref","unstructured":"Lai, Y., Liu, X., Liang, X., Rong, C., Wu, Y., Zheng, W.: Deep reinforcement learning driven weakly supervised lesion segmentation in lung CT sequence images. IEEE Transactions on Instrumentation and Measurement (2025)","DOI":"10.1109\/TIM.2025.3571146"},{"issue":"1","key":"16_CR11","doi-asserted-by":"publisher","first-page":"14634","DOI":"10.1038\/s41598-021-93977-0","volume":"11","author":"V Mazzia","year":"2021","unstructured":"Mazzia, V., Salvetti, F., Chiaberge, M.: Efficient-capsnet: capsule network with self-attention routing. Sci. Rep. 11(1), 14634 (2021)","journal-title":"Sci. Rep."},{"issue":"1","key":"16_CR12","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s12880-025-01725-x","volume":"25","author":"J Pan","year":"2025","unstructured":"Pan, J., Liang, L., Sun, P., Liang, Y., Zhu, J., Chen, Z.: Msa-net: multiple self-attention mechanism for 3d lung nodule classification in ct images. BMC Med. Imaging 25(1), 1\u201312 (2025)","journal-title":"BMC Med. Imaging"},{"issue":"2","key":"16_CR13","doi-asserted-by":"publisher","first-page":"221","DOI":"10.1007\/s13534-023-00332-5","volume":"14","author":"M Ramkumar","year":"2024","unstructured":"Ramkumar, M., Kalirajan, K., Kumar, U.P., Surya, P.: Deep volcanic residual u-net for nodal metastasis (nmet) identification from lung cancer. Biomed. Eng. Lett. 14(2), 221\u2013233 (2024)","journal-title":"Biomed. Eng. Lett."},{"key":"16_CR14","doi-asserted-by":"crossref","unstructured":"Rodrigues, E.M., Gouveia, M., Oliveira, H.P., Pereira, T.: Efficient-proto-caps: A parameter-efficient and interpretable capsule network for lung nodule characterization. IEEE Access (2025)","DOI":"10.1109\/ACCESS.2025.3555428"},{"issue":"1","key":"16_CR15","doi-asserted-by":"publisher","first-page":"23058","DOI":"10.1038\/s41598-025-08116-w","volume":"15","author":"Y Said","year":"2025","unstructured":"Said, Y., et al.: Ai-driven genetic algorithm-optimized lung segmentation for precision in early lung cancer diagnosis. Sci. Rep. 15(1), 23058 (2025)","journal-title":"Sci. Rep."},{"key":"16_CR16","doi-asserted-by":"crossref","unstructured":"Salehi, S.S.M., Erdogmus, D., Gholipour, A.: Tversky loss function for image segmentation using 3d fully convolutional deep networks. In: International Workshop on Machine Learning in Medical Imaging, pp. 379\u2013387. Springer (2017)","DOI":"10.1007\/978-3-319-67389-9_44"},{"issue":"9","key":"16_CR17","doi-asserted-by":"publisher","first-page":"234","DOI":"10.3390\/jimaging10090234","volume":"10","author":"T Tang","year":"2024","unstructured":"Tang, T., Zhang, R.: A multi-task model for pulmonary nodule segmentation and classification. J. Imaging 10(9), 234 (2024)","journal-title":"J. Imaging"},{"key":"16_CR18","doi-asserted-by":"crossref","unstructured":"Zhou, Z., Rahman\u00a0Siddiquee, M.M., Tajbakhsh, N., Liang, J.: Unet++: a nested u-net architecture for medical image segmentation. In: Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support: 4th International Workshop, DLMIA 2018, and 8th International Workshop, ML-CDS 2018, Held In Conjunction with MICCAI 2018, Granada, Spain, September 20, 2018, proceedings 4, pp. 3\u201311. Springer (2018)","DOI":"10.1007\/978-3-030-00889-5_1"},{"key":"16_CR19","doi-asserted-by":"crossref","unstructured":"Zhu, Y., Chien, A., Tian, Y.: Protecting lung CT nodule classification models with feature fusion of image and pixel-level features. In: Proceedings of the 2024 Workshop on Cybersecurity in Healthcare, pp. 29\u201336 (2023)","DOI":"10.1145\/3689942.3694744"}],"container-title":["Lecture Notes in Computer Science","Artificial Intelligence XLII"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-032-11402-0_16","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,15]],"date-time":"2026-04-15T10:03:47Z","timestamp":1776247427000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-032-11402-0_16"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,11,24]]},"ISBN":["9783032114013","9783032114020"],"references-count":19,"URL":"https:\/\/doi.org\/10.1007\/978-3-032-11402-0_16","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,11,24]]},"assertion":[{"value":"24 November 2025","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"SGAI-AI","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Innovative Techniques and Applications of Artificial Intelligence","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Cambridge","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"United Kingdom","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":"15 December 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"16 December 2025","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"45","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"sgai2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/bcs-sgai.org\/ai2025\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}