{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,14]],"date-time":"2026-07-14T14:52:36Z","timestamp":1784040756685,"version":"3.55.0"},"publisher-location":"Cham","reference-count":20,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031632105","type":"print"},{"value":"9783031632112","type":"electronic"}],"license":[{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"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":[[2024]]},"DOI":"10.1007\/978-3-031-63211-2_14","type":"book-chapter","created":{"date-parts":[[2024,6,20]],"date-time":"2024-06-20T14:02:22Z","timestamp":1718892142000},"page":"174-186","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["MTA-Net: A Multi-task Assisted Network for\u00a0Whole-Body Lymphoma Segmentation"],"prefix":"10.1007","author":[{"given":"Zhaohai","family":"Liang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiayi","family":"Wu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Siyi","family":"Chai","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yingkai","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chengdong","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Cong","family":"Shen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jingmin","family":"Xin","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,6,21]]},"reference":[{"key":"14_CR1","doi-asserted-by":"crossref","unstructured":"Ahamed, S., et al.: A cascaded deep network for automated tumor detection and segmentation in clinical pet imaging of diffuse large b-cell lymphoma. In: Medical Imaging 2022: Image Processing, vol. 12032, pp. 934\u2013941. SPIE (2022)","DOI":"10.1117\/12.2612684"},{"key":"14_CR2","doi-asserted-by":"publisher","unstructured":"Bi, L., Kim, J., Wen, L., Feng, D.D.: Automated and robust percist-based thresholding framework for whole body pet-ct studies. In: 2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 5335\u20135338 (2012). https:\/\/doi.org\/10.1109\/EMBC.2012.6347199","DOI":"10.1109\/EMBC.2012.6347199"},{"key":"14_CR3","unstructured":"Chen, J., et al.: Transunet: transformers make strong encoders for medical image segmentation. arXiv preprint arXiv:2102.04306 (2021)"},{"key":"14_CR4","doi-asserted-by":"crossref","unstructured":"Chen, Y., Dai, X., Liu, M., Chen, D., Yuan, L., Liu, Z.: Dynamic convolution: attention over convolution kernels. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 11030\u201311039 (2020)","DOI":"10.1109\/CVPR42600.2020.01104"},{"key":"14_CR5","doi-asserted-by":"crossref","unstructured":"Desbordes, P., Petitjean, C., Ruan, S.: 3d automated lymphoma segmentation in pet images based on cellular automata. In: 2014 4th International Conference on Image Processing Theory, Tools and Applications (IPTA), pp.\u00a01\u20136. IEEE (2014)","DOI":"10.1109\/IPTA.2014.7001923"},{"issue":"20","key":"14_CR6","first-page":"2019","volume":"3","author":"J Ferlay","year":"2018","unstructured":"Ferlay, J., et al.: Global cancer observatory: cancer today. Lyon, France: international agency for research on cancer 3(20), 2019 (2018)","journal-title":"Lyon, France: international agency for research on cancer"},{"key":"14_CR7","doi-asserted-by":"publisher","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, Cham (2021). https:\/\/doi.org\/10.1007\/978-3-031-08999-2_22","DOI":"10.1007\/978-3-031-08999-2_22"},{"key":"14_CR8","unstructured":"Heiliger, L., et al.: Autopet challenge: combining nn-unet with swin unetr augmented by maximum intensity projection classifier. arXiv preprint arXiv:2209.01112 (2022)"},{"key":"14_CR9","doi-asserted-by":"crossref","unstructured":"Hu, H., Shen, L., Zhou, T., Decazes, P., Vera, P., Ruan, S.: Lymphoma segmentation in pet images based on multi-view and conv3d fusion strategy. In: 2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI), pp. 1197\u20131200. IEEE (2020)","DOI":"10.1109\/ISBI45749.2020.9098595"},{"key":"14_CR10","doi-asserted-by":"publisher","first-page":"39","DOI":"10.1016\/j.ijar.2022.06.007","volume":"149","author":"L Huang","year":"2022","unstructured":"Huang, L., Ruan, S., Decazes, P., Den\u0153ux, T.: Lymphoma segmentation from 3d pet-ct images using a deep evidential network. Int. J. Approximate Reasoning 149, 39\u201360 (2022)","journal-title":"Int. J. Approximate Reasoning"},{"key":"14_CR11","doi-asserted-by":"crossref","unstructured":"Ji, Z., et\u00a0al.: Continual segment: towards a single, unified and accessible continual segmentation model of 143 whole-body organs in ct scans. arXiv preprint arXiv:2302.00162 (2023)","DOI":"10.1109\/ICCV51070.2023.01933"},{"issue":"21","key":"14_CR12","doi-asserted-by":"publisher","first-page":"5221","DOI":"10.3390\/cancers14215221","volume":"14","author":"RA Kuker","year":"2022","unstructured":"Kuker, R.A., et al.: A deep learning-aided automated method for calculating metabolic tumor volume in diffuse large b-cell lymphoma. Cancers 14(21), 5221 (2022)","journal-title":"Cancers"},{"key":"14_CR13","doi-asserted-by":"publisher","first-page":"8004","DOI":"10.1109\/ACCESS.2019.2963254","volume":"8","author":"H Li","year":"2019","unstructured":"Li, H., et al.: Densex-net: an end-to-end model for lymphoma segmentation in whole-body pet\/ct images. IEEE Access 8, 8004\u20138018 (2019)","journal-title":"IEEE Access"},{"key":"14_CR14","doi-asserted-by":"crossref","unstructured":"Marinov, Z., Rei\u00df, S., Kersting, D., Kleesiek, J., Stiefelhagen, R.: Mirror u-net: marrying multimodal fission with multi-task learning for semantic segmentation in medical imaging. arXiv preprint arXiv:2303.07126 (2023)","DOI":"10.1109\/ICCVW60793.2023.00242"},{"key":"14_CR15","doi-asserted-by":"crossref","unstructured":"Ronneberger, O., Fischer, P., Brox, T.: U-net: convolutional networks for biomedical image segmentation. In: Medical Image Computing and Computer-Assisted Intervention\u2013MICCAI 2015: 18th International Conference, Munich, Germany, October 5-9, 2015, Proceedings, Part III 18, pp. 234\u2013241. Springer (2015)","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"14_CR16","doi-asserted-by":"crossref","unstructured":"Tang, H., Liu, J., Zhao, M., Gong, X.: Progressive layered extraction (ple): a novel multi-task learning (mtl) model for personalized recommendations. In: Proceedings of the 14th ACM Conference on Recommender Systems, pp. 269\u2013278 (2020)","DOI":"10.1145\/3383313.3412236"},{"key":"14_CR17","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2022.106215","volume":"151","author":"M Wang","year":"2022","unstructured":"Wang, M., Jiang, H., Shi, T., Wang, Z., Guo, J., Lu, G., Wang, Y., Yao, Y.D.: Psr-nets: deep neural networks with prior shift regularization for pet\/ct based automatic, accurate, and calibrated whole-body lymphoma segmentation. Comput. Biol. Med. 151, 106215 (2022)","journal-title":"Comput. Biol. Med."},{"issue":"2","key":"14_CR18","doi-asserted-by":"publisher","first-page":"619","DOI":"10.1002\/mp.13331","volume":"46","author":"Z Zhong","year":"2019","unstructured":"Zhong, Z., Kim, Y., Plichta, K., Allen, B.G., Zhou, L., Buatti, J., Wu, X.: Simultaneous cosegmentation of tumors in pet-ct images using deep fully convolutional networks. Med. Phys. 46(2), 619\u2013633 (2019)","journal-title":"Med. Phys."},{"key":"14_CR19","doi-asserted-by":"crossref","unstructured":"Zhong, Z., Kim, Y., Zhou, L., Plichta, K., Allen, B., Buatti, J., Wu, X.: 3d fully convolutional networks for co-segmentation of tumors on pet-ct images. In: 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018), pp. 228\u2013231. IEEE (2018)","DOI":"10.1109\/ISBI.2018.8363561"},{"key":"14_CR20","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., Taylor, Z., Carneiro, G., Syeda-Mahmood, T., Martel, A., Maier-Hein, L., Tavares, J.M.R.S., Bradley, A., Papa, J.P., Belagiannis, V., Nascimento, J.C., Lu, Z., Conjeti, S., Moradi, M., Greenspan, H., Madabhushi, A. (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"}],"container-title":["IFIP Advances in Information and Communication Technology","Artificial Intelligence Applications and Innovations"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-63211-2_14","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,6,20]],"date-time":"2024-06-20T14:02:57Z","timestamp":1718892177000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-63211-2_14"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"ISBN":["9783031632105","9783031632112"],"references-count":20,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-63211-2_14","relation":{},"ISSN":["1868-4238","1868-422X"],"issn-type":[{"value":"1868-4238","type":"print"},{"value":"1868-422X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024]]},"assertion":[{"value":"21 June 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"AIAI","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"IFIP International Conference on Artificial Intelligence Applications and Innovations","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Corfu","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Greece","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2024","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"27 June 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"30 June 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"20","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"aiai2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/ifipaiai.org\/2024\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}