{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,19]],"date-time":"2026-02-19T17:21:30Z","timestamp":1771521690599,"version":"3.50.1"},"publisher-location":"Cham","reference-count":23,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783032049643","type":"print"},{"value":"9783032049650","type":"electronic"}],"license":[{"start":{"date-parts":[[2025,9,19]],"date-time":"2025-09-19T00:00:00Z","timestamp":1758240000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,9,19]],"date-time":"2025-09-19T00:00:00Z","timestamp":1758240000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026]]},"DOI":"10.1007\/978-3-032-04965-0_64","type":"book-chapter","created":{"date-parts":[[2025,9,18]],"date-time":"2025-09-18T08:05:30Z","timestamp":1758182730000},"page":"684-694","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["WaveFormer: A 3D Transformer with\u00a0Wavelet-Driven Feature Representation for\u00a0Efficient Medical Image Segmentation"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7788-388X","authenticated-orcid":false,"given":"Md Mahfuz","family":"Al Hasan","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1668-338X","authenticated-orcid":false,"given":"Mahdi","family":"Zaman","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6530-1451","authenticated-orcid":false,"given":"Abdul","family":"Jawad","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4012-8394","authenticated-orcid":false,"given":"Alberto","family":"Santamaria-Pang","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7378-2379","authenticated-orcid":false,"given":"Ho Hin","family":"Lee","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0009-0008-9971-9790","authenticated-orcid":false,"given":"Ivan","family":"Tarapov","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4180-0963","authenticated-orcid":false,"given":"Kyle B.","family":"See","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0009-0005-7418-7247","authenticated-orcid":false,"given":"Md Shah","family":"Imran","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7954-5588","authenticated-orcid":false,"given":"Antika","family":"Roy","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4920-7104","authenticated-orcid":false,"given":"Yaser Pourmohammadi","family":"Fallah","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3347-5072","authenticated-orcid":false,"given":"Navid","family":"Asadizanjani","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8572-1864","authenticated-orcid":false,"given":"Reza","family":"Forghani","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,9,19]]},"reference":[{"key":"64_CR1","doi-asserted-by":"publisher","unstructured":"Azad, R., et al.: Laplacian-former: overcoming the limitations of vision transformers in local texture detection. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 736\u2013746. Springer (2023). https:\/\/doi.org\/10.1007\/978-3-031-43898-1_70","DOI":"10.1007\/978-3-031-43898-1_70"},{"key":"64_CR2","doi-asserted-by":"publisher","unstructured":"Bai, J., Yuan, L., Xia, S.T., Yan, S., Li, Z., Liu, W.: Improving vision transformers by revisiting high-frequency components. In: European Conference on Computer Vision, pp. 1\u201318. Springer (2022). https:\/\/doi.org\/10.1007\/978-3-031-20053-3_1","DOI":"10.1007\/978-3-031-20053-3_1"},{"issue":"4","key":"64_CR3","doi-asserted-by":"publisher","first-page":"600","DOI":"10.1162\/089892903321662976","volume":"15","author":"M Bar","year":"2003","unstructured":"Bar, M.: A cortical mechanism for triggering top-down facilitation in visual object recognition. J. Cogn. Neurosci. 15(4), 600\u2013609 (2003)","journal-title":"J. Cogn. Neurosci."},{"key":"64_CR4","unstructured":"Chen, J., et al.: TransUNet: transformers make strong encoders for medical image segmentation. arXiv preprint arXiv:2102.04306 (2021)"},{"key":"64_CR5","doi-asserted-by":"publisher","unstructured":"Hatamizadeh, A., Nath, V., Tang, Y., Yang, D., Roth, H.R., Xu, D.: Swin UNETR: Swin transformers for semantic segmentation of brain tumors in MRI images. In: International MICCAI Brainlesion workshop, pp. 272\u2013284. Springer (2022). https:\/\/doi.org\/10.1007\/978-3-031-08999-2_22","DOI":"10.1007\/978-3-031-08999-2_22"},{"key":"64_CR6","doi-asserted-by":"crossref","unstructured":"Hatamizadeh, A., et al.: UNETR: transformers for 3D medical image segmentation. In: Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision, pp. 574\u2013584 (2022)","DOI":"10.1109\/WACV51458.2022.00181"},{"key":"64_CR7","doi-asserted-by":"publisher","unstructured":"He, Y., Nath, V., Yang, D., Tang, Y., Myronenko, A., Xu, D.: SwinUnetr-v2: stronger swin transformers with stagewise convolutions for 3D medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 416\u2013426. Springer (2023). https:\/\/doi.org\/10.1007\/978-3-031-43901-8_40","DOI":"10.1007\/978-3-031-43901-8_40"},{"key":"64_CR8","doi-asserted-by":"crossref","unstructured":"Heidari, M., et al.: HIFormer: hierarchical multi-scale representations using transformers for medical image segmentation. In: Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision, pp. 6202\u20136212 (2023)","DOI":"10.1109\/WACV56688.2023.00614"},{"key":"64_CR9","doi-asserted-by":"crossref","unstructured":"Hu, J., Shen, L., Sun, G.: Squeeze-and-excitation networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 7132\u20137141 (2018)","DOI":"10.1109\/CVPR.2018.00745"},{"key":"64_CR10","unstructured":"Kazerooni, A.F., et\u00a0al.: The brain tumor segmentation (brats) challenge 2023: focus on pediatrics (CBTN-connect-DIPGR-ASNR-MICCAI brats-PEDS). ArXiv (2023)"},{"key":"64_CR11","unstructured":"Lee, H.H., Bao, S., Huo, Y., Landman, B.A.: 3D UX-Net: a large kernel volumetric convnet modernizing hierarchical transformer for medical image segmentation. arXiv preprint arXiv:2209.15076 (2022)"},{"key":"64_CR12","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2022.102616","volume":"82","author":"J Ma","year":"2022","unstructured":"Ma, J., et al.: Fast and low-GPU-memory abdomen CT organ segmentation: the flare challenge. Med. Image Anal. 82, 102616 (2022)","journal-title":"Med. Image Anal."},{"issue":"10","key":"64_CR13","doi-asserted-by":"publisher","first-page":"1993","DOI":"10.1109\/TMI.2014.2377694","volume":"34","author":"BH Menze","year":"2014","unstructured":"Menze, B.H., et al.: The multimodal brain tumor image segmentation benchmark (brats). IEEE Trans. Med. Imaging 34(10), 1993\u20132024 (2014)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"64_CR14","doi-asserted-by":"publisher","unstructured":"Myronenko, A., Yang, D., He, Y., Xu, D.: Automated 3D segmentation of kidneys and tumors in MICCAI kits 2023 challenge. In: International Challenge on Kidney and Kidney Tumor Segmentation, pp.\u00a01\u20137. Springer (2023). https:\/\/doi.org\/10.1007\/978-3-031-54806-2_1","DOI":"10.1007\/978-3-031-54806-2_1"},{"key":"64_CR15","doi-asserted-by":"publisher","unstructured":"Roy, S., et al.: MedNext: transformer-driven scaling of convnets for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 405\u2013415. Springer (2023). https:\/\/doi.org\/10.1007\/978-3-031-43901-8_39","DOI":"10.1007\/978-3-031-43901-8_39"},{"key":"64_CR16","unstructured":"Santamaria-Pang, A., et al.: Adversarial attacks with time-scale representations. arXiv preprint arXiv:2107.12473 (2021)"},{"issue":"10","key":"64_CR17","doi-asserted-by":"publisher","first-page":"2464","DOI":"10.1109\/78.157290","volume":"40","author":"MJ Shensa","year":"1992","unstructured":"Shensa, M.J., et al.: The discrete wavelet transform: wedding the a Trous and Mallat algorithms. IEEE Trans. Signal Process. 40(10), 2464\u20132482 (1992)","journal-title":"IEEE Trans. Signal Process."},{"issue":"1","key":"64_CR18","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1093\/cercor\/5.1.1","volume":"5","author":"S Ullman","year":"1995","unstructured":"Ullman, S.: Sequence seeking and counter streams: a computational model for bidirectional information flow in the visual cortex. Cereb. Cortex 5(1), 1\u201311 (1995)","journal-title":"Cereb. Cortex"},{"key":"64_CR19","unstructured":"Wang, P., Zheng, W., Chen, T., Wang, Z.: Anti-oversmoothing in deep vision transformers via the fourier domain analysis: From theory to practice. arXiv preprint arXiv:2203.05962 (2022)"},{"key":"64_CR20","first-page":"13974","volume":"35","author":"Z Wang","year":"2022","unstructured":"Wang, Z., Luo, H., Wang, P., Ding, F., Wang, F., Li, H.: VTC-LFC: vision transformer compression with low-frequency components. Adv. Neural. Inf. Process. Syst. 35, 13974\u201313988 (2022)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"64_CR21","doi-asserted-by":"publisher","unstructured":"Wenxuan, W., Chen, C., Meng, D., Hong, Y., Sen, Z., Jiangyun, L.: TransBTS: multimodal brain tumor segmentation using transformer. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp. 109\u2013119 (2021). https:\/\/doi.org\/10.1007\/978-3-030-87193-2_11","DOI":"10.1007\/978-3-030-87193-2_11"},{"key":"64_CR22","doi-asserted-by":"publisher","unstructured":"Xing, Z., Ye, T., Yang, Y., Liu, G., Zhu, L.: SegMamba: long-range sequential modeling mamba for 3D medical image segmentation . In: proceedings of Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2024, vol. LNCS 15008. Springer Nature Switzerland (2024). https:\/\/doi.org\/10.1007\/978-3-031-72111-3_54","DOI":"10.1007\/978-3-031-72111-3_54"},{"key":"64_CR23","unstructured":"Zhou, H.Y., Guo, J., Zhang, Y., Yu, L., Wang, L., Yu, Y.: NNFormer: interleaved transformer for volumetric segmentation. arXiv preprint arXiv:2109.03201 (2021)"}],"container-title":["Lecture Notes in Computer Science","Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2025"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-032-04965-0_64","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,18]],"date-time":"2025-09-18T22:03:40Z","timestamp":1758233020000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-032-04965-0_64"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,9,19]]},"ISBN":["9783032049643","9783032049650"],"references-count":23,"URL":"https:\/\/doi.org\/10.1007\/978-3-032-04965-0_64","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,9,19]]},"assertion":[{"value":"19 September 2025","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"The authors declare that they have no conflict of interest.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Disclosure of Interests"}},{"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":"Daejeon","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Korea (Republic of)","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2025","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"23 September 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"27 September 2025","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"28","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"miccai2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/conferences.miccai.org\/2025\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}