{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,9,12]],"date-time":"2024-09-12T20:56:19Z","timestamp":1726174579649},"publisher-location":"Cham","reference-count":34,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783031172465"},{"type":"electronic","value":"9783031172472"}],"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\/978-3-031-17247-2_14","type":"book-chapter","created":{"date-parts":[[2022,9,21]],"date-time":"2022-09-21T23:35:39Z","timestamp":1663803339000},"page":"133-144","update-policy":"http:\/\/dx.doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["MTD-GAN: Multi-task Discriminator Based Generative Adversarial Networks for Low-Dose CT Denoising"],"prefix":"10.1007","author":[{"given":"Sunggu","family":"Kyung","sequence":"first","affiliation":[]},{"given":"JongJun","family":"Won","sequence":"additional","affiliation":[]},{"given":"Seongyong","family":"Pak","sequence":"additional","affiliation":[]},{"given":"Gil-sun","family":"Hong","sequence":"additional","affiliation":[]},{"given":"Namkug","family":"Kim","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,9,22]]},"reference":[{"key":"14_CR1","doi-asserted-by":"publisher","first-page":"2277","DOI":"10.1056\/NEJMra072149","volume":"357","author":"DJ Brenner","year":"2007","unstructured":"Brenner, D.J., Hall, E.J.: Computed tomography\u2014an increasing source of radiation exposure. N. Engl. J. Med. 357, 2277\u20132284 (2007)","journal-title":"N. Engl. J. Med."},{"key":"14_CR2","doi-asserted-by":"publisher","first-page":"345","DOI":"10.1016\/S0140-6736(04)15433-0","volume":"363","author":"AB de Gonzalez","year":"2004","unstructured":"de Gonzalez, A.B., Darby, S.: Risk of cancer from diagnostic X-rays: estimates for the UK and 14 other countries. Lancet 363, 345\u2013351 (2004)","journal-title":"Lancet"},{"key":"14_CR3","unstructured":"Valentin, J.: International commission on radiological protection. In: The 2007 Recommendations of the International Commission on Radiological Protection, vol. 103, pp. 2\u20134. Annals of the ICRP, ICRP Publication (2007)"},{"key":"14_CR4","doi-asserted-by":"crossref","unstructured":"Wang, H., Wu, X., Huang, Z., Xing, E.P.: High-frequency component helps explain the generalization of convolutional neural networks. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 8684\u20138694 (2020)","DOI":"10.1109\/CVPR42600.2020.00871"},{"key":"14_CR5","doi-asserted-by":"publisher","first-page":"2524","DOI":"10.1109\/TMI.2017.2715284","volume":"36","author":"H Chen","year":"2017","unstructured":"Chen, H., et al.: Low-dose CT with a residual encoder-decoder convolutional neural network. IEEE Trans. Med. Imaging 36, 2524\u20132535 (2017)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"14_CR6","unstructured":"Goodfellow, I., et al.: Generative adversarial nets. Adv. Neural Inform. Process. Syst. 27, (2014)"},{"key":"14_CR7","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., Simoncelli, E.P.: Image quality assessment: from error visibility to structural similarity. IEEE Trans. Image Process. 13, 600\u2013612 (2004)","journal-title":"IEEE Trans. Image Process."},{"key":"14_CR8","doi-asserted-by":"crossref","unstructured":"Liang, T., Jin, Y., Li, Y., Wang, T.: EDCNN: Edge enhancement-based densely connected network with compound loss for low-dose CT denoising. In: 2020 15th IEEE International Conference on Signal Processing (ICSP), pp. 193\u2013198. IEEE (2020)","DOI":"10.1109\/ICSP48669.2020.9320928"},{"key":"14_CR9","unstructured":"Zamir, S.W., Arora, A., Khan, S., Hayat, M., Khan, F.S., Yang, M.-H.: Restormer: efficient transformer for high-resolution image restoration. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 5728\u20135739 (20220)"},{"key":"14_CR10","doi-asserted-by":"crossref","unstructured":"Wang, D., Fan, F., Wu, Z., Liu, R., Wang, F., Yu, H.: CTformer: Convolution-free Token2Token Dilated Vision Transformer for Low-dose CT Denoising. arXiv preprint arXiv:2202.13517 (2022)","DOI":"10.1088\/1361-6560\/acc000"},{"key":"14_CR11","doi-asserted-by":"publisher","first-page":"1348","DOI":"10.1109\/TMI.2018.2827462","volume":"37","author":"Q Yang","year":"2018","unstructured":"Yang, Q., et al.: Low-dose CT image denoising using a generative adversarial network with Wasserstein distance and perceptual loss. IEEE Trans. Med. Imaging 37, 1348\u20131357 (2018)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"14_CR12","doi-asserted-by":"publisher","first-page":"269","DOI":"10.1038\/s42256-019-0057-9","volume":"1","author":"H Shan","year":"2019","unstructured":"Shan, H., et al.: Competitive performance of a modularized deep neural network compared to commercial algorithms for low-dose CT image reconstruction. Nat. Mach. Intell. 1, 269\u2013276 (2019)","journal-title":"Nat. Mach. Intell."},{"key":"14_CR13","doi-asserted-by":"crossref","unstructured":"Schonfeld, E., Schiele, B., Khoreva, A.: A u-net based discriminator for generative adversarial networks. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 8207\u20138216 (2020)","DOI":"10.1109\/CVPR42600.2020.00823"},{"key":"14_CR14","first-page":"1","volume":"71","author":"Z Huang","year":"2021","unstructured":"Huang, Z., Zhang, J., Zhang, Y., Shan, H.: DU-GAN: generative adversarial networks with dual-domain U-Net-based discriminators for low-dose CT denoising. IEEE Trans. Instrum. Meas. 71, 1\u201312 (2021)","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"14_CR15","doi-asserted-by":"crossref","unstructured":"Lin, C.H., Chang, C.-C., Chen, Y.-S., Juan, D.-C., Wei, W., Chen, H.-T.: COCO-GAN: generation by parts via conditional coordinating. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 4512\u20134521 (2020)","DOI":"10.1109\/ICCV.2019.00461"},{"key":"14_CR16","unstructured":"Zhang, H., Goodfellow, I., Metaxas, D., Odena, A.: Self-attention generative adversarial networks. In: International Conference on Machine Learning, pp. 7354\u20137363. PMLR (2019)"},{"key":"14_CR17","doi-asserted-by":"crossref","unstructured":"Vandenhende, S., Georgoulis, S., Van Gansbeke, W., Proesmans, M., Dai, D., Van Gool, L.: Multi-task learning for dense prediction tasks: a survey. IEEE Trans. Pattern Anal. Mach. Intell. 44 (2021)","DOI":"10.1109\/TPAMI.2021.3054719"},{"key":"14_CR18","doi-asserted-by":"publisher","first-page":"1424","DOI":"10.1109\/TGRS.2020.3003341","volume":"59","author":"R Hang","year":"2020","unstructured":"Hang, R., Zhou, F., Liu, Q., Ghamisi, P.: Classification of hyperspectral images via multitask generative adversarial networks. IEEE Trans. Geosci. Remote Sens. 59, 1424\u20131436 (2020)","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"14_CR19","doi-asserted-by":"publisher","first-page":"304","DOI":"10.1016\/j.neucom.2019.07.107","volume":"398","author":"MS Rad","year":"2020","unstructured":"Rad, M.S., et al.: Benefiting from multitask learning to improve single image super-resolution. Neurocomputing 398, 304\u2013313 (2020)","journal-title":"Neurocomputing"},{"key":"14_CR20","doi-asserted-by":"crossref","unstructured":"Liu, M.-Y., et al.: Few-shot unsupervised image-to-image translation. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 10551\u201310560 (2019)","DOI":"10.1109\/ICCV.2019.01065"},{"key":"14_CR21","doi-asserted-by":"publisher","unstructured":"Cha, J., Chun, S., Lee, G., Lee, B., Kim, S., Lee, H.: Few-shot compositional font generation with dual memory. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, JM. (eds.) European Conference on Computer Vision. LNIP, vol. 12364, pp. 735\u2013751. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-58529-7_43","DOI":"10.1007\/978-3-030-58529-7_43"},{"key":"14_CR22","doi-asserted-by":"crossref","unstructured":"Wan, W., Lee, H.J.: Generative adversarial multi-task learning for face sketch synthesis and recognition. In: 2019 IEEE International Conference on Image Processing (ICIP), pp. 4065\u20134069. IEEE (2019)","DOI":"10.1109\/ICIP.2019.8803617"},{"key":"14_CR23","doi-asserted-by":"crossref","unstructured":"Liu, Y., Wang, Z., Jin, H., Wassell, I.: Multi-task adversarial network for disentangled feature learning. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3743\u20133751 (2018)","DOI":"10.1109\/CVPR.2018.00394"},{"key":"14_CR24","doi-asserted-by":"crossref","unstructured":"Kyung, S., et al.: Improved performance and robustness of multi-task representation learning with consistency loss between pretexts for intracranial hemorrhage identification in head CT. Med. Image Anal. 81, 102489 (2022)","DOI":"10.1016\/j.media.2022.102489"},{"key":"14_CR25","unstructured":"Miyato, T., Kataoka, T., Koyama, M., Yoshida, Y.: Spectral normalization for generative adversarial networks. arXiv preprint arXiv:1802.05957 (2018)"},{"key":"14_CR26","doi-asserted-by":"crossref","unstructured":"Mao, X., Li, Q., Xie, H., Lau, R.Y., Wang, Z., Paul Smolley, S.: Least squares generative adversarial networks. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2794\u20132802 (2016)","DOI":"10.1109\/ICCV.2017.304"},{"key":"14_CR27","unstructured":"Zhang, H., Zhang, Z., Odena, A., Lee, H.: Consistency regularization for generative adversarial networks. arXiv preprint arXiv:1910.12027 (2019)"},{"key":"14_CR28","doi-asserted-by":"crossref","unstructured":"Katznelson, Y.: An introduction to Harmonic Analysis. Cambridge University Press (2004)","DOI":"10.1017\/CBO9781139165372"},{"key":"14_CR29","first-page":"4479","volume":"33","author":"L Chi","year":"2020","unstructured":"Chi, L., Jiang, B., Mu, Y.: Fast fourier convolution. Adv. Neural. Inf. Process. Syst. 33, 4479\u20134488 (2020)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"14_CR30","doi-asserted-by":"crossref","unstructured":"Zamir, S.W., et al.: Multi-stage progressive image restoration. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 14821\u201314831 (2021)","DOI":"10.1109\/CVPR46437.2021.01458"},{"key":"14_CR31","doi-asserted-by":"crossref","unstructured":"Kramer, M., et al.: Computed tomography angiography of carotid arteries and vertebrobasilar system: a simulation study for radiation dose reduction. Medicine 94 (2015)","DOI":"10.1097\/MD.0000000000001058"},{"key":"14_CR32","first-page":"5824","volume":"33","author":"T Yu","year":"2020","unstructured":"Yu, T., Kumar, S., Gupta, A., Levine, S., Hausman, K., Finn, C.: Gradient surgery for multi-task learning. Adv. Neural. Inf. Process. Syst. 33, 5824\u20135836 (2020)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"14_CR33","doi-asserted-by":"crossref","unstructured":"Sajjadi, M.S., Scholkopf, B., Hirsch, M.: Enhancenet: single image super-resolution through automated texture synthesis. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 4491\u20134500 (2017)","DOI":"10.1109\/ICCV.2017.481"},{"key":"14_CR34","unstructured":"Heusel, M., Ramsauer, H., Unterthiner, T., Nessler, B., Hochreiter, S.: Gans trained by a two time-scale update rule converge to a local nash equilibrium. Adv. Neural Inform. Process. Syst. 30 (2017)"}],"container-title":["Lecture Notes in Computer Science","Machine Learning for Medical Image Reconstruction"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-17247-2_14","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,11,27]],"date-time":"2023-11-27T10:34:49Z","timestamp":1701081289000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-17247-2_14"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783031172465","9783031172472"],"references-count":34,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-17247-2_14","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2022]]},"assertion":[{"value":"22 September 2022","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"MLMIR","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Workshop on Machine Learning for Medical Image Reconstruction","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Singapore","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Singapore","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2022","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"22 September 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"22 September 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"5","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"mlmir2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/sites.google.com\/view\/mlmir2022\/home","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Microsoft CMT","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"19","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"15","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"79% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"2,43","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"1,58","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}