{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,4]],"date-time":"2026-03-04T17:06:48Z","timestamp":1772644008247,"version":"3.50.1"},"reference-count":41,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2025,12,26]],"date-time":"2025-12-26T00:00:00Z","timestamp":1766707200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,12,26]],"date-time":"2025-12-26T00:00:00Z","timestamp":1766707200000},"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":["Vis Comput"],"published-print":{"date-parts":[[2026,1]]},"DOI":"10.1007\/s00371-025-04248-2","type":"journal-article","created":{"date-parts":[[2025,12,26]],"date-time":"2025-12-26T19:38:56Z","timestamp":1766777936000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Enhancing medical image segmentation with adaptive convolution and dynamic high-frequency feature enhancement"],"prefix":"10.1007","volume":"42","author":[{"given":"Wenguang","family":"Xu","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qian","family":"Dong","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shiren","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kamoliddin","family":"Shukurov","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shu","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Maksim","family":"Davydov","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jawad","family":"Hussain","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Guangguang","family":"Yang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,12,26]]},"reference":[{"key":"4248_CR1","doi-asserted-by":"publisher","first-page":"7192","DOI":"10.1109\/TIP.2020.2999854","volume":"29","author":"A Nazir","year":"2020","unstructured":"Nazir, A., Cheema, M.N., Sheng, B., Li, H., Li, P., Yang, P., Jung, Y., Qin, J., Kim, J., Feng, D.D.: Off-enet: An optimally fused fully end-to-end network for automatic dense volumetric 3d intracranial blood vessels segmentation. IEEE Trans. Image Process. 29, 7192\u20137202 (2020)","journal-title":"IEEE Trans. Image Process."},{"key":"4248_CR2","doi-asserted-by":"crossref","unstructured":"Ali, S.G., Wang, X., Li, P., Li, H., Yang, P., Jung, Y., Qin, J., Kim, J., Sheng, B.: Egdnet: an efficient glomerular detection network for multiple anomalous pathological feature in glomerulonephritis. The Visual Computer, 1\u201318 (2024)","DOI":"10.1007\/s00371-024-03570-5"},{"issue":"8","key":"4248_CR3","doi-asserted-by":"publisher","first-page":"5299","DOI":"10.1007\/s00371-024-03722-7","volume":"41","author":"Y Zhao","year":"2025","unstructured":"Zhao, Y., Zhang, G., Li, K., Zhu, Z., Li, X., Zhang, Y., Fan, Z.: Mfadu-net: an enhanced doubleu-net with multi-level feature fusion and atrous decoder for medical image segmentation. Vis. Comput. 41(8), 5299\u20135309 (2025)","journal-title":"Vis. Comput."},{"key":"4248_CR4","doi-asserted-by":"publisher","first-page":"33687","DOI":"10.1109\/ACCESS.2024.3372394","volume":"12","author":"G Sun","year":"2024","unstructured":"Sun, G., Shu, H., Shao, F., Racharak, T., Kong, W., Pan, Y., Dong, J., Wang, S., Nguyen, L.-M., Xin, J.: Fkd-med: Privacy-aware, communication-optimized medical image segmentation via federated learning and model lightweighting through knowledge distillation. Ieee Access 12, 33687\u201333704 (2024)","journal-title":"Ieee Access"},{"key":"4248_CR5","doi-asserted-by":"crossref","unstructured":"Xiao, Y., Chen, C., Wang, L., Yu, J., Chen, B., Fu, X., Lu, M.: A novel semi-supervised domain adaptive method for cross-modality medical image segmentation. The Visual Computer, 1\u201316 (2025)","DOI":"10.1007\/s00371-025-03918-5"},{"key":"4248_CR6","doi-asserted-by":"publisher","first-page":"1504249","DOI":"10.3389\/fbioe.2024.1504249","volume":"12","author":"Y Pan","year":"2024","unstructured":"Pan, Y., Xin, J., Yang, T., Li, S., Nguyen, L.-M., Racharak, T., Li, K., Sun, G.: A mutual inclusion mechanism for precise boundary segmentation in medical images. Front. Bioeng. Biotechnol. 12, 1504249 (2024)","journal-title":"Front. Bioeng. Biotechnol."},{"key":"4248_CR7","doi-asserted-by":"crossref","unstructured":"Ibtehaz, N., Kihara, D.: Acc-unet: A completely convolutional unet model for the 2020s. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 692\u2013702 (2023). Springer","DOI":"10.1007\/978-3-031-43898-1_66"},{"key":"4248_CR8","doi-asserted-by":"crossref","unstructured":"Li, G., Li, S., Lin, Y., Tang, S., Xu, W., Chen, K., Yang, G.: Cfseg-net: context feature extraction network for medical image segmentation. The Visual Computer, 1\u201311 (2025)","DOI":"10.1007\/s00371-025-04029-x"},{"issue":"6","key":"4248_CR9","doi-asserted-by":"publisher","first-page":"1856","DOI":"10.1109\/TMI.2019.2959609","volume":"39","author":"Z Zhou","year":"2019","unstructured":"Zhou, Z., Siddiquee, M.M.R., Tajbakhsh, N., Liang, J.: Unet++: Redesigning skip connections to exploit multiscale features in image segmentation. IEEE Trans. Med. Imaging 39(6), 1856\u20131867 (2019)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"4248_CR10","unstructured":"Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, \u0141., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017)"},{"key":"4248_CR11","doi-asserted-by":"publisher","first-page":"1398237","DOI":"10.3389\/fbioe.2024.1398237","volume":"12","author":"G Sun","year":"2024","unstructured":"Sun, G., Pan, Y., Kong, W., Xu, Z., Ma, J., Racharak, T., Nguyen, L.-M., Xin, J.: Da-transunet: integrating spatial and channel dual attention with transformer u-net for medical image segmentation. Front. Bioeng. Biotechnol. 12, 1398237 (2024)","journal-title":"Front. Bioeng. Biotechnol."},{"key":"4248_CR12","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2024.106731","volume":"97","author":"B Wang","year":"2024","unstructured":"Wang, B., Qin, J., Lv, L., Cheng, M., Li, L., He, J., Li, D., Xia, D., Wang, M., Ren, H., et al.: Dsml-unet: Depthwise separable convolution network with multiscale large kernel for medical image segmentation. Biomed. Signal Process. Control 97, 106731 (2024)","journal-title":"Biomed. Signal Process. Control"},{"key":"4248_CR13","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2023.110868","volume":"278","author":"S Yang","year":"2023","unstructured":"Yang, S., Zhang, X., Chen, Y., Jiang, Y., Feng, Q., Pu, L., Sun, F.: Ucunet: A lightweight and precise medical image segmentation network based on efficient large kernel u-shaped convolutional module design. Knowl.-Based Syst. 278, 110868 (2023)","journal-title":"Knowl.-Based Syst."},{"key":"4248_CR14","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2025.107949","volume":"108","author":"H Xu","year":"2025","unstructured":"Xu, H., Lv, R.: Adcformer: A hybrid model of adaptive depth-wise convolution and transformer for retinal macular edema segmentation in oct images. Biomed. Signal Process. Control 108, 107949 (2025)","journal-title":"Biomed. Signal Process. Control"},{"key":"4248_CR15","doi-asserted-by":"publisher","first-page":"2192","DOI":"10.1109\/JSTARS.2023.3244207","volume":"16","author":"J Hou","year":"2023","unstructured":"Hou, J., Guo, Z., Feng, Y., Wu, Y., Diao, W.: Spanet: Spatial adaptive convolution based content-aware network for aerial image semantic segmentation. IEEE J. Selected Topics Appl. Earth Observ. Remote Sens. 16, 2192\u20132204 (2023)","journal-title":"IEEE J. Selected Topics Appl. Earth Observ. Remote Sens."},{"key":"4248_CR16","unstructured":"Zizheng\u00a0Pan, J.C., Zhuang, B.: Fast vision transformers with hilo attention. In: Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, NeurIPS 2022, New Orleans, LA, USA, November 28 - December 9, 2022 (2022)"},{"key":"4248_CR17","doi-asserted-by":"crossref","unstructured":"Kong, L., Dong, J., Ge, J., Li, M., Pan, J.: Efficient frequency domain-based transformers for high-quality image deblurring. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 5886\u20135895 (2023)","DOI":"10.1109\/CVPR52729.2023.00570"},{"key":"4248_CR18","doi-asserted-by":"crossref","unstructured":"Xu, M., Yu, C., Li, Z., Tang, H., Hu, Y., Nie, L.: Hdnet: A hybrid domain network with multi-scale high-frequency information enhancement for infrared small target detection. IEEE Transactions on Geoscience and Remote Sensing (2025)","DOI":"10.1109\/TGRS.2025.3574962"},{"key":"4248_CR19","doi-asserted-by":"crossref","unstructured":"Wang, A., Chen, H., Lin, Z., Han, J., Ding, G.: Repvit: Revisiting mobile cnn from vit perspective. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 15909\u201315920 (2024)","DOI":"10.1109\/CVPR52733.2024.01506"},{"key":"4248_CR20","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s40537-021-00444-8","volume":"8","author":"L Alzubaidi","year":"2021","unstructured":"Alzubaidi, L., Zhang, J., Humaidi, A.J., Al-Dujaili, A., Duan, Y., Al-Shamma, O., Santamar\u00eda, J., Fadhel, M.A., Al-Amidie, M., Farhan, L.: Review of deep learning: concepts, cnn architectures, challenges, applications, future directions. J. Big Data 8, 1\u201374 (2021)","journal-title":"J. Big Data"},{"key":"4248_CR21","unstructured":"Joze, H.R.V., Shaban, A., Iuzzolino, M.L., Koishida, K.: Mmtm: Multimodal transfer module for cnn fusion. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 13289\u201313299 (2020)"},{"key":"4248_CR22","doi-asserted-by":"crossref","unstructured":"Chen, Y., Dai, X., Liu, M., Chen, D., Yuan, L., Liu, Z.: Dynamic convolution: Attention over convolution kernels. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 11030\u201311039 (2020)","DOI":"10.1109\/CVPR42600.2020.01104"},{"key":"4248_CR23","doi-asserted-by":"crossref","unstructured":"Qi, Y., He, Y., Qi, X., Zhang, Y., Yang, G.: Dynamic snake convolution based on topological geometric constraints for tubular structure segmentation. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 6070\u20136079 (2023)","DOI":"10.1109\/ICCV51070.2023.00558"},{"key":"4248_CR24","doi-asserted-by":"crossref","unstructured":"Qin, Z., Zhang, P., Wu, F., Li, X.: Fcanet: Frequency channel attention networks. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 783\u2013792 (2021)","DOI":"10.1109\/ICCV48922.2021.00082"},{"key":"4248_CR25","doi-asserted-by":"crossref","unstructured":"Kong, L., Dong, J., Ge, J., Li, M., Pan, J.: Efficient frequency domain-based transformers for high-quality image deblurring. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 5886\u20135895 (2023)","DOI":"10.1109\/CVPR52729.2023.00570"},{"key":"4248_CR26","doi-asserted-by":"crossref","unstructured":"Hu, K., Zhang, Q., Yuan, M., Zhang, Y.: Sfdfusion: An efficient spatial-frequency domain fusion network for infrared and visible image fusion. In: ECAI 2024, pp. 482\u2013489 (2024)","DOI":"10.3233\/FAIA240524"},{"key":"4248_CR27","unstructured":"Codella, N., Rotemberg, V., Tschandl, P., Celebi, M.E., Dusza, S., Gutman, D., Helba, B., Kalloo, A., Liopyris, K., Marchetti, M., et al.: Skin lesion analysis toward melanoma detection 2018: A challenge hosted by the international skin imaging collaboration (isic). arXiv preprint arXiv:1902.03368 (2019)"},{"issue":"1","key":"4248_CR28","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/sdata.2018.161","volume":"5","author":"P Tschandl","year":"2018","unstructured":"Tschandl, P., Rosendahl, C., Kittler, H.: The ham10000 dataset, a large collection of multi-source dermatoscopic images of common pigmented skin lesions. Scientific data 5(1), 1\u20139 (2018)","journal-title":"Scientific data"},{"key":"4248_CR29","doi-asserted-by":"publisher","first-page":"489","DOI":"10.1016\/j.media.2016.08.008","volume":"35","author":"K Sirinukunwattana","year":"2017","unstructured":"Sirinukunwattana, K., Pluim, J.P., Chen, H., Qi, X., Heng, P.-A., Guo, Y.B., Wang, L.Y., Matuszewski, B.J., Bruni, E., Sanchez, U., et al.: Gland segmentation in colon histology images: The glas challenge contest. Med. Image Anal. 35, 489\u2013502 (2017)","journal-title":"Med. Image Anal."},{"key":"4248_CR30","doi-asserted-by":"publisher","DOI":"10.1016\/j.dib.2019.104863","volume":"28","author":"W Al-Dhabyani","year":"2020","unstructured":"Al-Dhabyani, W., Gomaa, M., Khaled, H., Fahmy, A.: Dataset of breast ultrasound images. Data Brief 28, 104863 (2020)","journal-title":"Data Brief"},{"key":"4248_CR31","doi-asserted-by":"crossref","unstructured":"Jha, D., Smedsrud, P.H., Riegler, M.A., Halvorsen, P., De\u00a0Lange, T., Johansen, D., Johansen, H.D.: Kvasir-seg: A segmented polyp dataset. In: MultiMedia Modeling: 26th International Conference, MMM 2020, Daejeon, South Korea, January 5\u20138, 2020, Proceedings, Part II 26, pp. 451\u2013462 (2020). Springer","DOI":"10.1007\/978-3-030-37734-2_37"},{"key":"4248_CR32","doi-asserted-by":"publisher","first-page":"99","DOI":"10.1016\/j.compmedimag.2015.02.007","volume":"43","author":"J Bernal","year":"2015","unstructured":"Bernal, J., S\u00e1nchez, F.J., Fern\u00e1ndez-Esparrach, G., Gil, D., Rodr\u00edguez, C., Vilari\u00f1o, F.: Wm-dova maps for accurate polyp highlighting in colonoscopy: Validation vs. saliency maps from physicians. Comput. Med. Imaging Graph. 43, 99\u2013111 (2015)","journal-title":"Comput. Med. Imaging Graph."},{"key":"4248_CR33","unstructured":"Wang, L.L., Lo, K., Chandrasekhar, Y., Reas, R., Yang, J., Burdick, D., Eide, D., Funk, K., Katsis, Y., Kinney, R., et al.: Cord-19: The covid-19 open research dataset. ArXiv, 2004 (2020)"},{"key":"4248_CR34","doi-asserted-by":"crossref","unstructured":"Ronneberger, O., Fischer, P., Brox, T.: U-net: Convolutional networks for biomedical image segmentation. In: Medical Image Computing and Computer-assisted intervention\u2013MICCAI 2015: 18th International Conference, Munich, Germany, October 5-9, 2015, Proceedings, Part III 18, pp. 234\u2013241 (2015). Springer","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"4248_CR35","doi-asserted-by":"publisher","first-page":"74","DOI":"10.1016\/j.neunet.2019.08.025","volume":"121","author":"N Ibtehaz","year":"2020","unstructured":"Ibtehaz, N., Rahman, M.S.: Multiresunet: Rethinking the u-net architecture for multimodal biomedical image segmentation. Neural Netw. 121, 74\u201387 (2020)","journal-title":"Neural Netw."},{"key":"4248_CR36","doi-asserted-by":"crossref","unstructured":"Cao, H., Wang, Y., Chen, J., Jiang, D., Zhang, X., Tian, Q., Wang, M.: Swin-unet: Unet-like pure transformer for medical image segmentation. In: European Conference on Computer Vision, pp. 205\u2013218 (2022). Springer","DOI":"10.1007\/978-3-031-25066-8_9"},{"key":"4248_CR37","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, vol. 36, pp. 2441\u20132449 (2022)","DOI":"10.1609\/aaai.v36i3.20144"},{"key":"4248_CR38","doi-asserted-by":"crossref","unstructured":"Wang, Z., Min, X., Shi, F., Jin, R., Nawrin, S.S., Yu, I., Nagatomi, R.: Smeswin unet: Merging cnn and transformer for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 517\u2013526 (2022). Springer","DOI":"10.1007\/978-3-031-16443-9_50"},{"key":"4248_CR39","doi-asserted-by":"publisher","first-page":"4842","DOI":"10.1109\/TIP.2023.3304518","volume":"32","author":"W Qi","year":"2023","unstructured":"Qi, W., Wu, H.-C., Chan, S.-C.: Mdf-net: A multi-scale dynamic fusion network for breast tumor segmentation of ultrasound images. IEEE Trans. Image Process. 32, 4842\u20134855 (2023)","journal-title":"IEEE Trans. Image Process."},{"key":"4248_CR40","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2023.107301","volume":"164","author":"L Yang","year":"2023","unstructured":"Yang, L., Zhai, C., Liu, Y., Yu, H.: Cfha-net: A polyp segmentation method with cross-scale fusion strategy and hybrid attention. Comput. Biol. Med. 164, 107301 (2023)","journal-title":"Comput. Biol. Med."},{"key":"4248_CR41","doi-asserted-by":"crossref","unstructured":"Fiaz, M., Noman, M., Cholakkal, H., Anwer, R.M., Hanna, J., Khan, F.S.: Guided-attention and gated-aggregation network for medical image segmentation. Pattern Recogn. 156, 110812 (2024)","DOI":"10.1016\/j.patcog.2024.110812"}],"container-title":["The Visual Computer"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00371-025-04248-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00371-025-04248-2","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00371-025-04248-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,4]],"date-time":"2026-03-04T13:02:58Z","timestamp":1772629378000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00371-025-04248-2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,12,26]]},"references-count":41,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2026,1]]}},"alternative-id":["4248"],"URL":"https:\/\/doi.org\/10.1007\/s00371-025-04248-2","relation":{},"ISSN":["0178-2789","1432-2315"],"issn-type":[{"value":"0178-2789","type":"print"},{"value":"1432-2315","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,12,26]]},"assertion":[{"value":"8 July 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"16 October 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"26 December 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}],"article-number":"98"}}