{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,9]],"date-time":"2026-07-09T04:17:42Z","timestamp":1783570662987,"version":"3.55.0"},"reference-count":39,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2026,7,3]],"date-time":"2026-07-03T00:00:00Z","timestamp":1783036800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["J. Imaging"],"abstract":"<jats:p>Accurate medical image segmentation remains challenging when lesions have large-scale variation, weak boundaries, and strong background interference. Vision Mamba provides efficient long-range modeling, but current Mamba-based U-shaped networks are still limited by weak local multi-scale representation and coarse skip fusion. This study proposes MSCF-Net, a Vision Mamba segmentation network for dermoscopic and endoscopic images. The network is built on VM-UNet and introduces two modules. The Multi-Scale Context Bridging (MSCB) module enriches bottleneck features with local, dilated, and global context. The Cross-Layer Adaptive Fusion (CLAF) module recalibrates encoder\u2013decoder features in channel and spatial dimensions, reducing noisy shallow feature transmission. A structure loss is used to improve region completeness and boundary quality. Experiments on ISIC 2017, ISIC 2018, and CVC-ClinicDB show Dice scores of 90.62%, 90.82%, and 91.72%, and mIoU values of 82.02%, 82.31%, and 84.56%, respectively. Compared with representative baselines evaluated in our experiments, MSCF-Net achieves competitive segmentation performance under the adopted benchmark protocol. Ablation, qualitative, and spatial response analyses further indicate that MSCB improves scale-aware representation, while CLAF helps the decoder focus on lesion-related cues. The results suggest that MSCF-Net provides a favorable accuracy\u2013efficiency trade-off for medical image segmentation.<\/jats:p>","DOI":"10.3390\/jimaging12070299","type":"journal-article","created":{"date-parts":[[2026,7,3]],"date-time":"2026-07-03T14:33:06Z","timestamp":1783089186000},"page":"299","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["MSCF-Net: A Vision Mamba Network with Multi-Scale Context Bridging and Cross-Layer Adaptive Fusion for Medical Image Segmentation"],"prefix":"10.3390","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0009-0002-9165-5354","authenticated-orcid":false,"given":"Jiahao","family":"Guo","sequence":"first","affiliation":[{"name":"School of Mathematics and Computer Science, Shaanxi University of Technology, Hanzhong 723001, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-1867-6064","authenticated-orcid":false,"given":"Tao","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Mathematics and Computer Science, Shaanxi University of Technology, Hanzhong 723001, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-9217-1417","authenticated-orcid":false,"given":"Jiaxi","family":"Hu","sequence":"additional","affiliation":[{"name":"School of Mathematics and Computer Science, Shaanxi University of Technology, Hanzhong 723001, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-4734-9644","authenticated-orcid":false,"given":"Yuanhong","family":"Zhou","sequence":"additional","affiliation":[{"name":"School of Mathematics and Computer Science, Shaanxi University of Technology, Hanzhong 723001, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,7,3]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Ronneberger, O., Fischer, P., and Brox, T. (2015). U-Net: Convolutional networks for biomedical image segmentation. Proceedings of the International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer.","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"ref_2","unstructured":"Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., and Polosukhin, I. (2017). Attention is all you need. Advances in Neural Information Processing Systems, Neural Information Processing Systems Foundation."},{"key":"ref_3","unstructured":"Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., and Gelly, S. (2021, January 3\u20137). An image is worth 16 \u00d7 16 words: Transformers for image recognition at scale. Proceedings of the International Conference on Learning Representations, Virtual Event."},{"key":"ref_4","unstructured":"Gu, A., and Dao, T. (2024, January 7\u20139). Mamba: Linear-time sequence modeling with selective state spaces. Proceedings of the First Conference on Language Modeling, Philadelphia, PA, USA."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Ruan, J., and Xiang, S. (2025). VM-UNet: Vision Mamba UNet for medical image segmentation. ACM Trans. Multimed. Comput. Commun. Appl., accepted.","DOI":"10.1145\/3767748"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Long, J., Shelhamer, E., and Darrell, T. (2015, January 7\u201312). Fully convolutional networks for semantic segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Boston, MA, USA.","DOI":"10.1109\/CVPR.2015.7298965"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Zhou, Z., Siddiquee, M.M.R., Tajbakhsh, N., and Liang, J. (2018). UNet++: A nested U-Net architecture for medical image segmentation. Proceedings of the International Workshop on Deep Learning in Medical Image Analysis, Springer.","DOI":"10.1007\/978-3-030-00889-5_1"},{"key":"ref_8","unstructured":"Oktay, O., Schlemper, J., Folgoc, L.L., Lee, M., Heinrich, M., Misawa, K., Mori, K., McDonagh, S., Hammerla, N.Y., and Kainz, B. (2018). Attention U-Net: Learning where to look for the pancreas. arXiv."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Huang, H., Lin, L., Tong, R., Hu, H., Zhang, Q., Iwamoto, Y., Han, X.-H., Chen, Y.-W., and Wu, J. (2020). UNet 3+: A full-scale connected U-Net for medical image segmentation. Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing, IEEE.","DOI":"10.1109\/ICASSP40776.2020.9053405"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Chen, L.-C., Zhu, Y., Papandreou, G., Schroff, F., and Adam, H. (2018, January 8\u201314). Encoder-decoder with atrous separable convolution for semantic image segmentation. Proceedings of the European Conference on Computer Vision, Munich, Germany.","DOI":"10.1007\/978-3-030-01234-2_49"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"2281","DOI":"10.1109\/TMI.2019.2903562","article-title":"CE-Net: Context encoder network for 2D medical image segmentation","volume":"38","author":"Gu","year":"2019","journal-title":"IEEE Trans. Med. Imaging"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Jha, D., Smedsrud, P.H., Riegler, M.A., Johansen, D., de Lange, T., Halvorsen, P., and Johansen, H.D. (2019). ResUNet++: An advanced architecture for medical image segmentation. Proceedings of the IEEE International Symposium on Multimedia, IEEE.","DOI":"10.1109\/ISM46123.2019.00049"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"203","DOI":"10.1038\/s41592-020-01008-z","article-title":"nnU-Net: A self-configuring method for deep learning-based biomedical image segmentation","volume":"18","author":"Isensee","year":"2021","journal-title":"Nat. Methods"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Fan, D.-P., Ji, G.-P., Zhou, T., Chen, G., Fu, H., Shen, J., and Shao, L. (2020). PraNet: Parallel reverse attention network for polyp segmentation. Proceedings of the International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer.","DOI":"10.1007\/978-3-030-59725-2_26"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"103280","DOI":"10.1016\/j.media.2024.103280","article-title":"TransUNet: Rethinking the U-Net architecture design for medical image segmentation through the lens of transformers","volume":"97","author":"Chen","year":"2024","journal-title":"Med. Image Anal."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Liu, Z., Lin, Y., Cao, Y., Hu, H., Wei, Y., Zhang, Z., Lin, S., and Guo, B. (2021, January 11\u201317). Swin Transformer: Hierarchical vision transformer using shifted windows. Proceedings of the IEEE\/CVF International Conference on Computer Vision, Virtual Event.","DOI":"10.1109\/ICCV48922.2021.00986"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Liu, H., and Hu, Q. (2021). TransFuse: Fusing transformers and CNNs for medical image segmentation. Proceedings of the International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer.","DOI":"10.1007\/978-3-030-87193-2_2"},{"key":"ref_18","unstructured":"Wang, H., Cao, P., Wang, J., Zaiane, O., Salama, M., and Li, C. (March, January 22). UCTransNet: Rethinking the skip connections in U-Net from a channel-wise perspective with transformer. Proceedings of the AAAI Conference on Artificial Intelligence, Virtual Event."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Hatamizadeh, A., Tang, Y., Nath, V., Yang, D., Myronenko, A., Landman, B., Roth, H.R., and Xu, D. (2022, January 4\u20138). UNETR: Transformers for 3D medical image segmentation. Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision, Waikoloa, HI, USA.","DOI":"10.1109\/WACV51458.2022.00181"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Hatamizadeh, A., Nath, V., Tang, Y., Yang, D., Roth, H.R., and Xu, D. (2021). Swin UNETR: Swin transformers for semantic segmentation of brain tumors in MRI images. Proceedings of the International MICCAI Brainlesion Workshop, Springer.","DOI":"10.1007\/978-3-031-08999-2_22"},{"key":"ref_21","unstructured":"Gu, A., Goel, K., and Re, C. (2022, January 25\u201329). Efficiently modeling long sequences with structured state spaces. Proceedings of the International Conference on Learning Representations, Virtual Event."},{"key":"ref_22","unstructured":"Ma, J., Li, F., and Wang, B. (2024). U-Mamba: Enhancing long-range dependency for biomedical image segmentation. arXiv."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Xing, Z., Ye, T., Yang, Y., Liu, G., and Zhu, L. (2024). SegMamba: Long-range sequential modeling Mamba for 3D medical image segmentation. Proceedings of the International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer.","DOI":"10.1007\/978-3-031-72111-3_54"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Wang, Z., Zheng, J.-Q., Zhang, Y., Cui, G., and Li, L. (2024). Mamba-UNet: UNet-like pure Visual Mamba for medical image segmentation. arXiv.","DOI":"10.2139\/ssrn.5097998"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Zhang, M., Yu, Y., Gu, L., Lin, T., and Tao, X. (2024). VM-UNetV2: Rethinking Vision Mamba UNet for medical image segmentation. arXiv.","DOI":"10.1007\/978-981-97-5128-0_27"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Wang, J., Chen, J., Chen, D.Z., and Wu, J. (2024). LKM-UNet: Large kernel Vision Mamba U-Net for medical image segmentation. Proceedings of the International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer.","DOI":"10.1007\/978-3-031-72111-3_34"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Liu, J., Yang, H., Zhou, H.Y., Xi, Y., Yu, L., Li, C., Liang, Y., Shi, G., Yu, Y., and Zhang, S. (2024). Swin-UMamba: Mamba-based U-Net with ImageNet-based pretraining. Proceedings of the International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer.","DOI":"10.1007\/978-3-031-72114-4_59"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"129447","DOI":"10.1016\/j.neucom.2025.129447","article-title":"H-vmunet: High-order Vision Mamba U-Net for medical image segmentation","volume":"624","author":"Wu","year":"2025","journal-title":"Neurocomputing"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Hu, J., Shen, L., and Sun, G. (2018, January 18\u201322). Squeeze-and-excitation networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00745"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Woo, S., Park, J., Lee, J.-Y., and Kweon, I.S. (2018, January 8\u201314). CBAM: Convolutional block attention module. Proceedings of the European Conference on Computer Vision, Munich, Germany.","DOI":"10.1007\/978-3-030-01234-2_1"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Codella, N., Gutman, D., Celebi, M.E., Helba, B., Marchetti, M., Dusza, S., Kalloo, A., Liopyris, K., Mishra, N., and Kittler, H. (2018). Skin lesion analysis toward melanoma detection 2017: A challenge hosted by the International Skin Imaging Collaboration (ISIC). Proceedings of the IEEE International Symposium on Biomedical Imaging, IEEE.","DOI":"10.1109\/ISBI.2018.8363547"},{"key":"ref_32","unstructured":"Codella, N.C.F., Rotemberg, V., Tschandl, P., Celebi, M.E., Dusza, S., Gutman, D., Helba, B., Kalloo, A., Liopyris, K., and Marchetti, M. (2019). Skin lesion analysis toward melanoma detection 2018: A challenge hosted by the International Skin Imaging Collaboration (ISIC). arXiv."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"99","DOI":"10.1016\/j.compmedimag.2015.02.007","article-title":"WM-DOVA maps for accurate polyp highlighting in colonoscopy: Validation versus saliency maps from physicians","volume":"43","author":"Bernal","year":"2015","journal-title":"Comput. Med. Imaging Graph."},{"key":"ref_34","unstructured":"Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., and Antiga, L. (2019). PyTorch: An imperative style, high-performance deep learning library. Advances in Neural Information Processing Systems, Neural Information Processing Systems Foundation."},{"key":"ref_35","unstructured":"Loshchilov, I., and Hutter, F. (2019, January 6\u20139). Decoupled weight decay regularization. Proceedings of the International Conference on Learning Representations, New Orleans, LA, USA."},{"key":"ref_36","unstructured":"Loshchilov, I., and Hutter, F. (2017, January 24\u201326). SGDR: Stochastic gradient descent with warm restarts. Proceedings of the International Conference on Learning Representations, Toulon, France."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"179656","DOI":"10.1109\/ACCESS.2020.3025372","article-title":"MA-Net: A multi-scale attention network for liver and tumor segmentation","volume":"8","author":"Fan","year":"2020","journal-title":"IEEE Access"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Ruan, J., Xie, M., Gao, J., Lu, Y., Jin, X., and Huang, J. (2023). EGE-UNet: An efficient group enhanced U-Net for skin lesion segmentation. Proceedings of the International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer.","DOI":"10.1007\/978-3-031-43901-8_46"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., and Batra, D. (2017, January 22\u201329). Grad-CAM: Visual explanations from deep networks via gradient-based localization. Proceedings of the IEEE International Conference on Computer Vision, Venice, Italy.","DOI":"10.1109\/ICCV.2017.74"}],"container-title":["Journal of Imaging"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2313-433X\/12\/7\/299\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,7,9]],"date-time":"2026-07-09T04:10:59Z","timestamp":1783570259000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2313-433X\/12\/7\/299"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,7,3]]},"references-count":39,"journal-issue":{"issue":"7","published-online":{"date-parts":[[2026,7]]}},"alternative-id":["jimaging12070299"],"URL":"https:\/\/doi.org\/10.3390\/jimaging12070299","relation":{},"ISSN":["2313-433X"],"issn-type":[{"value":"2313-433X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,7,3]]}}}