{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,13]],"date-time":"2026-05-13T04:38:09Z","timestamp":1778647089686,"version":"3.51.4"},"reference-count":36,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2023,3,13]],"date-time":"2023-03-13T00:00:00Z","timestamp":1678665600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Science Foundation of China","doi-asserted-by":"publisher","award":["U1903213"],"award-info":[{"award-number":["U1903213"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Science Foundation of China","doi-asserted-by":"publisher","award":["2022ZD0115802"],"award-info":[{"award-number":["2022ZD0115802"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Scientific and technological innovation 2030 major project","award":["U1903213"],"award-info":[{"award-number":["U1903213"]}]},{"name":"Scientific and technological innovation 2030 major project","award":["2022ZD0115802"],"award-info":[{"award-number":["2022ZD0115802"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>At present, convolutional neural networks (CNNs) have been widely applied to the task of skin disease image segmentation due to the fact of their powerful information discrimination abilities and have achieved good results. However, it is difficult for CNNs to capture the connection between long-range contexts when extracting deep semantic features of lesion images, and the resulting semantic gap leads to the problem of segmentation blur in skin lesion image segmentation. In order to solve the above problems, we designed a hybrid encoder network based on transformer and fully connected neural network (MLP) architecture, and we call this approach HMT-Net. In the HMT-Net network, we use the attention mechanism of the CTrans module to learn the global relevance of the feature map to improve the network\u2019s ability to understand the overall foreground information of the lesion. On the other hand, we use the TokMLP module to effectively enhance the network\u2019s ability to learn the boundary features of lesion images. In the TokMLP module, the tokenized MLP axial displacement operation strengthens the connection between pixels to facilitate the extraction of local feature information by our network. In order to verify the superiority of our network in segmentation tasks, we conducted extensive experiments on the proposed HMT-Net network and several newly proposed Transformer and MLP networks on three public datasets (ISIC2018, ISBI2017, and ISBI2016) and obtained the following results. Our method achieves 82.39%, 75.53%, and 83.98% on the Dice index and 89.35%, 84.93%, and 91.33% on the IOU. Compared with the latest skin disease segmentation network, FAC-Net, our method improves the Dice index by 1.99%, 1.68%, and 1.6%, respectively. In addition, the IOU indicators have increased by 0.45%, 2.36%, and 1.13%, respectively. The experimental results show that our designed HMT-Net achieves state-of-the-art performance superior to other segmentation methods.<\/jats:p>","DOI":"10.3390\/s23063067","type":"journal-article","created":{"date-parts":[[2023,3,13]],"date-time":"2023-03-13T04:35:41Z","timestamp":1678682141000},"page":"3067","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["HMT-Net: Transformer and MLP Hybrid Encoder for Skin Disease Segmentation"],"prefix":"10.3390","volume":"23","author":[{"given":"Sen","family":"Yang","sequence":"first","affiliation":[{"name":"College of Information Science and Engineering, Xinjiang University, Urumqi 830046, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0210-2273","authenticated-orcid":false,"given":"Liejun","family":"Wang","sequence":"additional","affiliation":[{"name":"College of Information Science and Engineering, Xinjiang University, Urumqi 830046, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2023,3,13]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"994","DOI":"10.1109\/TMI.2016.2642839","article-title":"Automated Melanoma Recognition in Dermoscopy Images via Very Deep Residual Networks","volume":"36","author":"Yu","year":"2017","journal-title":"IEEE Trans. Med. Imaging"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"\u00dcnver, H.M., and Ayan, E. (2019). Skin Lesion Segmentation in Dermoscopic Images with Combination of YOLO and GrabCut Algorithm. Diagnostics, 9.","DOI":"10.3390\/diagnostics9030072"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1137","DOI":"10.1109\/TMI.2014.2305769","article-title":"Model-Based Classification Methods of Global Patterns in Dermoscopic Images","volume":"33","author":"Saez","year":"2014","journal-title":"IEEE Trans. Med. Imaging"},{"key":"ref_4","first-page":"2151","article-title":"Image segmentation by using threshold techniques","volume":"2","author":"Saleh","year":"2010","journal-title":"J. Comput."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"2275","DOI":"10.1109\/TIP.2009.2025555","article-title":"Automatic Image Segmentation by Dynamic Region Growth and Multiresolution Merging","volume":"18","author":"Ugarriza","year":"2009","journal-title":"IEEE Trans. Image Process."},{"key":"ref_6","first-page":"259","article-title":"Edge Detection Techniques for Image Segmentation","volume":"3","author":"Muthukrishnan","year":"2011","journal-title":"Int. J. Comput. Sci. Inf. Technol."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"2481","DOI":"10.1109\/TPAMI.2016.2644615","article-title":"SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation","volume":"39","author":"Badrinarayanan","year":"2017","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Alom, M.Z., and Hasan, M. (2018). Recurrent Residual Convolutional Neural Network based on U-Net (R2U-Net) for Medical Image Segmentation. arXiv.","DOI":"10.1109\/NAECON.2018.8556686"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Dong, Y., Wang, L., Cheng, S., and Li, Y. (2021). Fac-net: Feedback attention network based on context encoder network for skin lesion segmentation. Sensors, 21.","DOI":"10.3390\/s21155172"},{"key":"ref_10","unstructured":"Gao, Y., Zhou, M., and Metaxas, D.N. (2021). International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer."},{"key":"ref_11","unstructured":"Ji, Y., Zhang, R., Wang, H., Li, Z., Wu, L., Zhang, S., and Luo, P. (2021). International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Hatamizadeh, A., Yang, D., and Roth, H. (2021). Transformers for 3D Medical Image Segmentation. arXiv.","DOI":"10.1109\/WACV51458.2022.00181"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Dang, N., Thanh, H., and Erkan, U. (2019, January 16\u201317). A Skin Lesion Segmentation Method for Dermoscopic Images Based on Adaptive Thresholding with Normalization of Color Models. Proceedings of the IEEE 2019 6th International Conference on Electrical and Electronics Engineering, Istanbul, Turkey.","DOI":"10.1109\/ICEEE2019.2019.00030"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Valanarasu, J.M.J., and Patel, V.M. (2022). UNeXt: MLP-based Rapid Medical Image Segmentation Network. arXiv.","DOI":"10.1007\/978-3-031-16443-9_3"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Yu, T., Li, X., Cai, Y., Sun, M., and Li, P. (2022, January 3\u20138). S2-MLP: Spatial-Shift MLP Architecture for Vision. Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision (WACV), Waikoloa, HI, USA.","DOI":"10.1109\/WACV51458.2022.00367"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Jafari, M., Karimi, N., Nasr-Esfahani, E., Samavi, S., Soroushmehr, S., Ward, K., and Najarian, K. (2016, January 4\u20138). Skin lesion segmentation in clinical images using deep learning. Proceedings of the 2016 23rd International Conference on Pattern Recognition (ICPR), Cancun, Mexico.","DOI":"10.1109\/ICPR.2016.7899656"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"3021","DOI":"10.1016\/j.sigpro.2007.05.026","article-title":"Nonparametric shape priors for active contour-based image segmentation","volume":"87","author":"Kim","year":"2007","journal-title":"Signal Process."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Ben-Cohen, A., Diamant, I., Klang, E., Amitai, M., and Greenspan, H. (2016). Fully Convolutional Network for Liver Segmentation and Lesions Detection, Springer.","DOI":"10.1007\/978-3-319-46976-8_9"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"1876","DOI":"10.1109\/TMI.2017.2695227","article-title":"Automatic Skin Lesion Segmentation Using Deep Fully Convolutional Networks with Jaccard Distance","volume":"36","author":"Yuan","year":"2017","journal-title":"IEEE Trans. Med. Imaging"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"1856","DOI":"10.1109\/TMI.2019.2959609","article-title":"Unet++: Redesigning skip connections to exploit multiscale features in image segmentation","volume":"39","author":"Zhou","year":"2019","journal-title":"IEEE Trans. Med. Imaging"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Huang, H., Lin, L., Tong, R., Hu, H., Zhang, Q., Iwamoto, Y., Han, X., Chen, Y.-W., and Wu, J. (2020, January 4\u20138). UNet 3+: A Full-Scale Connected UNet for Medical Image Segmentation. Proceedings of the ICASSP 2020\u20142020 IEEE International Conference on Acous-tics, Speech and Signal Processing (ICASSP), Barcelona, Spain.","DOI":"10.1109\/ICASSP40776.2020.9053405"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Guo, C., Szemenyei, M., Yi, Y., Wang, W., Chen, B., and Fan, C. (2021, January 10\u201315). SA-UNet: Spatial Attention U-Net for Retinal Vessel Segmenta-tion. Proceedings of the 2020 25th International Conference on Pattern Recognition (ICPR), Milan, Italy.","DOI":"10.1109\/ICPR48806.2021.9413346"},{"key":"ref_23","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_24","doi-asserted-by":"crossref","first-page":"476","DOI":"10.1109\/TMI.2021.3116087","article-title":"SMU-Net: Saliency-Guided Morphology-Aware U-Net for Breast Lesion Segmentation in Ultrasound Image","volume":"41","author":"Ning","year":"2021","journal-title":"IEEE Trans. Med. Imaging"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"102395","DOI":"10.1016\/j.media.2022.102395","article-title":"Boundary-aware context neural network for medical image segmentation","volume":"78","author":"Wang","year":"2022","journal-title":"Med. Image Anal."},{"key":"ref_26","unstructured":"Chen, J., Lu, Y., Yu, Q., Luo, X., Adeli, E., Wang, Y., Lu, L., Yuille, A.L., and Zhou, Y. (2021). Transunet: Transformers make strong encoders for medical image segmentation. arXiv."},{"key":"ref_27","first-page":"2441","article-title":"Uctransnet: Rethinking the skip connections in u-net from a channel-wise perspective with transformer","volume":"36","author":"Wang","year":"2022","journal-title":"Proc. AAAI Conf. Artif. Intell."},{"key":"ref_28","unstructured":"Cao, H., Wang, Y., Chen, J., Jiang, D., Zhang, X., Tian, Q., and Wang, M. (2021). Swin-unet: Unet-like pure transformer for medical image segmentation. arXiv."},{"key":"ref_29","unstructured":"Lian, D., Yu, Z., Sun, X., and Gao, S. (2021). As-mlp: An axial shifted mlp architecture for vision. arXiv."},{"key":"ref_30","unstructured":"Chen, S., Xie, E., Ge, C., Chen, R., Liang, D., and Luo, P. (2021). Cyclemlp: A mlp-like architecture for dense prediction. arXiv."},{"key":"ref_31","unstructured":"Hendrycks, D., and Gimpel, K. (2016). Gaussian error linear units (gelus). arXiv."},{"key":"ref_32","unstructured":"Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., and Gelly, S. (2020). An image is worth 16x16 words: Transformers for image recognition at scale. arXiv."},{"key":"ref_33","unstructured":"Devlin, J., Chang, M.W., Lee, K., and Toutanova, K. (2018). Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"615","DOI":"10.1109\/JBHI.2015.2390032","article-title":"A Novel Approach to Segment Skin Lesions in Dermoscopic Images Based on a Deformable Model","volume":"20","author":"Zhen","year":"2016","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Codella, N.C.F., Gutman, D., Celebi, M.E., Helba, B., Marchetti, M.A., Dusza, S.W., Kalloo, A., Liopyris, K., Mishra, N., and Kittler, H. (2018, January 4\u20137). Skin lesion analysis toward melanoma detection: A challenge at the 2017 International symposium on biomedical imaging (ISBI), hosted by the international skin imaging collaboration (ISIC). Proceedings of the 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018), Washington, DC, USA.","DOI":"10.1109\/ISBI.2018.8363547"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"180161","DOI":"10.1038\/sdata.2018.161","article-title":"The HAM10000 dataset, a large collection of multi-source dermatoscopic images of common pigmented skin lesions","volume":"5","author":"Tschandl","year":"2018","journal-title":"Sci. Data"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/6\/3067\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T18:53:37Z","timestamp":1760122417000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/6\/3067"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,3,13]]},"references-count":36,"journal-issue":{"issue":"6","published-online":{"date-parts":[[2023,3]]}},"alternative-id":["s23063067"],"URL":"https:\/\/doi.org\/10.3390\/s23063067","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,3,13]]}}}