{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,15]],"date-time":"2026-03-15T03:05:40Z","timestamp":1773543940617,"version":"3.50.1"},"publisher-location":"Cham","reference-count":26,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031164361","type":"print"},{"value":"9783031164378","type":"electronic"}],"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_8","type":"book-chapter","created":{"date-parts":[[2022,9,15]],"date-time":"2022-09-15T18:13:04Z","timestamp":1663265584000},"page":"78-87","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":15,"title":["FFCNet: Fourier Transform-Based Frequency Learning and\u00a0Complex Convolutional Network for\u00a0Colon Disease Classification"],"prefix":"10.1007","author":[{"given":"Kai-Ni","family":"Wang","sequence":"first","affiliation":[]},{"given":"Yuting","family":"He","sequence":"additional","affiliation":[]},{"given":"Shuaishuai","family":"Zhuang","sequence":"additional","affiliation":[]},{"given":"Juzheng","family":"Miao","sequence":"additional","affiliation":[]},{"given":"Xiaopu","family":"He","sequence":"additional","affiliation":[]},{"given":"Ping","family":"Zhou","sequence":"additional","affiliation":[]},{"given":"Guanyu","family":"Yang","sequence":"additional","affiliation":[]},{"given":"Guang-Quan","family":"Zhou","sequence":"additional","affiliation":[]},{"given":"Shuo","family":"Li","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,9,16]]},"reference":[{"issue":"23","key":"8_CR1","doi-asserted-by":"publisher","first-page":"2564","DOI":"10.1001\/jama.2016.5989","volume":"315","author":"K Bibbins-Domingo","year":"2016","unstructured":"Bibbins-Domingo, K., et al.: Screening for colorectal cancer: US preventive services task force recommendation statement. JAMA 315(23), 2564\u20132575 (2016)","journal-title":"JAMA"},{"key":"8_CR2","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2020.101653","volume":"62","author":"G Carneiro","year":"2020","unstructured":"Carneiro, G., Pu, L.Z.C.T., Singh, R., Burt, A.: Deep learning uncertainty and confidence calibration for the five-class polyp classification from colonoscopy. Med. Image Anal. 62, 101653 (2020)","journal-title":"Med. Image Anal."},{"key":"8_CR3","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":"8_CR4","first-page":"3965","volume":"34","author":"Z Dai","year":"2021","unstructured":"Dai, Z., Liu, H., Le, Q.V., Tan, M.: CoAtnNet: marrying convolution and attention for all data sizes. Adv. Neural Inf. Process. Syst. 34, 3965\u20133977 (2021)","journal-title":"Adv. Neural Inf. Process. Syst."},{"issue":"10","key":"8_CR5","first-page":"1674","volume":"143","author":"A Elbediwy","year":"2016","unstructured":"Elbediwy, A., et al.: Integrin signalling regulates YAP and TAZ to control skin homeostasis. Development 143(10), 1674\u20131687 (2016)","journal-title":"Development"},{"issue":"2","key":"8_CR6","doi-asserted-by":"publisher","first-page":"377","DOI":"10.1109\/TMI.2019.2927101","volume":"39","author":"Y Han","year":"2019","unstructured":"Han, Y., Sunwoo, L., Ye, J.C.: k-Space deep learning for accelerated MRI. IEEE Trans. Med. Imaging 39(2), 377\u2013386 (2019)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"8_CR7","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":"8_CR8","unstructured":"Howard, A.G., et al.: MobileNets: efficient convolutional neural networks for mobile vision applications. arXiv preprint arXiv:1704.04861 (2017)"},{"key":"8_CR9","doi-asserted-by":"crossref","unstructured":"Huang, G., Liu, Z., Van Der Maaten, L., Weinberger, K.Q.: Densely connected convolutional networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4700\u20134708 (2017)","DOI":"10.1109\/CVPR.2017.243"},{"issue":"2","key":"8_CR10","doi-asserted-by":"publisher","first-page":"418","DOI":"10.1053\/j.gastro.2019.06.043","volume":"158","author":"U Ladabaum","year":"2020","unstructured":"Ladabaum, U., Dominitz, J.A., Kahi, C., Schoen, R.E.: Strategies for colorectal cancer screening. Gastroenterology 158(2), 418\u2013432 (2020)","journal-title":"Gastroenterology"},{"key":"8_CR11","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2021.102052","volume":"71","author":"X Liu","year":"2021","unstructured":"Liu, X., Guo, X., Liu, Y., Yuan, Y.: Consolidated domain adaptive detection and localization framework for cross-device colonoscopic images. Med. Image Anal. 71, 102052 (2021)","journal-title":"Med. Image Anal."},{"issue":"1","key":"8_CR12","doi-asserted-by":"publisher","first-page":"197","DOI":"10.3390\/ijms18010197","volume":"18","author":"I M\u00e1rmol","year":"2017","unstructured":"M\u00e1rmol, I., S\u00e1nchez-de-Diego, C., Pradilla Dieste, A., Cerrada, E., Rodriguez Yoldi, M.J.: Colorectal carcinoma: a general overview and future perspectives in colorectal cancer. Int. J. Mol. Sci. 18(1), 197 (2017)","journal-title":"Int. J. Mol. Sci."},{"issue":"8","key":"8_CR13","doi-asserted-by":"publisher","first-page":"2027","DOI":"10.1053\/j.gastro.2018.04.003","volume":"154","author":"M Misawa","year":"2018","unstructured":"Misawa, M., et al.: Artificial intelligence-assisted polyp detection for colonoscopy: initial experience. Gastroenterology 154(8), 2027\u20132029 (2018)","journal-title":"Gastroenterology"},{"key":"8_CR14","unstructured":"Paszke, A., et al.: Pytorch: an imperative style, high- performance deep learning library. In: Advances in Neural Information Processing Systems 32 (2019)"},{"key":"8_CR15","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2020.101897","volume":"68","author":"HA Qadir","year":"2021","unstructured":"Qadir, H.A., Shin, Y., Solhusvik, J., Bergsland, J., Aabakken, L., Balasingham, I.: Toward real-time polyp detection using fully CNNs for 2D Gaussian shapes prediction. Med. Image Anal. 68, 101897 (2021)","journal-title":"Med. Image Anal."},{"key":"8_CR16","first-page":"980","volume":"34","author":"Y Rao","year":"2021","unstructured":"Rao, Y., Zhao, W., Zhu, Z., Lu, J., Zhou, J.: Global filter networks for image classification. Adv. Neural Inf. Process. Syst. 34, 980\u2013993 (2021)","journal-title":"Adv. Neural Inf. Process. Syst."},{"issue":"1","key":"8_CR17","doi-asserted-by":"publisher","first-page":"307","DOI":"10.1053\/j.gastro.2017.05.013","volume":"153","author":"DK Rex","year":"2017","unstructured":"Rex, D.K., et al.: Colorectal cancer screening: recommendations for physicians and patients from the US multi-society task force on colorectal cancer. Gastroenterology 153(1), 307\u2013323 (2017)","journal-title":"Gastroenterology"},{"key":"8_CR18","unstructured":"Stuchi, J.A., Boccato, L., Attux, R.: Frequency learning for image classification. CoRR abs\/2006.15476 (2020)"},{"key":"8_CR19","doi-asserted-by":"crossref","unstructured":"Szegedy, C., et al.: Going deeper with convolutions. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1\u20139 (2015)","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"8_CR20","unstructured":"Tan, M., Le, Q.: EfficientNet: rethinking model scaling for convolutional neural networks. In: International Conference on Machine Learning, pp. 6105\u20136114. PMLR (2019)"},{"key":"8_CR21","unstructured":"Trabelsi, C., et al.: Deep complex networks. CoRR abs\/1705.09792 (2017)"},{"key":"8_CR22","first-page":"1","volume":"2021","author":"Y Wang","year":"2021","unstructured":"Wang, Y., Feng, Z., Song, L., Liu, X., Liu, S.: Multiclassification of endoscopic colonoscopy images based on deep transfer learning. Comput. Math. Methods Med. 2021, 1\u201321 (2021)","journal-title":"Comput. Math. Methods Med."},{"key":"8_CR23","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"699","DOI":"10.1007\/978-3-030-87193-2_66","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2021","author":"J Wei","year":"2021","unstructured":"Wei, J., Hu, Y., Zhang, R., Li, Z., Zhou, S.K., Cui, S.: Shallow attention network for polyp segmentation. In: de Bruijne, M., et al. (eds.) MICCAI 2021. LNCS, vol. 12901, pp. 699\u2013708. Springer, Cham (2021). https:\/\/doi.org\/10.1007\/978-3-030-87193-2_66"},{"key":"8_CR24","doi-asserted-by":"crossref","unstructured":"Xu, K., Qin, M., Sun, F., Wang, Y., Chen, Y.K., Ren, F.: Learning in the frequency domain. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 1740\u20131749 (2020)","DOI":"10.1109\/CVPR42600.2020.00181"},{"issue":"1","key":"8_CR25","doi-asserted-by":"publisher","first-page":"41","DOI":"10.1109\/JBHI.2016.2635662","volume":"21","author":"R Zhang","year":"2016","unstructured":"Zhang, R., et al.: Automatic detection and classification of colorectal polyps by transferring low- level CNN features from nonmedical domain. IEEE J. Biomed. Health Inform. 21(1), 41\u201347 (2016)","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"8_CR26","doi-asserted-by":"publisher","first-page":"209","DOI":"10.1016\/j.patcog.2018.05.026","volume":"83","author":"R Zhang","year":"2018","unstructured":"Zhang, R., Zheng, Y., Poon, C.C., Shen, D., Lau, J.Y.: Polyp detection during colonoscopy using a regression-based convolutional neural network with a tracker. Pattern Recogn. 83, 209\u2013219 (2018)","journal-title":"Pattern Recogn."}],"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_8","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,12]],"date-time":"2024-03-12T14:03:04Z","timestamp":1710252184000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-16437-8_8"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783031164361","9783031164378"],"references-count":26,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-16437-8_8","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"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)"}}]}}