{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,16]],"date-time":"2026-07-16T05:54:06Z","timestamp":1784181246687,"version":"3.55.0"},"publisher-location":"Cham","reference-count":23,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783032051134","type":"print"},{"value":"9783032051141","type":"electronic"}],"license":[{"start":{"date-parts":[[2025,9,21]],"date-time":"2025-09-21T00:00:00Z","timestamp":1758412800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,9,21]],"date-time":"2025-09-21T00:00:00Z","timestamp":1758412800000},"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\/978-3-032-05114-1_27","type":"book-chapter","created":{"date-parts":[[2025,9,20]],"date-time":"2025-09-20T14:11:07Z","timestamp":1758377467000},"page":"278-288","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Frequency-Domain Multi-modal Fusion for\u00a0Language-Guided Medical Image Segmentation"],"prefix":"10.1007","author":[{"given":"Bo","family":"Yu","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jianhua","family":"Yang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zetao","family":"Du","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yan","family":"Huang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chenglong","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Liang","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,9,21]]},"reference":[{"key":"27_CR1","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"234","DOI":"10.1007\/978-3-319-24574-4_28","volume-title":"Medical Image Computing and Computer-Assisted Intervention \u2013 MICCAI 2015","author":"O Ronneberger","year":"2015","unstructured":"Ronneberger, O., Fischer, P., Brox, T.: U-Net: convolutional networks for biomedical image segmentation. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9351, pp. 234\u2013241. Springer, Cham (2015). https:\/\/doi.org\/10.1007\/978-3-319-24574-4_28"},{"key":"27_CR2","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1007\/978-3-030-00889-5_1","volume-title":"Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support","author":"Z Zhou","year":"2018","unstructured":"Zhou, Z., Rahman Siddiquee, M.M., Tajbakhsh, N., Liang, J.: UNet++: a nested U-Net architecture for medical image segmentation. In: Stoyanov, D., et al. (eds.) DLMIA\/ML-CDS -2018. LNCS, vol. 11045, pp. 3\u201311. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-00889-5_1"},{"key":"27_CR3","unstructured":"Chen, J., et al.: TransUNet: transformers make strong encoders for medical image segmentation. arXiv preprint arXiv:2102.04306 (2021)"},{"key":"27_CR4","series-title":"LNCS","doi-asserted-by":"publisher","first-page":"205","DOI":"10.1007\/978-3-031-25066-8_9","volume-title":"ECCV 2022","author":"H Cao","year":"2022","unstructured":"Cao, H., et al.: Swin-UNet: UNet-like pure transformer for medical image segmentation. In: Karlinsky, L., Michaeli, T., Nishino, K. (eds.) ECCV 2022. LNCS, vol. 13803, pp. 205\u2013218. Springer, Cham (2022). https:\/\/doi.org\/10.1007\/978-3-031-25066-8_9"},{"key":"27_CR5","unstructured":"Azad, R., et al.: MedClip: contrastive learning from unpaired medical images and text. In: Proceedings of the Conference on Empirical Methods in Natural Language Conference, vol. 2022, pp. 38\u201376 (2022)"},{"issue":"1","key":"27_CR6","doi-asserted-by":"publisher","first-page":"96","DOI":"10.1109\/TMI.2023.3291719","volume":"43","author":"Z Li","year":"2023","unstructured":"Li, Z., et al.: LViT: language meets vision transformer in medical image segmentation. IEEE Trans. Med. Imaging 43(1), 96\u2013107 (2023)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"27_CR7","doi-asserted-by":"crossref","unstructured":"Hu, J., et al.: LGA: a language guide adapter for advancing the SAM model\u2019s capabilities in medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 610\u2013620 (2024)","DOI":"10.1007\/978-3-031-72390-2_57"},{"key":"27_CR8","series-title":"LNCS","doi-asserted-by":"publisher","first-page":"724","DOI":"10.1007\/978-3-031-43901-8_69","volume-title":"MICCAI 2023","author":"Y Zhong","year":"2023","unstructured":"Zhong, Y., Xu, M., Liang, K., Chen, K., Wu, M.: Ariadne\u2019s thread: using text prompts to improve segmentation of infected areas from chest X-ray images. In: Greenspan, H., et al. (eds.) MICCAI 2023. LNCS, vol. 14223, pp. 724\u2013733. Springer, Cham (2023). https:\/\/doi.org\/10.1007\/978-3-031-43901-8_69"},{"key":"27_CR9","series-title":"LNCS","doi-asserted-by":"publisher","first-page":"77","DOI":"10.1007\/978-3-031-72384-1_8","volume-title":"MICCAI 2024","author":"Y Chen","year":"2024","unstructured":"Chen, Y., et al.: CausalCLIPSeg: unlocking CLIP\u2019s potential in referring medical image segmentation with causal intervention. In: Linguraru, M.G., et al. (eds.) MICCAI 2024. LNCS, vol. 15003, pp. 77\u201387. Springer, Cham (2024). https:\/\/doi.org\/10.1007\/978-3-031-72384-1_8"},{"key":"27_CR10","series-title":"LNCS","doi-asserted-by":"publisher","first-page":"242","DOI":"10.1007\/978-3-031-72111-3_23","volume-title":"MICCAI 2024","author":"S Ye","year":"2024","unstructured":"Ye, S., Meng, M., Li, M., Feng, D., Kim, J.: Enabling text-free inference in language-guided segmentation of chest X-rays via self-guidance. In: Linguraru, M.G., et al. (eds.) MICCAI 2024. LNCS, vol. 15008, pp. 242\u2013252. Springer, Cham (2024). https:\/\/doi.org\/10.1007\/978-3-031-72111-3_23"},{"key":"27_CR11","doi-asserted-by":"crossref","unstructured":"Huang, X., Li, H., Cao, M., Chen, L., You, C., An, D.: Cross-modal conditioned reconstruction for language-guided medical image segmentation. IEEE Trans. Med. Imaging (2024)","DOI":"10.1109\/TMI.2024.3523333"},{"key":"27_CR12","series-title":"LNCS","doi-asserted-by":"publisher","first-page":"425","DOI":"10.1007\/978-3-031-72114-4_41","volume-title":"MICCAI 2024","author":"X Zhang","year":"2024","unstructured":"Zhang, X., Ni, B., Yang, Y., Zhang, L.: MAdapter: a better interaction between image and language for medical image segmentation. In: Linguraru, M.G., et al. (eds.) MICCAI 2024. LNCS, vol. 15009, pp. 425\u2013434. Springer, Cham (2024). https:\/\/doi.org\/10.1007\/978-3-031-72114-4_41"},{"key":"27_CR13","series-title":"LNCS","doi-asserted-by":"publisher","first-page":"192","DOI":"10.1007\/978-3-031-72114-4_19","volume-title":"MICCAI 2024","author":"Y Guo","year":"2024","unstructured":"Guo, Y., et al.: Common vision-language attention for text-guided medical image segmentation of pneumonia. In: Linguraru, M.G., et al. (eds.) MICCAI 2024. LNCS, vol. 15009, pp. 192\u2013201. Springer, Cham (2024). https:\/\/doi.org\/10.1007\/978-3-031-72114-4_19"},{"key":"27_CR14","doi-asserted-by":"crossref","unstructured":"Kirillov, A., et al.: Segment anything. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 4015\u20134026 (2023)","DOI":"10.1109\/ICCV51070.2023.00371"},{"key":"27_CR15","doi-asserted-by":"crossref","unstructured":"Zhou, Y., Huang, J., Wang, C., Song, L., Yang, G.: Xnet: wavelet-based low and high frequency fusion networks for fully-and semi-supervised semantic segmentation of biomedical images. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 21085\u201321096 (2023)","DOI":"10.1109\/ICCV51070.2023.01928"},{"key":"27_CR16","doi-asserted-by":"crossref","unstructured":"Degerli, A., Kiranyaz, S., Chowdhury, M.E., Gabbouj, M.: Osegnet: operational segmentation network for COVID-19 detection using chest x-ray images. In: 2022 IEEE International Conference on Image Processing (ICIP), pp. 2306\u20132310 (2022)","DOI":"10.1109\/ICIP46576.2022.9897412"},{"key":"27_CR17","unstructured":"Andreychenko, A., et al.: MosMedData: chest CT scans with COVID-19 related findings dataset. arXiv preprint arXiv:2005.06465 (2020)"},{"key":"27_CR18","doi-asserted-by":"crossref","unstructured":"Liu, Z., Mao, H., Wu, C.Y., Feichtenhofer, C., Darrell, T., Xie, S.: A convnet for the 2020s. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 11976\u201311986 (2022)","DOI":"10.1109\/CVPR52688.2022.01167"},{"key":"27_CR19","series-title":"LNCS","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/978-3-031-20059-5_1","volume-title":"ECCV 2022","author":"B Boecking","year":"2022","unstructured":"Boecking, B., Usuyama, N., Bannur, S., Castro, D.C., Schwaighofer, A., Hyland, S.: Making the most of text semantics to improve biomedical vision-language processing. In: Avidan, S., Brostow, G., Ciss\u00e9, M., Farinella, G.M., Hassner, T. (eds.) ECCV 2022. LNCS, vol. 13696, pp. 1\u201321. Springer, Cham (2022). https:\/\/doi.org\/10.1007\/978-3-031-20059-5_1"},{"key":"27_CR20","unstructured":"Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G.: PyTorch: an imperative style, high-performance deep learning library. Adv. Neural Inf. Process. Syst. 32 (2019)"},{"key":"27_CR21","unstructured":"Loshchilov, I.: Decoupled weight decay regularization. arXiv preprint arXiv:1711.05101 (2017)"},{"issue":"2","key":"27_CR22","doi-asserted-by":"publisher","first-page":"203","DOI":"10.1038\/s41592-020-01008-z","volume":"18","author":"F Isensee","year":"2021","unstructured":"Isensee, F., Jaeger, P.F., Kohl, S.A., Petersen, J., Maier-Hein, K.H.: nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation. Nat. Methods 18(2), 203\u2013211 (2021)","journal-title":"Nat. Methods"},{"key":"27_CR23","doi-asserted-by":"crossref","unstructured":"Wang, H., Cao, P., Wang, J., Zaiane, O.R.: UCTransNet: rethinking the skip connections in U-Net from a channel-wise perspective with transformer. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp. 2441\u20132449 (2022)","DOI":"10.1609\/aaai.v36i3.20144"}],"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\/978-3-032-05114-1_27","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,20]],"date-time":"2025-09-20T14:11:12Z","timestamp":1758377472000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-032-05114-1_27"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,9,21]]},"ISBN":["9783032051134","9783032051141"],"references-count":23,"URL":"https:\/\/doi.org\/10.1007\/978-3-032-05114-1_27","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,9,21]]},"assertion":[{"value":"21 September 2025","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"The authors have no competing interests to declare that are relevant to the content of this paper.","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"}}]}}