{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,2]],"date-time":"2026-08-02T05:47:09Z","timestamp":1785649629049,"version":"3.56.0"},"publisher-location":"Cham","reference-count":35,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783032316653","type":"print"},{"value":"9783032316660","type":"electronic"}],"license":[{"start":{"date-parts":[[2026,8,3]],"date-time":"2026-08-03T00:00:00Z","timestamp":1785715200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,8,3]],"date-time":"2026-08-03T00:00:00Z","timestamp":1785715200000},"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":[[2027]]},"DOI":"10.1007\/978-3-032-31666-0_13","type":"book-chapter","created":{"date-parts":[[2026,8,2]],"date-time":"2026-08-02T05:45:17Z","timestamp":1785649517000},"page":"191-203","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["MFANet: A Lightweight Network Combining CNN and\u00a0Mamba for\u00a0Medical Image Segmentation"],"prefix":"10.1007","author":[{"given":"Haozhuo","family":"Zhang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bob","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Pinxian","family":"Zeng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,8,3]]},"reference":[{"issue":"1","key":"13_CR1","doi-asserted-by":"publisher","first-page":"677","DOI":"10.1038\/s41597-023-02540-1","volume":"10","author":"L Antonelli","year":"2023","unstructured":"Antonelli, L., Polverino, F., Albu, A., et al.: ALFI: cell cycle phenotype annotations of label-free time-lapse imaging data from cultured human cells. Sci. Data 10(1), 677 (2023)","journal-title":"Sci. Data"},{"key":"13_CR2","doi-asserted-by":"crossref","unstructured":"Canny, J.: A computational approach to edge detection. IEEE Trans. Patt. Anal. Mach. Intell. PAMI 8(6), 679\u2013698 (1986)","DOI":"10.1109\/TPAMI.1986.4767851"},{"key":"13_CR3","unstructured":"Chen, J., et al.: Transformers make strong encoders for medical image segmentation, Transunet (2021)"},{"key":"13_CR4","doi-asserted-by":"crossref","unstructured":"Chen, L.-C., Papandreou, G., Kokkinos, I., Murphy, K., Yuille, A.L.: Deeplab: semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected CRFS (2017)","DOI":"10.1109\/TPAMI.2017.2699184"},{"key":"13_CR5","unstructured":"Chen, L.-C., Papandreou, G., Schroff, F., Adam, H.: Rethinking atrous convolution for semantic image segmentation (2017)"},{"key":"13_CR6","unstructured":"Dosovitskiy, A., et al.: An image is worth 16x16 words: transformers for image recognition at scale (2021)"},{"key":"13_CR7","unstructured":"Gu, A., Dao, T.: Mamba: Linear-time sequence modeling with selective state spaces (2024)"},{"issue":"1","key":"13_CR8","doi-asserted-by":"publisher","first-page":"87","DOI":"10.1109\/TPAMI.2022.3152247","volume":"45","author":"K Han","year":"2023","unstructured":"Han, K., et al.: A survey on vision transformer. IEEE Trans. Pattern Anal. Mach. Intell. 45(1), 87\u2013110 (2023)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"issue":"1","key":"13_CR9","doi-asserted-by":"publisher","first-page":"100","DOI":"10.1016\/S0734-189X(85)90153-7","volume":"29","author":"RM Haralick","year":"1985","unstructured":"Haralick, R.M., Shapiro, L.G.: Image segmentation techniques. Comput. Vision Graph. Image Process. 29(1), 100\u2013132 (1985)","journal-title":"Comput. Vision Graph. Image Process."},{"key":"13_CR10","doi-asserted-by":"crossref","unstructured":"Hatamizadeh, A., Kautz, J.: Mambavision: a hybrid mamba-transformer vision backbone (2025)","DOI":"10.1109\/CVPR52734.2025.02352"},{"key":"13_CR11","doi-asserted-by":"crossref","unstructured":"Hatamizadeh, A., Nath, V., Tang, Y., Yang, D., Roth, H., Xu, D.: Swin transformers for semantic segmentation of brain tumors in MRI images (2022)","DOI":"10.1007\/978-3-031-08999-2_22"},{"key":"13_CR12","doi-asserted-by":"crossref","unstructured":"Hatamizadeh, A., et al.: UNETR: transformers for 3d medical image segmentation (2021)","DOI":"10.1109\/WACV51458.2022.00181"},{"key":"13_CR13","doi-asserted-by":"crossref","unstructured":"He, J., Ma, Y., Yang, M., Yang, W., Wu, C., Chen, S.: Tac-unet: transformer-assisted convolutional neural network for medical image segmentation. Quant. Imaging Med. Surgery, 14(12) 2024","DOI":"10.21037\/qims-24-1229"},{"key":"13_CR14","doi-asserted-by":"crossref","unstructured":"He, K., Gkioxari, G., Doll\u00e1r, P., Girshick, R.: Mask R-CNN (2018)","DOI":"10.1109\/ICCV.2017.322"},{"key":"13_CR15","volume-title":"Thirteenth International Conference on Digital Image Processing (ICDIP 2021), volume 11878, page 118781I","author":"Y He","year":"2021","unstructured":"He, Y., Zhu, Y., Li, H.: Cross-layer channel attention mechanism for convolutional neural networks. In: Jiang, X., Fujita, H. (eds.) Thirteenth International Conference on Digital Image Processing (ICDIP 2021), volume 11878, page 118781I. International Society for Optics and Photonics, SPIE (2021)"},{"key":"13_CR16","doi-asserted-by":"crossref","unstructured":"Huang, H., et al.: UNET 3+: a full-scale connected UNET for medical image segmentation. In: ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1055\u20131059 (2020)","DOI":"10.1109\/ICASSP40776.2020.9053405"},{"key":"13_CR17","doi-asserted-by":"crossref","unstructured":"Isensee, F., et al.: NNU-net: self-adapting framework for u-net-based medical image segmentation (2018)","DOI":"10.1007\/978-3-658-25326-4_7"},{"key":"13_CR18","doi-asserted-by":"crossref","unstructured":"Jha, D., et al.: KVASIR-seg: a segmented polyp dataset (2019)","DOI":"10.1007\/978-3-030-37734-2_37"},{"key":"13_CR19","unstructured":"LeCun, Y., Bengio, Y.: Convolutional networks for images, speech, and time series (1998)"},{"key":"13_CR20","unstructured":"Li, C., et al.: U-KAN makes strong backbone for medical image segmentation and generation (2024)"},{"key":"13_CR21","doi-asserted-by":"crossref","unstructured":"Litjens, G., et al.: A survey on deep learning in medical image analysis. Med. Image Anal. 42, 60\u201388 (2017)","DOI":"10.1016\/j.media.2017.07.005"},{"key":"13_CR22","doi-asserted-by":"crossref","unstructured":"Liu, Y., et al.: Visual state space model, Vmamba (2024)","DOI":"10.52202\/079017-3273"},{"key":"13_CR23","doi-asserted-by":"crossref","unstructured":"Liu, Z., et al.: Hierarchical vision transformer using shifted windows, Swin transformer (2021)","DOI":"10.1109\/ICCV48922.2021.00986"},{"key":"13_CR24","doi-asserted-by":"crossref","unstructured":"Long, J., Shelhamer, E., Darrell, T.: Fully convolutional networks for semantic segmentation (2015)","DOI":"10.1109\/CVPR.2015.7298965"},{"key":"13_CR25","unstructured":"Ma, J., Li, F., Wang, B.: Enhancing long-range dependency for biomedical image segmentation, U-mamba (2024)"},{"key":"13_CR26","doi-asserted-by":"crossref","unstructured":"Milletari, F., Navab, N., Ahmadi, S.: Fully convolutional neural networks for volumetric medical image segmentation, V-net (2016)","DOI":"10.1109\/3DV.2016.79"},{"issue":"7","key":"13_CR27","first-page":"3523","volume":"44","author":"S Minaee","year":"2022","unstructured":"Minaee, S., Boykov, Y., Porikli, F., Plaza, A., Kehtarnavaz, N., Terzopoulos, D.: Image segmentation using deep learning: A survey. IEEE Trans. Pattern Anal. Mach. Intell. 44(7), 3523\u20133542 (2022)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"13_CR28","doi-asserted-by":"crossref","unstructured":"Myronenko, A.: 3d MRI brain tumor segmentation using autoencoder regularization (2018)","DOI":"10.1007\/978-3-030-11726-9_28"},{"issue":"1","key":"13_CR29","doi-asserted-by":"publisher","first-page":"62","DOI":"10.1109\/TSMC.1979.4310076","volume":"9","author":"N Otsu","year":"1979","unstructured":"Otsu, N.: A threshold selection method from gray-level histograms. IEEE Trans. Syst. Man Cybern. 9(1), 62\u201366 (1979)","journal-title":"IEEE Trans. Syst. Man Cybern."},{"key":"13_CR30","doi-asserted-by":"crossref","unstructured":"Ronneberger, O., Fischer, P., Brox, T.: Convolutional networks for biomedical image segmentation, U-net (2015)","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"13_CR31","unstructured":"Vaswani, A., et al.: Attention is all you need. In: Guyon, I., et al. (eds) Advances in Neural Information Processing Systems, volume\u00a030. Curran Associates, Inc., (2017)"},{"key":"13_CR32","doi-asserted-by":"crossref","unstructured":"Xing, Z., Ye, T., Yang, Y., Liu, G., Zhu, L.: Segmamba: long-range sequential modeling mamba for 3d medical image segmentation (2024)","DOI":"10.1007\/978-3-031-72111-3_54"},{"key":"13_CR33","doi-asserted-by":"crossref","unstructured":"Zhang, C., Wang, L., Wei, G., Kong, Z., Qiu, M.: A dual-branch and dual attention transformer and CNN hybrid network for ultrasound image segmentation. Front. Phys. 15 (2024)","DOI":"10.3389\/fphys.2024.1432987"},{"key":"13_CR34","doi-asserted-by":"crossref","unstructured":"Zhou, Z., Siddiquee, M.M.R., Tajbakhsh, N., Liang, J.: Unet++: a nested u-net architecture for medical image segmentation (2018)","DOI":"10.1007\/978-3-030-00889-5_1"},{"key":"13_CR35","unstructured":"Zhu, L., Liao, B., Zhang, Q., Wang, X., Liu, W., Wang, X.: Efficient visual representation learning with bidirectional state space model, Vision mamba (2024)"}],"container-title":["Lecture Notes in Computer Science","Pattern Recognition"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-032-31666-0_13","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,8,2]],"date-time":"2026-08-02T05:45:20Z","timestamp":1785649520000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-032-31666-0_13"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,8,3]]},"ISBN":["9783032316653","9783032316660"],"references-count":35,"URL":"https:\/\/doi.org\/10.1007\/978-3-032-31666-0_13","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,8,3]]},"assertion":[{"value":"3 August 2026","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICPR","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Pattern Recognition","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Lyon","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"France","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2026","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"17 August 2026","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"22 August 2026","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":"icpr2026","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/icpr2026.org\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}