{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,27]],"date-time":"2025-03-27T11:15:20Z","timestamp":1743074120623,"version":"3.40.3"},"publisher-location":"Cham","reference-count":22,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031164361"},{"type":"electronic","value":"9783031164378"}],"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\/978-3-031-16437-8_35","type":"book-chapter","created":{"date-parts":[[2022,9,15]],"date-time":"2022-09-15T18:13:04Z","timestamp":1663265584000},"page":"366-375","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Residual Wavelon Convolutional Networks for\u00a0Characterization of\u00a0Disease Response on\u00a0MRI"],"prefix":"10.1007","author":[{"given":"Amir Reza","family":"Sadri","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Thomas","family":"DeSilvio","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Prathyush","family":"Chirra","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sneha","family":"Singh","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Satish E.","family":"Viswanath","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2022,9,16]]},"reference":[{"key":"35_CR1","doi-asserted-by":"publisher","DOI":"10.1002\/9781118535561","volume-title":"Nonlinear System Identification: NARMAX Methods in the Time, Frequency, and Spatio-Temporal Domains","author":"SA Billings","year":"2013","unstructured":"Billings, S.A.: Nonlinear System Identification: NARMAX Methods in the Time, Frequency, and Spatio-Temporal Domains. Wiley, Hoboken (2013)"},{"key":"35_CR2","doi-asserted-by":"publisher","first-page":"102209","DOI":"10.1016\/j.media.2021.102209","volume":"74","author":"J Gu","year":"2021","unstructured":"Gu, J., Yang, T.S., Ye, J.C., Yang, D.H.: CycleGAN denoising of extreme low-dose cardiac CT using wavelet-assisted noise disentanglement. Med. Image Anal. 74, 102209 (2021)","journal-title":"Med. Image Anal."},{"issue":"1","key":"35_CR3","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.media.2015.03.004","volume":"23","author":"L Chen","year":"2015","unstructured":"Chen, L., et al.: Super-resolved enhancing and edge deghosting (SEED) for spatiotemporally encoded single-shot MRI. Med. Image Anal. 23(1), 1\u201314 (2015)","journal-title":"Med. Image Anal."},{"issue":"1","key":"35_CR4","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41598-017-10649-8","volume":"7","author":"J Lao","year":"2017","unstructured":"Lao, J., et al.: A deep learning-based radiomics model for prediction of survival in glioblastoma multiforme. Sci. Rep. 7(1), 1\u20138 (2017)","journal-title":"Sci. Rep."},{"issue":"1","key":"35_CR5","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/s41598-020-79139-8","volume":"11","author":"J Liu","year":"2021","unstructured":"Liu, J., Li, P., Tang, X., Li, J., Chen, J.: Research on improved convolutional wavelet neural network. Sci. Rep. 11(1), 1\u201314 (2021)","journal-title":"Sci. Rep."},{"issue":"11","key":"35_CR6","doi-asserted-by":"publisher","first-page":"3980","DOI":"10.1109\/TPAMI.2020.2990339","volume":"43","author":"A Zaeemzadeh","year":"2020","unstructured":"Zaeemzadeh, A., Rahnavard, N., Shah, M.: Norm-preservation: why residual networks can become extremely deep? IEEE Trans. Pattern Anal. Mach. Intell. 43(11), 3980\u20133990 (2020)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"35_CR7","doi-asserted-by":"publisher","first-page":"7074","DOI":"10.1109\/TIP.2021.3101395","volume":"30","author":"Q Li","year":"2021","unstructured":"Li, Q., Shen, L., Guo, S., Lai, Z.: Wavecnet: wavelet integrated CNNs to suppress aliasing effect for noise-robust image classification. IEEE Trans. Image Process. 30, 7074\u20137089 (2021)","journal-title":"IEEE Trans. Image Process."},{"key":"35_CR8","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770\u2013778 (2016)","DOI":"10.1109\/CVPR.2016.90"},{"key":"35_CR9","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2021.102031","volume":"71","author":"H Xie","year":"2021","unstructured":"Xie, H., et al.: Cross-attention multi-branch network for fundus diseases classification using SLO images. Med. Image Anal. 71, 102031 (2021)","journal-title":"Med. Image Anal."},{"issue":"8","key":"35_CR10","doi-asserted-by":"publisher","first-page":"1872","DOI":"10.1109\/TPAMI.2012.230","volume":"35","author":"J Bruna","year":"2013","unstructured":"Bruna, J., Mallat, S.: Invariant scattering convolution networks. IEEE Trans. Pattern Anal. Mach. Intell. 35(8), 1872\u20131886 (2013)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"issue":"3","key":"35_CR11","doi-asserted-by":"publisher","first-page":"1845","DOI":"10.1109\/TIT.2017.2776228","volume":"64","author":"T Wiatowski","year":"2017","unstructured":"Wiatowski, T., B\u00f6lcskei, H.: A mathematical theory of deep convolutional neural networks for feature extraction. IEEE Trans. Inf. Theor. 64(3), 1845\u20131866 (2017)","journal-title":"IEEE Trans. Inf. Theor."},{"key":"35_CR12","unstructured":"Rodriguez, M.X.B., et al.: Deep adaptive wavelet network. In: Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision, pp. 3111\u20133119 (2020)"},{"key":"35_CR13","doi-asserted-by":"publisher","first-page":"354","DOI":"10.1016\/j.patcog.2017.10.013","volume":"77","author":"J Gu","year":"2018","unstructured":"Gu, J., et al.: Recent advances in convolutional neural networks. Pattern Recogn. 77, 354\u2013377 (2018)","journal-title":"Pattern Recogn."},{"key":"35_CR14","doi-asserted-by":"publisher","DOI":"10.1002\/9781118596272","volume-title":"Wavelet Neural Networks: with Applications in Financial Engineering, Chaos, and Classification","author":"AK Alexandridis","year":"2014","unstructured":"Alexandridis, A.K., Zapranis, A.D.: Wavelet Neural Networks: with Applications in Financial Engineering, Chaos, and Classification. Wiley, Hoboken (2014)"},{"key":"35_CR15","unstructured":"Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014)"},{"key":"35_CR16","doi-asserted-by":"publisher","first-page":"120613","DOI":"10.1109\/ACCESS.2021.3105355","volume":"9","author":"K Biswas","year":"2021","unstructured":"Biswas, K., Kumar, S., Banerjee, S., Pandey, A.K.: Tanhsoft-dynamic trainable activation functions for faster learning and better performance. IEEE Access 9, 120613\u2013120623 (2021)","journal-title":"IEEE Access"},{"issue":"184","key":"35_CR17","first-page":"1","volume":"21","author":"G Naitzat","year":"2020","unstructured":"Naitzat, G., Zhitnikov, A., Lim, L.H.: Topology of deep neural networks. J. Mach. Learn. Res. 21(184), 1\u201340 (2020)","journal-title":"J. Mach. Learn. Res."},{"key":"35_CR18","doi-asserted-by":"crossref","unstructured":"Oyedotun, O.K., Al Ismaeil, K., Aouada, D.: Why is everyone training very deep neural network with skip connections? IEEE Trans. Neural Netw. Learn. Syst. (2022)","DOI":"10.1109\/ICPR48806.2021.9412508"},{"key":"35_CR19","doi-asserted-by":"crossref","unstructured":"Furusho, Y., Ikeda, K.: Theoretical analysis of skip connections and batch normalization from generalization and optimization perspectives. APSIPA Trans. Sig. Inf. Process. 9 (2020)","DOI":"10.1017\/ATSIP.2020.7"},{"issue":"1","key":"35_CR20","doi-asserted-by":"publisher","first-page":"56","DOI":"10.1038\/s42256-019-0138-9","volume":"2","author":"SM Lundberg","year":"2020","unstructured":"Lundberg, S.M., et al.: From local explanations to global understanding with explainable AI for trees. Nature Mach. Intell. 2(1), 56\u201367 (2020)","journal-title":"Nature Mach. Intell."},{"issue":"10","key":"35_CR21","doi-asserted-by":"publisher","first-page":"749","DOI":"10.1038\/s41551-018-0304-0","volume":"2","author":"SM Lundberg","year":"2018","unstructured":"Lundberg, S.M., et al.: Explainable machine-learning predictions for the prevention of hypoxaemia during surgery. Nature Biomed. Eng. 2(10), 749 (2018)","journal-title":"Nature Biomed. Eng."},{"key":"35_CR22","unstructured":"Ancona, M., Oztireli, C., Gross, M.: Explaining deep neural networks with a polynomial time algorithm for Shapley value approximation. In: International Conference on Machine Learning, pp. 272\u2013281. PMLR (2019)"}],"container-title":["Lecture Notes in Computer Science","Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2022"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-16437-8_35","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,12]],"date-time":"2024-03-12T14:07:27Z","timestamp":1710252447000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-16437-8_35"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783031164361","9783031164378"],"references-count":22,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-16437-8_35","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":"16 September 2022","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"MICCAI","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Medical Image Computing and Computer-Assisted Intervention","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":"18 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":"25","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"miccai2022","order":10,"name":"conference_id","label":"Conference ID","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 Conference","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"1831","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":"574","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":"31% - 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":"3","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":"5","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":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}