{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,25]],"date-time":"2025-03-25T23:43:59Z","timestamp":1742946239123,"version":"3.40.3"},"publisher-location":"Singapore","reference-count":17,"publisher":"Springer Singapore","isbn-type":[{"type":"print","value":"9789811668890"},{"type":"electronic","value":"9789811668906"}],"license":[{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022]]},"DOI":"10.1007\/978-981-16-6890-6_26","type":"book-chapter","created":{"date-parts":[[2022,3,5]],"date-time":"2022-03-05T14:02:28Z","timestamp":1646488948000},"page":"347-360","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Multitask Deep Learning Model with Efficient Encoding Layer and Enhanced Parallel Convolution Block"],"prefix":"10.1007","author":[{"given":"Anupam","family":"Biswas","sequence":"first","affiliation":[]},{"given":"Angshuman","family":"Bora","sequence":"additional","affiliation":[]},{"given":"Debashish","family":"Malakar","sequence":"additional","affiliation":[]},{"given":"Subham","family":"Chakraborty","sequence":"additional","affiliation":[]},{"given":"Suman","family":"Bera","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,3,6]]},"reference":[{"key":"26_CR1","doi-asserted-by":"publisher","unstructured":"Agarwal A, Triggs W (2008) Multilevel image coding with hyperfeatures. Int J Comput Vis 78 (06 2008). https:\/\/doi.org\/10.1007\/s11263-007-0072-x","DOI":"10.1007\/s11263-007-0072-x"},{"key":"26_CR2","doi-asserted-by":"crossref","unstructured":"Alom MZ, Taha TM, Yakopcic C, Westberg S, Sidike P, Nasrin MS, Hasan M, Van Essen BC, Awwal AA, Asari VK (2019) A state-of-the-art survey on deep learning theory and architectures. Electronics 8(3):292","DOI":"10.3390\/electronics8030292"},{"key":"26_CR3","doi-asserted-by":"crossref","unstructured":"Amyar A, Modzelewski R, Ruan S (2020) Multi-task deep learning based ct imaging analysis for covid-19: Classification and segmentation. medRxiv","DOI":"10.1101\/2020.04.16.20064709"},{"key":"26_CR4","doi-asserted-by":"crossref","unstructured":"Das A, Dantcheva A, Bremond F (2018) Mitigating bias in gender, age and ethnicity classification: a multi-task convolution neural network approach. In: Proceedings of the European conference on computer vision (ECCV). pp\u00a00","DOI":"10.1007\/978-3-030-11009-3_35"},{"key":"26_CR5","doi-asserted-by":"crossref","unstructured":"Fourure D, Emonet R, Fromont E, Muselet D, Neverova N, Tr\u00e9meau A, Wolf C (2017) Multi-task, multi-domain learning: application to semantic segmentation and pose regression. Neurocomputing 251:68\u201380","DOI":"10.1016\/j.neucom.2017.04.014"},{"key":"26_CR6","doi-asserted-by":"crossref","unstructured":"Ke R, Bugeau A, Papadakis N, Kirkland M, Schuetz P (2020) Sch\u00f6nlieb. Multi-task deep learning for image segmentation using recursive approximation tasks, C.B","DOI":"10.1109\/TIP.2021.3062726"},{"key":"26_CR7","doi-asserted-by":"publisher","unstructured":"Kong K, Lee J, Song W, Kang M, Kwon K, Kim SG (2019) Multitask bilateral learning for real-time image enhancement. J Soc Inf Displ 27. https:\/\/doi.org\/10.1002\/jsid.791","DOI":"10.1002\/jsid.791"},{"key":"26_CR8","unstructured":"Le TLT, Thome N, Bernard S, Bismuth V, Patoureaux F (2019) Multitask classification and segmentation for cancer diagnosis in mammography. arXiv:1909.05397"},{"key":"26_CR9","unstructured":"Li Y, Tian X, Liu T, Tao D (2015) Multi-task model and feature joint learning. In: Twenty-fourth international joint conference on artificial intelligence"},{"key":"26_CR10","unstructured":"Long M, Cao Z, Wang J, Yu PS (2015) Learning multiple tasks with multilinear relationship networks. arXiv:1506.02117"},{"key":"26_CR11","doi-asserted-by":"crossref","unstructured":"Moeskops P, Wolterink JM, van\u00a0der Velden BH, Gilhuijs KG, Leiner T, Viergever MA, I\u0161gum I (2016) Deep learning for multi-task medical image segmentation in multiple modalities. In: International conference on medical image computing and computer-assisted intervention. Springer, pp 478\u2013486","DOI":"10.1007\/978-3-319-46723-8_55"},{"issue":"1","key":"26_CR12","doi-asserted-by":"publisher","first-page":"121","DOI":"10.1109\/TPAMI.2017.2781233","volume":"41","author":"R Ranjan","year":"2017","unstructured":"Ranjan R, Patel VM, Chellappa R (2017) Hyperface: a deep multi-task learning framework for face detection, landmark localization, pose estimation, and gender recognition. IEEE Trans Pattern Anal Mach Intell 41(1):121\u2013135","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"26_CR13","doi-asserted-by":"crossref","unstructured":"Wang N, Christodoulou AG, Xie Y, Wang Z, Deng Z, Zhou B, Lee S, Fan Z, Chang H, Yu W, Li D (April 2019) Quantitative 3d dynamic contrast-enhanced (dce) mr imaging of carotid vessel wall by fast t1 mapping using multitasking. Mag Resonan Med 81(4):2302\u20132314","DOI":"10.1002\/mrm.27553"},{"issue":"3","key":"26_CR14","doi-asserted-by":"publisher","first-page":"2097","DOI":"10.1093\/gji\/ggz418","volume":"219","author":"X Wu","year":"2019","unstructured":"Wu X, Liang L, Shi Y, Geng Z, Fomel S (2019) Multitask learning for local seismic image processing: fault detection, structure-oriented smoothing with edge-preserving, and seismic normal estimation by using a single convolutional neural network. Geophys J Int 219(3):2097\u20132109","journal-title":"Geophys J Int"},{"key":"26_CR15","doi-asserted-by":"crossref","unstructured":"Yu B, Lane I (2014) Multi-task deep learning for image understanding. In: 2014 6th international conference of soft computing and pattern recognition (SoCPaR). IEEE, pp 37\u201342","DOI":"10.1109\/SOCPAR.2014.7007978"},{"key":"26_CR16","doi-asserted-by":"crossref","unstructured":"Zhang, Zhifei SY, Qi H (2017) Age progression\/regression by conditional adversarial autoencoder. In: IEEE conference on computer vision and pattern recognition (CVPR). IEEE","DOI":"10.1109\/CVPR.2017.463"},{"key":"26_CR17","doi-asserted-by":"crossref","unstructured":"Zhang Y, Yang Q (2017) A survey on multi-task learning. arXiv:1707.08114","DOI":"10.1093\/nsr\/nwx105"}],"container-title":["Advances in Intelligent Systems and Computing","Proceedings of the Seventh International Conference on Mathematics and Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-16-6890-6_26","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,3,5]],"date-time":"2022-03-05T14:05:31Z","timestamp":1646489131000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-16-6890-6_26"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9789811668890","9789811668906"],"references-count":17,"URL":"https:\/\/doi.org\/10.1007\/978-981-16-6890-6_26","relation":{},"ISSN":["2194-5357","2194-5365"],"issn-type":[{"type":"print","value":"2194-5357"},{"type":"electronic","value":"2194-5365"}],"subject":[],"published":{"date-parts":[[2022]]},"assertion":[{"value":"6 March 2022","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}}]}}