{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,23]],"date-time":"2025-10-23T21:05:48Z","timestamp":1761253548610,"version":"build-2065373602"},"reference-count":66,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2021,5,16]],"date-time":"2021-05-16T00:00:00Z","timestamp":1621123200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>The automatic segmentation of skin lesions is considered to be a key step in the diagnosis and treatment of skin lesions, which is essential to improve the survival rate of patients. However, due to the low contrast, the texture and boundary are difficult to distinguish, which makes the accurate segmentation of skin lesions challenging. To cope with these challenges, this paper proposes an attention-guided network with densely connected convolution for skin lesion segmentation, called CSAG and DCCNet. In the last step of the encoding path, the model uses densely connected convolution to replace the ordinary convolutional layer. A novel attention-oriented filter module called Channel Spatial Fast Attention-guided Filter (CSFAG for short) was designed and embedded in the skip connection of the CSAG and DCCNet. On the ISIC-2017 data set, a large number of ablation experiments have verified the superiority and robustness of the CSFAG module and Densely Connected Convolution. The segmentation performance of CSAG and DCCNet is compared with other latest algorithms, and very competitive results have been achieved in all indicators. The robustness and cross-data set performance of our method was tested on another publicly available data set PH2, further verifying the effectiveness of the model.<\/jats:p>","DOI":"10.3390\/s21103462","type":"journal-article","created":{"date-parts":[[2021,5,17]],"date-time":"2021-05-17T02:31:34Z","timestamp":1621218694000},"page":"3462","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":15,"title":["Attention-Guided Network with Densely Connected Convolution for Skin Lesion Segmentation"],"prefix":"10.3390","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6040-9113","authenticated-orcid":false,"given":"Shengxin","family":"Tao","sequence":"first","affiliation":[{"name":"College of Computer Science and Engineering, Northwest Normal University, Lanzhou 730070, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3034-6729","authenticated-orcid":false,"given":"Yun","family":"Jiang","sequence":"additional","affiliation":[{"name":"College of Computer Science and Engineering, Northwest Normal University, Lanzhou 730070, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2238-0545","authenticated-orcid":false,"given":"Simin","family":"Cao","sequence":"additional","affiliation":[{"name":"College of Computer Science and Engineering, Northwest Normal University, Lanzhou 730070, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8293-7988","authenticated-orcid":false,"given":"Chao","family":"Wu","sequence":"additional","affiliation":[{"name":"College of Computer Science and Engineering, Northwest Normal University, Lanzhou 730070, China"}]},{"given":"Zeqi","family":"Ma","sequence":"additional","affiliation":[{"name":"College of Computer Science and Engineering, Northwest Normal University, Lanzhou 730070, China"}]}],"member":"1968","published-online":{"date-parts":[[2021,5,16]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Ge, Z., Demyanov, S., Chakravorty, R., Bowling, A., and Garnavi, R. (2017, January 11\u201313). Skin disease recognition using deep saliency features and multimodal learning of dermoscopy and clinical images. Proceedings of the International Conference on Medical Image Computing and Computer-Assisted Intervention, Quebec City, QC, Canada.","DOI":"10.1007\/978-3-319-66179-7_29"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"986","DOI":"10.1016\/j.ijrobp.2013.08.035","article-title":"Combinations of radiation therapy and immunotherapy for melanoma: A review of clinical outcomes","volume":"88","author":"Barker","year":"2014","journal-title":"Int. J. Radiat. Oncol. Biol. Phys."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"362","DOI":"10.1016\/j.compmedimag.2007.01.003","article-title":"A methodological approach to the classification of dermoscopy images","volume":"31","author":"Celebi","year":"2007","journal-title":"Comput. Med. Imaging Graph."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Woltsche, N., Piana, S., Ferrara, G., Hofmann-Wellenhof, R., and Zalaudek, I. (2015). Three dermoscopic signs of growth of pigmented lesions. J. Am. Acad. Dermatol.","DOI":"10.1016\/j.jaad.2015.06.032"},{"key":"ref_5","first-page":"669","article-title":"Dermoscopy compared with naked eye examination for the diagnosis of primary melanoma: A meta-analysis of studies performed in a clinical setting","volume":"159","author":"Vestergaard","year":"2008","journal-title":"Br. J. Dermatol."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"148","DOI":"10.1016\/j.compmedimag.2008.11.002","article-title":"Lesion border detection in dermoscopy images","volume":"33","author":"Celebi","year":"2009","journal-title":"Comput. Med. Imaging Graph."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"127","DOI":"10.1016\/j.cmpb.2016.03.032","article-title":"Computational methods for the image segmentation of pigmented skin lesions: A review","volume":"131","author":"Oliveira","year":"2016","journal-title":"Comput. Methods Progr. Biomed."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1810","DOI":"10.1109\/TII.2020.2994227","article-title":"Sparse low-rank tensor decomposition for metal defect detection using thermographic imaging diagnostics","volume":"17","author":"Ahmed","year":"2020","journal-title":"IEEE Trans. Ind. Inform."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"12351","DOI":"10.1007\/s00521-020-04737-6","article-title":"Tracking changes in user activity from unlabelled smart home sensor data using unsupervised learning methods","volume":"32","author":"Gupta","year":"2020","journal-title":"Neural Comput. Appl."},{"key":"ref_10","unstructured":"Li, X., Yu, L., Chen, H., Fu, C.W., and Heng, P.A. (2018). Semi-supervised skin lesion segmentation via transformation consistent self-ensembling model. arXiv."},{"key":"ref_11","unstructured":"Pathan, S., and Tripathi, A. (2020). Y-net: Biomedical Image Segmentation and Clustering. arXiv."},{"key":"ref_12","unstructured":"Feyjie, A.R., Azad, R., Pedersoli, M., Kauffman, C., Ayed, I.B., and Dolz, J. (2020). Semi-supervised few-shot learning for medical image segmentation. arXiv."},{"key":"ref_13","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_14","doi-asserted-by":"crossref","first-page":"527","DOI":"10.1109\/JBHI.2018.2859898","article-title":"Dense deconvolutional network for skin lesion segmentation","volume":"23","author":"Li","year":"2018","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"519","DOI":"10.1109\/JBHI.2017.2787487","article-title":"Improving dermoscopic image segmentation with enhanced convolutional-deconvolutional networks","volume":"23","author":"Yuan","year":"2017","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"2065","DOI":"10.1109\/TBME.2017.2712771","article-title":"Dermoscopic image segmentation via multistage fully convolutional networks","volume":"64","author":"Bi","year":"2017","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_17","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_18","doi-asserted-by":"crossref","unstructured":"Ronneberger, O., Fischer, P., and Brox, T. (2015, January 5\u20139). U-net: Convolutional networks for biomedical image segmentation. Proceedings of the International Conference on Medical Image Computing and Computer-Assisted Intervention, Munich, Germany.","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Lin, G., Milan, A., Shen, C., and Reid, I. (2017, January 21\u201326). Refinenet: Multi-path refinement networks for high-resolution semantic segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.549"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Lin, G., Shen, C., Van Den Hengel, A., and Reid, I. (2016, January 27\u201330). Efficient piecewise training of deep structured models for semantic segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.348"},{"key":"ref_21","unstructured":"Lin, Z., Feng, M., Santos, C.N.D., Yu, M., Xiang, B., Zhou, B., and Bengio, Y. (2017). A structured self-attentive sentence embedding. arXiv."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Shen, T., Zhou, T., Long, G., Jiang, J., Pan, S., and Zhang, C. (2017). Disan: Directional self-attention network for rnn\/cnn-free language understanding. arXiv.","DOI":"10.1609\/aaai.v32i1.11941"},{"key":"ref_23","unstructured":"He, K., and Sun, J. (2015). Fast guided filter. arXiv."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Milletari, F., Navab, N., and Ahmadi, S.A. (2016, January 25\u201328). V-net: Fully convolutional neural networks for volumetric medical image segmentation. Proceedings of the 2016 Fourth International Conference on 3D Vision (3DV), Stanford, CA, USA.","DOI":"10.1109\/3DV.2016.79"},{"key":"ref_25","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. Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support, Springer.","DOI":"10.1007\/978-3-030-00889-5_1"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"74","DOI":"10.1016\/j.neunet.2019.08.025","article-title":"MultiResUNet: Rethinking the U-Net architecture for multimodal biomedical image segmentation","volume":"121","author":"Ibtehaz","year":"2020","journal-title":"Neural Netw."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Huang, H., Lin, L., Tong, R., Hu, H., Zhang, Q., Iwamoto, Y., 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 Acoustics, Speech and Signal Processing (ICASSP), Barcelona, Spain.","DOI":"10.1109\/ICASSP40776.2020.9053405"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"834","DOI":"10.1109\/TPAMI.2017.2699184","article-title":"Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs","volume":"40","author":"Chen","year":"2017","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_29","unstructured":"Chen, L.C., Papandreou, G., Schroff, F., and Adam, H. (2017). Rethinking atrous convolution for semantic image segmentation. arXiv."},{"key":"ref_30","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 (ECCV), Munich, Germany.","DOI":"10.1007\/978-3-030-01234-2_49"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Zhao, H., Shi, J., Qi, X., Wang, X., and Jia, J. (2017, January 21\u201326). Pyramid scene parsing network. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.660"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"1254","DOI":"10.1109\/34.730558","article-title":"A model of saliency-based visual attention for rapid scene analysis","volume":"20","author":"Itti","year":"1998","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"201","DOI":"10.1038\/nrn755","article-title":"Control of goal-directed and stimulus-driven attention in the brain","volume":"3","author":"Corbetta","year":"2002","journal-title":"Nat. Rev. Neurosci."},{"key":"ref_34","unstructured":"Mnih, V., Heess, N., Graves, A., and Kavukcuoglu, K. (2014). Recurrent models of visual attention. arXiv."},{"key":"ref_35","unstructured":"Ba, J., Mnih, V., and Kavukcuoglu, K. (2014). Multiple object recognition with visual attention. arXiv."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Wang, F., Jiang, M., Qian, C., Yang, S., Li, C., Zhang, H., and Tang, X. (2017, January 21\u201326). Residual attention network for image classification. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.683"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Hu, J., Shen, L., and Sun, G. (2018, January 18\u201323). 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_38","doi-asserted-by":"crossref","unstructured":"Zhao, H., Zhang, Y., Liu, S., Shi, J., Loy, C.C., Lin, D., and Jia, J. (2018, January 8\u201314). Psanet: Point-wise spatial attention network for scene parsing. Proceedings of the European Conference on Computer Vision (ECCV), Munich, Germany.","DOI":"10.1007\/978-3-030-01240-3_17"},{"key":"ref_39","unstructured":"Chen, Y., Kalantidis, Y., Li, J., Yan, S., and Feng, J. (2018). A2-Nets: Double Attention Networks. arXiv."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Fu, J., Liu, J., Tian, H., Li, Y., Bao, Y., Fang, Z., and Lu, H. (2019, January 16\u201320). Dual attention network for scene segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00326"},{"key":"ref_41","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 (ECCV), Munich, Germany.","DOI":"10.1007\/978-3-030-01234-2_1"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"357","DOI":"10.1109\/TMI.2020.3027341","article-title":"Automated Skin Lesion Segmentation via an Adaptive Dual Attention Module","volume":"40","author":"Wu","year":"2020","journal-title":"IEEE Trans. Med. Imaging"},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Hamad, R.A., Kimura, M., Yang, L., Woo, W.L., and Wei, B. (2021). Dilated Causal Convolution with Multi-Head Self Attention for Sensor Human Activity Recognition. Neural Comput. Appl., 1\u201318. Available online: https:\/\/link.springer.com\/article\/10.1007\/s00521-021-06007-5.","DOI":"10.1007\/s00521-021-06007-5"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"472","DOI":"10.1109\/TIP.2020.3036770","article-title":"A Lightweight Spatial and Temporal Multi-Feature Fusion Network for Defect Detection","volume":"30","author":"Hu","year":"2020","journal-title":"IEEE Trans. Image Process."},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Kawahara, J., and Hamarneh, G. (2016, January 17). Multi-resolution-tract CNN with hybrid pretrained and skin-lesion trained layers. Proceedings of the International Workshop on Machine Learning in Medical Imaging, Athens, Greece.","DOI":"10.1007\/978-3-319-47157-0_20"},{"key":"ref_46","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":"2016","journal-title":"IEEE Trans. Med. Imaging"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"111","DOI":"10.1016\/j.compbiomed.2018.11.010","article-title":"Domain-specific classification-pretrained fully convolutional network encoders for skin lesion segmentation","volume":"104","author":"Tschandl","year":"2019","journal-title":"Comput. Biol. Med."},{"key":"ref_48","unstructured":"Goyal, M., Ng, J., and Yap, M.H. (2018). Multi-class lesion diagnosis with pixel-wise classification network. arXiv."},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Hasan, M.K., Dahal, L., Samarakoon, P.N., Tushar, F.I., and Mart\u00ed, R. (2020). DSNet: Automatic dermoscopic skin lesion segmentation. Comput. Biol. Med., 103738.","DOI":"10.1016\/j.compbiomed.2020.103738"},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"1397","DOI":"10.1109\/TPAMI.2012.213","article-title":"Guided image filtering","volume":"35","author":"He","year":"2012","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Huang, G., Liu, Z., Van Der Maaten, L., and Weinberger, K.Q. (2017, January 21\u201326). Densely connected convolutional networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.243"},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Codella, N.C., Gutman, D., Celebi, M.E., Helba, B., Marchetti, M.A., Dusza, S.W., and Halpern, A. (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_53","doi-asserted-by":"crossref","unstructured":"Mendon\u00e7a, T., Ferreira, P.M., Marques, J.S., Marcal, A.R., and Rozeira, J. (2013, January 3\u20137). PH 2-A dermoscopic image database for research and benchmarking. Proceedings of the 2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Osaka, Japan.","DOI":"10.1109\/EMBC.2013.6610779"},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"1597","DOI":"10.1109\/TMI.2018.2791488","article-title":"Joint optic disc and cup segmentation based on multi-label deep network and polar transformation","volume":"37","author":"Fu","year":"2018","journal-title":"IEEE Trans. Med. Imaging"},{"key":"ref_55","unstructured":"Li, H., Xiong, P., An, J., and Wang, L. (2018). Pyramid attention network for semantic segmentation. arXiv."},{"key":"ref_56","unstructured":"Badrinarayanan, V., Handa, A., and Cipolla, R. (2015). Segnet: A deep convolutional encoder-decoder architecture for robust semantic pixel-wise labelling. arXiv."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"221","DOI":"10.1016\/j.cmpb.2018.05.027","article-title":"Skin lesion segmentation in dermoscopy images via deep full resolution convolutional networks","volume":"162","author":"Choi","year":"2018","journal-title":"Comput. Methods Programs Biomed."},{"key":"ref_58","doi-asserted-by":"crossref","unstructured":"Goyal, M., Oakley, A., Bansal, P., Dancey, D., and Yap, M.H. (2019). Skin Lesion Segmentation in Dermoscopic Images with Ensemble Deep Learning Methods. IEEE Access.","DOI":"10.1109\/ACCESS.2019.2960504"},{"key":"ref_59","unstructured":"Wen, H. (2017). II-FCN for skin lesion analysis towards melanoma detection. arXiv."},{"key":"ref_60","doi-asserted-by":"crossref","unstructured":"Sarker, M.M.K., Rashwan, H.A., Akram, F., Banu, S.F., Saleh, A., and Singh, V.K. (2018, January 16\u201320). SLSDeep: Skin lesion segmentation based on dilated residual and pyramid pooling networks. Proceedings of the International Conference on Medical Image Computing and Computer-Assisted Intervention, Granada, Spain.","DOI":"10.1007\/978-3-030-00934-2_3"},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"509","DOI":"10.1109\/JBHI.2018.2839647","article-title":"Supervised saliency map driven segmentation of lesions in dermoscopic images","volume":"23","author":"Jahanifar","year":"2018","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"78","DOI":"10.1016\/j.patcog.2018.08.001","article-title":"Step-wise integration of deep class-specific learning for dermoscopic image segmentation","volume":"85","author":"Bi","year":"2019","journal-title":"Pattern Recognition"},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"136616","DOI":"10.1109\/ACCESS.2019.2940794","article-title":"Attention-based DenseUnet network with adversarial training for skin lesion segmentation","volume":"7","author":"Wei","year":"2019","journal-title":"IEEE Access"},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"101716","DOI":"10.1016\/j.media.2020.101716","article-title":"Skin lesion segmentation via generative adversarial networks with dual discriminators","volume":"64","author":"Lei","year":"2020","journal-title":"Med. Image Anal."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"122811","DOI":"10.1109\/ACCESS.2020.3007512","article-title":"Skin Lesion Segmentation Based on Multi-Scale Attention Convolutional Neural Network","volume":"8","author":"Jiang","year":"2020","journal-title":"IEEE Access"},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"105241","DOI":"10.1016\/j.cmpb.2019.105241","article-title":"Skin lesion segmentation using high-resolution convolutional neural network","volume":"186","author":"Xie","year":"2020","journal-title":"Comput. 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