{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,18]],"date-time":"2026-01-18T02:00:19Z","timestamp":1768701619147,"version":"3.49.0"},"reference-count":24,"publisher":"Springer Science and Business Media LLC","issue":"9","license":[{"start":{"date-parts":[[2021,6,5]],"date-time":"2021-06-05T00:00:00Z","timestamp":1622851200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,6,5]],"date-time":"2021-06-05T00:00:00Z","timestamp":1622851200000},"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":["Int J CARS"],"published-print":{"date-parts":[[2021,9]]},"DOI":"10.1007\/s11548-021-02418-w","type":"journal-article","created":{"date-parts":[[2021,6,5]],"date-time":"2021-06-05T05:06:17Z","timestamp":1622869577000},"page":"1425-1434","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":23,"title":["DUDA-Net: a double U-shaped dilated attention network for automatic infection area segmentation in COVID-19 lung CT images"],"prefix":"10.1007","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8116-1225","authenticated-orcid":false,"given":"Feng","family":"Xie","sequence":"first","affiliation":[]},{"given":"Zheng","family":"Huang","sequence":"additional","affiliation":[]},{"given":"Zhengjin","family":"Shi","sequence":"additional","affiliation":[]},{"given":"Tianyu","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Guoli","family":"Song","sequence":"additional","affiliation":[]},{"given":"Bolun","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Zihong","family":"Liu","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2021,6,5]]},"reference":[{"key":"2418_CR1","doi-asserted-by":"publisher","DOI":"10.1007\/s11356-021-13249-2","author":"MH Nasirpour","year":"2021","unstructured":"Nasirpour MH, Sharifi A, Ahmadi M, Ghoushchi SJ (2021) Revealing the relationship between solar activity and COVID-19 and forecasting of possible future viruses using multi-step autoregression (MSAR). Environ Sci Pollut Res. https:\/\/doi.org\/10.1007\/s11356-021-13249-2","journal-title":"Environ Sci Pollut Res"},{"key":"2418_CR2","doi-asserted-by":"publisher","first-page":"110170","DOI":"10.1016\/j.chaos.2020.110170","volume":"140","author":"S Hassantabar","year":"2020","unstructured":"Hassantabar S, Ahmadi M, Sharifi A (2020) Diagnosis and detection of infected tissue of COVID-19 patients based on lung X-ray image using convolutional neural network approaches. Chaos, Solitons Fractals 140:110170. https:\/\/doi.org\/10.1016\/j.chaos.2020.110170","journal-title":"Chaos, Solitons Fractals"},{"key":"2418_CR3","doi-asserted-by":"publisher","first-page":"109761","DOI":"10.1016\/j.mehy.2020.109761","volume":"140","author":"F Ucar","year":"2020","unstructured":"Ucar F, Korkmaz D (2020) COVIDiagnosis-Net: Deep Bayes-SqueezeNet based Diagnostic of the Coronavirus Disease 2019 (COVID-19) from X-Ray Images. Medical Hypotheses. 140:109761","journal-title":"Medical Hypotheses."},{"issue":"5","key":"2418_CR4","doi-asserted-by":"publisher","first-page":"516","DOI":"10.1164\/rccm.200407-127OC","volume":"170","author":"HS Hofmann","year":"2004","unstructured":"Hofmann HS, Hansen G, Burdach S, Bartling B, Silber RE, Simm A (2004) Discrimination of human lung neoplasm from normal lung by two target genes. Am J Respir Crit Care Med 170(5):516\u2013519. https:\/\/doi.org\/10.1164\/rccm.200407-127OC","journal-title":"Am J Respir Crit Care Med"},{"key":"2418_CR5","doi-asserted-by":"publisher","DOI":"10.1080\/00207454.2021.1883602","author":"M Ahmadi","year":"2021","unstructured":"Ahmadi M, Sharifi A, Jafarian Fard M, Soleimani N (2021) Detection of brain lesion location in MRI images using convolutional neural network and robust PCA. Int J Neurosci. https:\/\/doi.org\/10.1080\/00207454.2021.1883602","journal-title":"Int J Neurosci"},{"key":"2418_CR6","doi-asserted-by":"publisher","DOI":"10.1155\/2021\/6653879","author":"M Ahmadi","year":"2021","unstructured":"Ahmadi M, Sharifi A, Hassantabar S, Enayati S (2021) QAIS-DSNN: Tumor Area Segmentation of MRI Image with Optimized Quantum Matched-Filter Technique and Deep Spiking Neural Network. Biomed Res Int. https:\/\/doi.org\/10.1155\/2021\/6653879","journal-title":"Biomed Res Int"},{"key":"2418_CR7","doi-asserted-by":"crossref","unstructured":"Wu YH, Gao SH, Mei J, Xu J, Fan DP, Zhao CW, Cheng MM (2020) JCS: An explainable COVID-19 diagnosis system by joint classification and segmentation. arXiv preprint arxiv:2004.07054","DOI":"10.1109\/TIP.2021.3058783"},{"key":"2418_CR8","unstructured":"Chen X, Yao L, Zhang Y, (2020). Residual attention U-Net for automated multi-class segmentation of COVID-19 Chest CT Images. arXiv preprint arXiv:2004.05645"},{"key":"2418_CR9","unstructured":"Qiu Y, Liu Y, Xu J (2020) MiniSeg: An extremely minimum network for efficient COVID-19 Segmentation. arXiv preprint arXiv:2004.09750"},{"key":"2418_CR10","doi-asserted-by":"publisher","first-page":"104037","DOI":"10.1016\/j.compbiomed.2020.104037","volume":"126","author":"A Amyar","year":"2020","unstructured":"Amyar A, Modzelewski R, Li H, Ruan S (2020) Multi-task deep learning based CT imaging analysis for COVID-19 pneumonia: Classification and segmentation. Comput Biol Med 126:104037. https:\/\/doi.org\/10.1016\/j.compbiomed.2020.104037","journal-title":"Comput Biol Med"},{"key":"2418_CR11","doi-asserted-by":"crossref","unstructured":"Cohen JP, Morrison P, Dao L, Roth K, Duong TQ, Ghassemi M (2020) Covid-19 image data collection: Prospective predictions are the future. arXiv preprint arXiv:2006.11988","DOI":"10.59275\/j.melba.2020-48g7"},{"key":"2418_CR12","doi-asserted-by":"publisher","unstructured":"Senthilkumaran N, Thimmiaraja J (2014) Histogram equalization for image enhancement using MRI brain images. In 2014 world congress on computing and communication technologies, IEEE. pp 80\u201383. https:\/\/doi.org\/10.1109\/WCCCT.2014.45","DOI":"10.1109\/WCCCT.2014.45"},{"issue":"7","key":"2418_CR13","doi-asserted-by":"publisher","first-page":"375","DOI":"10.1002\/cem.1349","volume":"25","author":"AA Gowen","year":"2011","unstructured":"Gowen AA, Downey G, Esquerre C, O\u2019Donnell CP (2011) Preventing over-fitting in PLS calibration models of near-infrared (NIR) spectroscopy data using regression coefficients. J Chemom 25(7):375\u2013381. https:\/\/doi.org\/10.1002\/cem.1349","journal-title":"J Chemom"},{"key":"2418_CR14","unstructured":"Roelofs R, Shankar V, Recht B, Fridovich-Keil S, Hardt M, Miller J, Schmidt L (2019) A meta-analysis of overfitting in machine learning. In Adv Neural Inf Process Syst pp 9179\u20139189"},{"key":"2418_CR15","unstructured":"Ioffe S, Szegedy C (2015) Batch normalization: Accelerating deep network training by reducing internal covariate shift. In International conference on machine learning. PMLR. pp 448\u2013456"},{"issue":"1","key":"2418_CR16","doi-asserted-by":"publisher","first-page":"41","DOI":"10.17977\/um017v2i12019p41-46","volume":"2","author":"IKM Jais","year":"2019","unstructured":"Jais IKM, Ismail AR, Nisa SQ (2019) Adam optimization algorithm for wide and deep neural network. Knowl Eng Data Sci 2(1):41\u201346. https:\/\/doi.org\/10.17977\/um017v2i12019p41-46","journal-title":"Knowl Eng Data Sci"},{"key":"2418_CR17","doi-asserted-by":"publisher","first-page":"101926","DOI":"10.1016\/j.bspc.2020.101926","volume":"59","author":"Z Huang","year":"2020","unstructured":"Huang Z, Zhao Y, Li X, Zhao X, Liu Y, Song G, Luo Y (2020) Application of innovative image processing methods and AdaBound-SE-DenseNet to optimize the diagnosis performance of meningiomas and gliomas. Biomed Signal Process Control 59:101926. https:\/\/doi.org\/10.1016\/j.bspc.2020.101926","journal-title":"Biomed Signal Process Control"},{"key":"2418_CR18","doi-asserted-by":"crossref","unstructured":"Long J, Shelhamer E, Darrell T (2015) Fully convolutional networks for semantic segmentation. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp 3431\u20133440","DOI":"10.1109\/CVPR.2015.7298965"},{"key":"2418_CR19","doi-asserted-by":"publisher","unstructured":"Ronneberger O, Fischer P, Brox T (2015) October. U-net: Convolutional networks for biomedical image segmentation. In: International conference on medical image computing and computer-assisted intervention, Springer, Cham. pp 234\u2013241. https:\/\/doi.org\/10.1007\/978-3-319-24574-4_28","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"2418_CR20","doi-asserted-by":"publisher","unstructured":"Zhou Z, Siddiquee MMR, Tajbakhsh N, Liang J (2018). Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support, Springer, Cham. pp 3-11. https:\/\/doi.org\/10.1007\/978-3-030-00889-5_1","DOI":"10.1007\/978-3-030-00889-5_1"},{"key":"2418_CR21","doi-asserted-by":"crossref","unstructured":"Azad R, Asadi-Aghbolaghi M, Fathy M, Escalera S (2019) Bi-directional ConvLSTM U-net with Densley connected convolutions. In: Proceedings of the IEEE international conference on computer vision workshops. 0\u20130","DOI":"10.1109\/ICCVW.2019.00052"},{"key":"2418_CR22","doi-asserted-by":"publisher","unstructured":"Fang Z, Chen Y, Nie D, Lin W, Shen D (2019) RCA-U-Net: Residual Channel Attention U-Net for Fast Tissue Quantification in Magnetic Resonance Fingerprinting. In: International conference on medical image computing and computer-assisted intervention, Springer, Cham. pp 101-109https:\/\/doi.org\/10.1007\/978-3-030-32248-9_12","DOI":"10.1007\/978-3-030-32248-9_12"},{"key":"2418_CR23","unstructured":"Zhou T, Canu S, Ruan S (2020) An automatic covid-19 ct segmentation network using spatial and channel attention mechanism. arXiv preprint arXiv:2004.06673"},{"key":"2418_CR24","doi-asserted-by":"crossref","unstructured":"Elharrouss, O., Subramanian, N., Al-Maadeed, S., 2020. An encoder-decoder-based method for covid-19 lung infection segmentation. arXiv preprint arXiv:2007.00861","DOI":"10.29117\/quarfe.2020.0294"}],"container-title":["International Journal of Computer Assisted Radiology and Surgery"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11548-021-02418-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11548-021-02418-w\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11548-021-02418-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,11,4]],"date-time":"2023-11-04T15:05:33Z","timestamp":1699110333000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11548-021-02418-w"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,6,5]]},"references-count":24,"journal-issue":{"issue":"9","published-print":{"date-parts":[[2021,9]]}},"alternative-id":["2418"],"URL":"https:\/\/doi.org\/10.1007\/s11548-021-02418-w","relation":{},"ISSN":["1861-6410","1861-6429"],"issn-type":[{"value":"1861-6410","type":"print"},{"value":"1861-6429","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,6,5]]},"assertion":[{"value":"13 January 2021","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"21 May 2021","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"5 June 2021","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare that they have no conflicts of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"The article uses open-source datasets. All procedures in studies involving human participants were performed in accordance with the ethical standards of the institutional and\/or national research committee and the 1964 Helsinki declaration and its later amendments or comparable ethical standards.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval"}},{"value":"Informed consent was obtained from all individual participants included in the study.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Informed consent"}},{"value":"This content has been made available to all.","name":"free","label":"Free to read"}]}}