乐胖代购免代理版

{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,9,2]],"date-time":"2024-09-02T20:40:01Z","timestamp":1725309601618},"reference-count":69,"publisher":"Springer Science and Business Media LLC","issue":"4","license":[{"start":{"date-parts":[[2021,6,28]],"date-time":"2021-06-28T00:00:00Z","timestamp":1624838400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,6,28]],"date-time":"2021-06-28T00:00:00Z","timestamp":1624838400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"funder":[{"name":"Shanghai Pujiang Program","award":["20PJ1402400"]},{"name":"Zhongshan Hospital Clinical Research Foundation","award":["2016ZSLC05"]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Machine Vision and Applications"],"published-print":{"date-parts":[[2021,7]]},"DOI":"10.1007\/s00138-021-01224-3","type":"journal-article","created":{"date-parts":[[2021,6,28]],"date-time":"2021-06-28T08:02:38Z","timestamp":1624867358000},"update-policy":"http:\/\/dx.doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Images denoising for COVID-19 chest X-ray based on multi-resolution parallel residual CNN"],"prefix":"10.1007","volume":"32","author":[{"given":"Xiaoben","family":"Jiang","sequence":"first","affiliation":[]},{"given":"Yu","family":"Zhu","sequence":"additional","affiliation":[]},{"given":"Bingbing","family":"Zheng","sequence":"additional","affiliation":[]},{"given":"Dawei","family":"Yang","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2021,6,28]]},"reference":[{"issue":"8","key":"1224_CR1","doi-asserted-by":"publisher","first-page":"91916","DOI":"10.1109\/ACCESS.2020.2994762","volume":"8","author":"A Waheed","year":"2020","unstructured":"Waheed, A., Goyal, M., Gupta, D., et al.: CovidGAN: data augmentation using auxiliary classifier GAN for improved Covid-19 detection. IEEE Access 8(8), 91916\u201391923 (2020)","journal-title":"IEEE Access"},{"key":"1224_CR2","doi-asserted-by":"publisher","first-page":"71","DOI":"10.1016\/j.ijsu.2020.02.034","volume":"76","author":"C Sohrabi","year":"2020","unstructured":"Sohrabi, C., Alsafi, Z., O\u2019Neill, N., et al.: World Health Organization declares global emergency: a review of the 2019 novel coronavirus (COVID-19). Int. J. Surg. 76, 71\u201376 (2020)","journal-title":"Int. J. Surg."},{"issue":"8","key":"1224_CR3","doi-asserted-by":"publisher","first-page":"2688","DOI":"10.1109\/TMI.2020.2993291","volume":"39","author":"Y Oh","year":"2020","unstructured":"Oh, Y., Park, S., Ye, J.C.: Deep learning COVID-19 features on CXR using limited training data sets. IEEE Trans. Med. Imaging 39(8), 2688\u20132700 (2020)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"1224_CR4","doi-asserted-by":"crossref","unstructured":"Ma, J., Wang, Y., An, X., et al.: Towards efficient COVID-19 CT annotation: a benchmark for lung and infection segmentation. arXiv:2004.12537 (2020)","DOI":"10.1002\/mp.14676"},{"issue":"7553","key":"1224_CR5","doi-asserted-by":"publisher","first-page":"436","DOI":"10.1038\/nature14539","volume":"521","author":"Y Lecun","year":"2015","unstructured":"Lecun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521(7553), 436\u2013444 (2015)","journal-title":"Nature"},{"key":"1224_CR6","doi-asserted-by":"publisher","first-page":"85","DOI":"10.1016\/j.neunet.2014.09.003","volume":"61","author":"J Schmidhuber","year":"2015","unstructured":"Schmidhuber, J.: Deep learning in neural networks: an overview. Neural Netw. 61, 85\u2013117 (2015)","journal-title":"Neural Netw."},{"key":"1224_CR7","unstructured":"Krizhevsky, A., Sutskever, I., Hinton, G.: ImageNet classification with deep convolutional neural networks. In: Proceedings of 26th Conference on Neural Information Processing Systems (NIPS), pp. 1097\u20131105 (2012)"},{"issue":"4","key":"1224_CR8","doi-asserted-by":"publisher","first-page":"1445","DOI":"10.1109\/TPAMI.2020.2975798","volume":"43","author":"C Yan","year":"2020","unstructured":"Yan, C., Gong, B., Wei, Y., et al.: Deep multi-view enhancement hashing for image retrieval. IEEE Trans. Pattern Anal. Mach. Intell. 43(4), 1445\u20131451 (2020)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"issue":"11","key":"1224_CR9","doi-asserted-by":"publisher","first-page":"3014","DOI":"10.1109\/TMM.2020.2967645","volume":"22","author":"C Yan","year":"2020","unstructured":"Yan, C., Shao, B., Zhao, H., et al.: 3D room layout estimation from a single RGB image. IEEE Trans. Multimed. 22(11), 3014\u20133024 (2020)","journal-title":"IEEE Trans. Multimed."},{"issue":"4","key":"1224_CR10","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3404374","volume":"16","author":"C Yan","year":"2020","unstructured":"Yan, C., Li, Z., Zhang, Y., et al.: Depth image denoising using nuclear norm and learning graph model. ACM Trans. Multimed. Comput. Commun. Appl. 16(4), 1\u201317 (2020)","journal-title":"ACM Trans. Multimed. Comput. Commun. Appl."},{"key":"1224_CR11","doi-asserted-by":"publisher","DOI":"10.1109\/TCSVT.2021.3067449","author":"C Yan","year":"2021","unstructured":"Yan, C., Hao, Y., Li, L., et al.: Task-adaptive attention for image captioning. IEEE Trans. Circuits Syst. Video Technol. (2021). https:\/\/doi.org\/10.1109\/TCSVT.2021.3067449","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"1224_CR12","doi-asserted-by":"publisher","first-page":"60","DOI":"10.1016\/j.media.2017.07.005","volume":"42","author":"G Litjens","year":"2017","unstructured":"Litjens, G., Kooi, T., Bejnordi, B.E., et al.: A survey on deep learning in medical image analysis. Med. Image Anal. 42, 60\u201388 (2017)","journal-title":"Med. Image Anal."},{"key":"1224_CR13","doi-asserted-by":"publisher","first-page":"422","DOI":"10.3389\/fnins.2019.00422","volume":"13","author":"S Wang","year":"2019","unstructured":"Wang, S., Tang, C., Sun, J., et al.: Cerebral micro-bleeding detection based on densely connected neural network. Front. Neurosci. 13, 422\u2013432 (2019)","journal-title":"Front. Neurosci."},{"issue":"8","key":"1224_CR14","doi-asserted-by":"publisher","first-page":"2595","DOI":"10.1109\/TMI.2020.2995508","volume":"39","author":"X Ouyang","year":"2020","unstructured":"Ouyang, X., Huo, J., Xia, L., et al.: Dual-sampling attention network for diagnosis of COVID-19 from community acquired pneumonia. IEEE Trans. Med. Imaging 39(8), 2595\u20132605 (2020)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"1224_CR15","first-page":"1","volume":"2021","author":"SH Wang","year":"2021","unstructured":"Wang, S.H., Zhang, Y., Cheng, X., et al.: PSSPNN: PatchShuffle Stochastic Pooling Neural Network for an explainable diagnosis of COVID-19 with multiple-way data augmentation. Comput. Math. Methods Med. 2021, 1\u201318 (2021)","journal-title":"Comput. Math. Methods Med."},{"key":"1224_CR16","doi-asserted-by":"publisher","DOI":"10.1007\/s12559-020-09776-8","author":"Y-D Zhang","year":"2021","unstructured":"Zhang, Y.-D., Satapathy, S.C., Zhang, X., et al.: Covid-19 diagnosis via DenseNet and optimization of transfer learning setting. Cognit. Comput. (2021). https:\/\/doi.org\/10.1007\/s12559-020-09776-8","journal-title":"Cognit. Comput."},{"key":"1224_CR17","doi-asserted-by":"publisher","first-page":"114848","DOI":"10.1016\/j.eswa.2021.114848","volume":"176","author":"Q Jin","year":"2021","unstructured":"Jin, Q., Cui, H., Sun, C., et al.: Domain adaptation based self-correction model for COVID-19 infection segmentation in CT images. Expert Syst. Appl. 176, 114848 (2021)","journal-title":"Expert Syst. Appl."},{"issue":"8","key":"1224_CR18","doi-asserted-by":"publisher","first-page":"2626","DOI":"10.1109\/TMI.2020.2996645","volume":"39","author":"DP Fan","year":"2020","unstructured":"Fan, D.P., Zhou, T., Ji, G.P., et al.: Inf-net: automatic covid-19 lung infection segmentation from CT images. IEEE Trans. Med. Imaging 39(8), 2626\u20132637 (2020)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"1224_CR19","doi-asserted-by":"publisher","first-page":"4","DOI":"10.1109\/RBME.2020.2987975","volume":"14","author":"F Shi","year":"2020","unstructured":"Shi, F., Wang, J., Shi, J., et al.: Review of artificial intelligence techniques in imaging data acquisition, segmentation and diagnosis for covid-19. IEEE Rev. Biomed. Eng. 14, 4\u201315 (2020)","journal-title":"IEEE Rev. Biomed. Eng."},{"issue":"11","key":"1224_CR20","doi-asserted-by":"publisher","first-page":"1970","DOI":"10.1049\/iet-ipr.2019.0241","volume":"13","author":"Y Jin","year":"2019","unstructured":"Jin, Y., Jiang, X.B., Wei, Z.K., et al.: Chest X-ray image denoising method based on deep convolution neural network. IET Image Proc. 13(11), 1970\u20131978 (2019)","journal-title":"IET Image Proc."},{"issue":"1","key":"1224_CR21","doi-asserted-by":"publisher","first-page":"63","DOI":"10.1186\/s12938-018-0496-2","volume":"17","author":"C Wang","year":"2018","unstructured":"Wang, C., Elazab, A., Jia, F., et al.: Automated chest screening based on a hybrid model of transfer learning and convolutional sparse denoising autoencoder. Biomed. Eng. Online 17(1), 63 (2018)","journal-title":"Biomed. Eng. Online"},{"issue":"11","key":"1224_CR22","doi-asserted-by":"publisher","first-page":"97","DOI":"10.1016\/j.nima.2017.12.050","volume":"884","author":"D Lee","year":"2018","unstructured":"Lee, D., Choi, S., Kim, H.J.: Performance evaluation of image denoising developed using convolutional denoising autoencoders in chest radiography. Nucl. Instrum. Methods B 884(11), 97\u2013104 (2018)","journal-title":"Nucl. Instrum. Methods B"},{"key":"1224_CR23","doi-asserted-by":"crossref","first-page":"89095","DOI":"10.1155\/IJBI\/2006\/89095","volume":"2006","author":"Y Wang","year":"2006","unstructured":"Wang, Y., Zhou, H.: Total variation wavelet-based medical image denoising. Int. J. Biomed. Imaging. 2006, 89095\u201389107 (2006)","journal-title":"Int. J. Biomed. Imaging."},{"issue":"12","key":"1224_CR24","doi-asserted-by":"publisher","first-page":"2826","DOI":"10.1109\/TBME.2009.2028876","volume":"56","author":"H Rabbani","year":"2009","unstructured":"Rabbani, H., Nezafat, R., Gazor, S.: Wavelet-domain medical image denoising using bivariate laplacian mixture model. IEEE. Trans. Biomed. Eng. 56(12), 2826\u20132837 (2009)","journal-title":"IEEE. Trans. Biomed. Eng."},{"key":"1224_CR25","doi-asserted-by":"crossref","unstructured":"Satheesh, S., Prasad, K.: Medical image denoising using adaptive threshold based on contourlet transform. arXiv:1103.4907 (2011)","DOI":"10.5121\/acij.2011.2205"},{"issue":"12","key":"1224_CR26","doi-asserted-by":"publisher","first-page":"3450","DOI":"10.1109\/TBME.2012.2217493","volume":"59","author":"S Li","year":"2012","unstructured":"Li, S., Yin, H., Fang, L.: Group-sparse representation with dictionary learning for medical image denoising and fusion. IEEE. Trans. Biomed. Eng. 59(12), 3450\u20133459 (2012)","journal-title":"IEEE. Trans. Biomed. Eng."},{"key":"1224_CR27","doi-asserted-by":"crossref","unstructured":"Gondara, L.: Medical image denoising using convolutional denoising autoencoders. In: Proceedings of 2016 IEEE 16th International Conference on Data Mining Workshops (ICDMW), pp. 241\u2013246 (2016)","DOI":"10.1109\/ICDMW.2016.0041"},{"issue":"2","key":"1224_CR28","first-page":"1","volume":"2","author":"T Mondal","year":"2014","unstructured":"Mondal, T., Maitra, M.: Denoising and compression of medical image in wavelet 2D. Int. J. Recent Innov. Trends Comput. Commun. 2(2), 1\u20134 (2014)","journal-title":"Int. J. Recent Innov. Trends Comput. Commun."},{"key":"1224_CR29","doi-asserted-by":"crossref","unstructured":"Raj, V.N.P., Venkateswarlu, T.: Denoising of medical images using undecimated wavelet transform. In: Proceedings of 2011 IEEE Recent Advances in Intelligent Computational Systems (RAICS), pp. 483\u2013488 (2011)","DOI":"10.1109\/RAICS.2011.6069359"},{"issue":"5","key":"1224_CR30","first-page":"750","volume":"32","author":"R Chao","year":"2004","unstructured":"Chao, R., Zhang, K., Li, Y.-J.: An image fusion algorithm using wavelet transform. Acta Electr. Sin. 32(5), 750\u2013753 (2004)","journal-title":"Acta Electr. Sin."},{"key":"1224_CR31","unstructured":"Tomasi, C., Manduchi, R.: Bilateral filtering for gray and color images. In: Proceedings of 16th International Conference on Computer Vision (ICCV), pp. 839\u2013846 (1998)"},{"issue":"6","key":"1224_CR32","first-page":"36","volume":"4","author":"D Bhonsle","year":"2012","unstructured":"Bhonsle, D., Chandra, V., Sinha, G.: Medical image denoising using bilateral filter. Int. J. Image Graph. 4(6), 36\u201343 (2012)","journal-title":"Int. J. Image Graph."},{"key":"1224_CR33","doi-asserted-by":"crossref","unstructured":"Chang, C.C., Hsiao, J.Y., Hsieh, C.P.: An adaptive median filter for image denoising. In: Proceedings of 2nd International Symposium on Intelligent Information Technology Application, pp. 346\u2013350 (2008)","DOI":"10.1109\/IITA.2008.259"},{"issue":"1","key":"1224_CR34","doi-asserted-by":"publisher","first-page":"129","DOI":"10.1049\/ip-vis:20050975","volume":"152","author":"S Gupta","year":"2005","unstructured":"Gupta, S., Chauhan, R., Saxena, S.: Locally adaptive wavelet domain Bayesian processor for denoising medical ultrasound images using speckle modelling based on Rayleigh distribution. IEEE Proc. Vis. Image Signal Proc. 152(1), 129\u2013135 (2005)","journal-title":"IEEE Proc. Vis. Image Signal Proc."},{"key":"1224_CR35","doi-asserted-by":"crossref","unstructured":"Buades, A., Coll, B., Morel, J.M.: A non-local algorithm for image denoising. In: Proceedings of 5th IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), pp. 60\u201365 (2005)","DOI":"10.1109\/CVPR.2005.38"},{"key":"1224_CR36","doi-asserted-by":"publisher","first-page":"117","DOI":"10.1016\/j.neucom.2015.08.117","volume":"195","author":"X Mingliang","year":"2016","unstructured":"Mingliang, X., Pei, L., Mingyuan, L., et al.: Medical image denoising by parallel non-local means. Neurocomputing 195, 117\u2013122 (2016)","journal-title":"Neurocomputing"},{"issue":"8","key":"1224_CR37","doi-asserted-by":"publisher","first-page":"2080","DOI":"10.1109\/TIP.2007.901238","volume":"16","author":"K Dabov","year":"2007","unstructured":"Dabov, K., Foi, A., Katkovnik, V., et al.: Image denoising by sparse 3-D transform-domain collaborative filtering. IEEE Trans. Image Process. 16(8), 2080\u20132095 (2007)","journal-title":"IEEE Trans. Image Process."},{"issue":"1","key":"1224_CR38","doi-asserted-by":"publisher","first-page":"190","DOI":"10.1002\/mp.13252","volume":"46","author":"T Zhao","year":"2019","unstructured":"Zhao, T., Hoffman, J., McNitt-Gray, M., et al.: Ultra-low-dose CT image denoising using modified BM3D scheme tailored to data statistics. Med. Phys. 46(1), 190\u2013198 (2019)","journal-title":"Med. Phys."},{"key":"1224_CR39","doi-asserted-by":"publisher","first-page":"117","DOI":"10.1016\/j.neunet.2019.12.024","volume":"124","author":"C Tian","year":"2020","unstructured":"Tian, C., Xu, Y., Li, Z., et al.: Attention-guided CNN for image denoising. Neural Netw. 124, 117\u2013129 (2020)","journal-title":"Neural Netw."},{"key":"1224_CR40","doi-asserted-by":"crossref","unstructured":"Anwar, S., Barnes, N.: Real image denoising with feature attention. In: Proceedings of the IEEE International Conference on Computer Vision (ECCV), pp. 3155\u20133164 (2019)","DOI":"10.1109\/ICCV.2019.00325"},{"key":"1224_CR41","doi-asserted-by":"crossref","unstructured":"Chen, L.C., Zhu, Y., Papandreou, G., et al.: Encoder\u2013decoder with atrous separable convolution for semantic image segmentation. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 801\u2013818 (2018)","DOI":"10.1007\/978-3-030-01234-2_49"},{"issue":"8","key":"1224_CR42","doi-asserted-by":"publisher","first-page":"e00393","DOI":"10.1016\/j.heliyon.2017.e00393","volume":"3","author":"M Nishio","year":"2017","unstructured":"Nishio, M., Nagashima, C., Hirabayashi, S., et al.: Convolutional auto-encoder for image denoising of ultra-low-dose CT. Heliyon 3(8), e00393 (2017)","journal-title":"Heliyon"},{"issue":"7","key":"1224_CR43","doi-asserted-by":"publisher","first-page":"3142","DOI":"10.1109\/TIP.2017.2662206","volume":"26","author":"K Zhang","year":"2017","unstructured":"Zhang, K., Zuo, W., Chen, Y., et al.: Beyond a Gaussian denoiser: residual learning of deep CNN for image denoising. IEEE Trans. Image Process. 26(7), 3142\u20133155 (2017)","journal-title":"IEEE Trans. Image Process."},{"issue":"2","key":"1224_CR44","doi-asserted-by":"publisher","first-page":"224","DOI":"10.1109\/LSP.2017.2782270","volume":"25","author":"K Isogawa","year":"2018","unstructured":"Isogawa, K., Ida, T., Shiodera, T., et al.: Deep shrinkage convolutional neural network for adaptive noise reduction. IEEE Signal Process. Lett. 25(2), 224\u2013228 (2018)","journal-title":"IEEE Signal Process. Lett."},{"key":"1224_CR45","doi-asserted-by":"crossref","unstructured":"Ledig, C., Theis, L., Husz\u00e1r, F., et al.: Photo-realistic single image super-resolution using a generative adversarial network. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 4681\u20134690 (2017)","DOI":"10.1109\/CVPR.2017.19"},{"key":"1224_CR46","doi-asserted-by":"crossref","unstructured":"Wang, X., Yu, K., Wu, S., et al.: ESRGAN: enhanced super-resolution generative adversarial networks. In: Proceedings of the European Conference on Computer Vision (ECCV) (2018)","DOI":"10.1007\/978-3-030-11021-5_5"},{"key":"1224_CR47","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., et al.: Deep residual learning for image recognition. In: Proceedings of IEEE Conference on Computer Vision Pattern Recognition (CVPR), pp. 770\u2013778 (2016)","DOI":"10.1109\/CVPR.2016.90"},{"key":"1224_CR48","doi-asserted-by":"crossref","unstructured":"Burger, H.C., Schuler, C.J., Harmeling, S.: Image denoising: can plain neural networks compete with BM3D? In: Proceedings of IEEE Conference on Computer Vision Pattern Recognition (CVPR), pp. 2392\u20132399 (2012)","DOI":"10.1109\/CVPR.2012.6247952"},{"key":"1224_CR49","doi-asserted-by":"crossref","unstructured":"Zhang, K., Zuo, W., Gu, S., et al.: Learning deep CNN denoiser prior for image restoration. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 3929\u20133938 (2017)","DOI":"10.1109\/CVPR.2017.300"},{"key":"1224_CR50","doi-asserted-by":"crossref","unstructured":"Kupyn, O., Martyniuk, T., Wu, J., et al.: DeblurGAN-v2: deblurring (orders-of-magnitude) faster and better. In: Proceedings of IEEE International Conference on Computer Vision (ECCV), pp. 8878\u20138887 (2019)","DOI":"10.1109\/ICCV.2019.00897"},{"key":"1224_CR51","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Zhang, J., Guo, X.: Kindling the darkness: a practical low-light image enhancer. In: Proceedings of ACM International Conference on Multimedia (ACM MM), pp. 1632\u20131640 (2019)","DOI":"10.1145\/3343031.3350926"},{"key":"1224_CR52","unstructured":"Mao, X.J., Shen, C., Yang, Y.-B.: Image restoration using very deep convolutional encoder\u2013decoder networks with symmetric skip connections. In: Proceedings of Conference on Neural Information Processing Systems (NIPS), pp. 2810\u20132818 (2016)"},{"issue":"9","key":"1224_CR53","doi-asserted-by":"publisher","first-page":"4608","DOI":"10.1109\/TIP.2018.2839891","volume":"27","author":"K Zhang","year":"2018","unstructured":"Zhang, K., Zuo, W., Zhang, L.: FFDNet: toward a fast and flexible solution for CNN-based image denoising. IEEE Trans. Image Process. 27(9), 4608\u20134622 (2018)","journal-title":"IEEE Trans. Image Process."},{"issue":"1","key":"1224_CR54","doi-asserted-by":"publisher","first-page":"295","DOI":"10.1016\/S0031-3203(01)00152-2","volume":"35","author":"J Yang","year":"2002","unstructured":"Yang, J., Yang, J.Y.: Generalized K\u2013L transform based combined feature extraction. Pattern Recognit. 35(1), 295\u2013297 (2002)","journal-title":"Pattern Recognit."},{"issue":"6","key":"1224_CR55","doi-asserted-by":"publisher","first-page":"1369","DOI":"10.1016\/S0031-3203(02)00262-5","volume":"36","author":"J Yang","year":"2003","unstructured":"Yang, J., Yang, J.Y., Zhang, D., et al.: Feature fusion: parallel strategy vs. serial strategy. Pattern Recognit. 36(6), 1369\u20131381 (2003)","journal-title":"Pattern Recognit."},{"issue":"4","key":"1224_CR56","doi-asserted-by":"publisher","first-page":"598","DOI":"10.1109\/83.913594","volume":"10","author":"CJ Liu","year":"2001","unstructured":"Liu, C.J., Wechsler, H.: A shape and texture-based enhanced Fisher classifier for face recognition. IEEE Trans. Image Process. 10(4), 598\u2013608 (2001)","journal-title":"IEEE Trans. Image Process."},{"key":"1224_CR57","doi-asserted-by":"crossref","unstructured":"Hu, J., Shen, L., Albanie, S., et al.: Squeeze and excitation networks. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 7132\u20137141 (2018)","DOI":"10.1109\/CVPR.2018.00745"},{"key":"1224_CR58","doi-asserted-by":"crossref","unstructured":"Wang, X., Girshick, R., Gupta, A., et al.: Non-local neural networks. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 7794\u20137803 (2018)","DOI":"10.1109\/CVPR.2018.00813"},{"key":"1224_CR59","doi-asserted-by":"crossref","unstructured":"Woo, S., Park, J., Lee, J.Y., et al.: CBAM: convolutional block attention module. In: Proceedings of IEEE International Conference on Computer Vision (ECCV), pp. 3\u201319 (2018)","DOI":"10.1007\/978-3-030-01234-2_1"},{"key":"1224_CR60","unstructured":"Zhang, Y., Li, K., Li, K., et al.: Residual non-local attention networks for image restoration. In: Proceedings of International Conference on Learning Representations (ICLR) (2019)"},{"key":"1224_CR61","unstructured":"Loshchilov, I., Hutter, F.: SGDR: stochastic gradient descent with warm restarts. arXiv:1608.03983 (2016)"},{"issue":"4","key":"1224_CR62","doi-asserted-by":"publisher","first-page":"600","DOI":"10.1109\/TIP.2003.819861","volume":"13","author":"Z Wang","year":"2004","unstructured":"Wang, Z., Bovik, A.C., Sheikh, H.R., et al.: Image quality assessment: from error visibility to structural similarity. IEEE Trans. Image Process. 13(4), 600\u2013612 (2004)","journal-title":"IEEE Trans. Image Process."},{"key":"1224_CR63","unstructured":"Wang, Z., Simoncelli, E.P., Bovik, A.C.: Multiscale structural similarity for image quality assessment. In: Proceedings of 37th Asilomar Conference on Signals, Systems & Computers (ACSSC), pp. 1398\u20131402 (2003)"},{"key":"1224_CR64","unstructured":"Radiological Society of North America, COVID-19 radiography database. https:\/\/www.kaggle.com\/tawsifurrahman\/covid19-radiography-database (2020)"},{"key":"1224_CR65","unstructured":"Chung, A.: Figure 1 COVID-19 chest X-ray data initiative. https:\/\/github.com\/agchung\/Figure1-COVID-chestxray-dataset (2020)"},{"key":"1224_CR66","unstructured":"Cohen, J.P., Morrison, P., & Dao, L.: COVID-19 image data collection. https:\/\/github.com\/ieee8023\/covid-chestxray-dataset (2020)"},{"key":"1224_CR67","unstructured":"Chung, A.: Actualmed COVID-19 chest x-ray data initiative. https:\/\/github.com\/agchung\/Actualmed-COVID-chestxray-datasetActualmed-COVID-chestxray-dataset (2020)"},{"key":"1224_CR68","unstructured":"Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv:1409.1556 (2014)"},{"key":"1224_CR69","unstructured":"Lin Min, C.Q., Yan, S.: Network in network. arXiv:1312.4400 (2013)"}],"container-title":["Machine Vision and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00138-021-01224-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00138-021-01224-3\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00138-021-01224-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,9,2]],"date-time":"2024-09-02T19:59:35Z","timestamp":1725307175000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00138-021-01224-3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,6,28]]},"references-count":69,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2021,7]]}},"alternative-id":["1224"],"URL":"https:\/\/doi.org\/10.1007\/s00138-021-01224-3","relation":{},"ISSN":["0932-8092","1432-1769"],"issn-type":[{"type":"print","value":"0932-8092"},{"type":"electronic","value":"1432-1769"}],"subject":[],"published":{"date-parts":[[2021,6,28]]},"assertion":[{"value":"28 January 2021","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"19 May 2021","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"9 June 2021","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"28 June 2021","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The author(s) declared no conflicts of interest with respect to the research, authorship, and publication of this paper.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"This content has been made available to all.","name":"free","label":"Free to read"}],"article-number":"100"}}