{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,13]],"date-time":"2026-01-13T03:47:47Z","timestamp":1768276067879,"version":"3.49.0"},"reference-count":45,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2021,4,2]],"date-time":"2021-04-02T00:00:00Z","timestamp":1617321600000},"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>Skin lesion segmentation is a primary step for skin lesion analysis, which can benefit the subsequent classification task. It is a challenging task since the boundaries of pigment regions may be fuzzy and the entire lesion may share a similar color. Prevalent deep learning methods for skin lesion segmentation make predictions by ensembling different convolutional neural networks (CNN), aggregating multi-scale information, or by multi-task learning framework. The main purpose of doing so is trying to make use of as much information as possible so as to make robust predictions. A multi-task learning framework has been proved to be beneficial for the skin lesion segmentation task, which is usually incorporated with the skin lesion classification task. However, multi-task learning requires extra labeling information which may not be available for the skin lesion images. In this paper, a novel CNN architecture using auxiliary information is proposed. Edge prediction, as an auxiliary task, is performed simultaneously with the segmentation task. A cross-connection layer module is proposed, where the intermediate feature maps of each task are fed into the subblocks of the other task which can implicitly guide the neural network to focus on the boundary region of the segmentation task. In addition, a multi-scale feature aggregation module is proposed, which makes use of features of different scales and enhances the performance of the proposed method. Experimental results show that the proposed method obtains a better performance compared with the state-of-the-art methods with a Jaccard Index (JA) of 79.46, Accuracy (ACC) of 94.32, SEN of 88.76 with only one integrated model, which can be learned in an end-to-end manner.<\/jats:p>","DOI":"10.3390\/jimaging7040067","type":"journal-article","created":{"date-parts":[[2021,4,2]],"date-time":"2021-04-02T04:13:51Z","timestamp":1617336831000},"page":"67","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":72,"title":["Skin Lesion Segmentation Using Deep Learning with Auxiliary Task"],"prefix":"10.3390","volume":"7","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0185-2638","authenticated-orcid":false,"given":"Lina","family":"Liu","sequence":"first","affiliation":[{"name":"Department of Electrical and Computer Engineering, University of Alberta, Edmonton, AB T6G1H9, Canada"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ying Y.","family":"Tsui","sequence":"additional","affiliation":[{"name":"Department of Electrical and Computer Engineering, University of Alberta, Edmonton, AB T6G1H9, Canada"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mrinal","family":"Mandal","sequence":"additional","affiliation":[{"name":"Department of Electrical and Computer Engineering, University of Alberta, Edmonton, AB T6G1H9, Canada"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2021,4,2]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"7","DOI":"10.3322\/caac.21590","article-title":"Cancer statistics, 2020","volume":"70","author":"Siegel","year":"2020","journal-title":"CA A Cancer J. Clin."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"115","DOI":"10.1038\/nature21056","article-title":"Dermatologist-level classification of skin cancer with deep neural networks","volume":"542","author":"Esteva","year":"2017","journal-title":"Nature"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"973","DOI":"10.1111\/j.1365-2133.2011.10208.x","article-title":"Mobile teledermatology for skin tumour screening: Diagnostic accuracy of clinical and dermoscopic image tele-evaluation using cellular phones","volume":"164","author":"Kroemer","year":"2011","journal-title":"Br. J. Dermatol."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Alves, J., Moreira, D., Alves, P., Rosado, L., and Vasconcelos, M.J.M. (2019). Automatic focus assessment on dermoscopic images acquired with smartphones. Sensors, 19.","DOI":"10.3390\/s19224957"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"99","DOI":"10.1016\/j.ijmedinf.2018.08.004","article-title":"Fighting melanoma with smartphones: A snapshot of where we are a decade after app stores opened their doors","volume":"118","author":"Ngoo","year":"2018","journal-title":"Int. J. Med. Inform."},{"key":"ref_6","first-page":"521","article-title":"Abcd rule of dermatoscopy-a new practical method for early recognition of malignant-melanoma","volume":"4","author":"Stolz","year":"1994","journal-title":"Eur. J. Dermatol."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"3","DOI":"10.5070\/D366X188MB","article-title":"The clinical diagnosis of early malignant melanoma: Expansion of the ABCD criteria to improve diagnostic sensitivity","volume":"5","author":"Hazen","year":"1999","journal-title":"Dermatol. Online J."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1563","DOI":"10.1001\/archderm.134.12.1563","article-title":"Epiluminescence microscopy for the diagnosis of doubtful melanocytic skin lesions: Comparison of the ABCD rule of dermatoscopy and a new 7-point checklist based on pattern analysis","volume":"134","author":"Argenziano","year":"1998","journal-title":"Arch. Dermatol."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"571","DOI":"10.1016\/S0190-9622(87)70239-4","article-title":"In vivo epiluminescence microscopy of pigmented skin lesions. I. Pattern analysis of pigmented skin lesions","volume":"17","author":"Pehamberger","year":"1987","journal-title":"J. Am. Acad. Dermatol."},{"key":"ref_10","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_11","doi-asserted-by":"crossref","first-page":"101765","DOI":"10.1016\/j.compmedimag.2020.101765","article-title":"Automatic skin lesion classification based on mid-level feature learning","volume":"84","author":"Liu","year":"2020","journal-title":"Comput. Med. Imaging Graph."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Codella, N.C., 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_13","doi-asserted-by":"crossref","unstructured":"Li, Y., and Shen, L. (2018). Skin lesion analysis towards melanoma detection using deep learning network. Sensors, 18.","DOI":"10.3390\/s18020556"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"130552","DOI":"10.1109\/ACCESS.2019.2940418","article-title":"FCA-Net: Adversarial learning for skin lesion segmentation based on multi-scale features and factorized channel attention","volume":"7","author":"Singh","year":"2019","journal-title":"IEEE Access"},{"key":"ref_15","unstructured":"Yang, X., Zeng, Z., Yeo, S.Y., Tan, C., Tey, H.L., and Su, Y. (2017). A novel multi-task deep learning model for skin lesion segmentation and classification. arXiv."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Xie, Y., Zhang, J., Xia, Y., and Shen, C. (2020). A mutual bootstrapping model for automated skin lesion segmentation and classification. IEEE Trans. Med. Imaging.","DOI":"10.1109\/TMI.2020.2972964"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Humayun, J., Malik, A.S., and Kamel, N. (2011, January 17\u201318). Multilevel thresholding for segmentation of pigmented skin lesions. Proceedings of the 2011 IEEE International Conference on Imaging Systems and Techniques, Batu Ferringhi, Malaysia.","DOI":"10.1109\/IST.2011.5962214"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"929","DOI":"10.1109\/TITB.2011.2157829","article-title":"Automatic skin lesion segmentation via iterative stochastic region merging","volume":"15","author":"Wong","year":"2011","journal-title":"IEEE Trans. Inf. Technol. Biomed."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"489","DOI":"10.1109\/JBHI.2018.2832455","article-title":"Active contours based segmentation and lesion periphery analysis for characterization of skin lesions in dermoscopy images","volume":"23","author":"Riaz","year":"2018","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Abbas, Q., Fond\u00f3n, I., Sarmiento, A., and Celebi, M.E. (2014, January 22\u201324). An improved segmentation method for non-melanoma skin lesions using active contour model. Proceedings of the International Conference Image Analysis and Recognition, Vilamoura, Portugal.","DOI":"10.1007\/978-3-319-11755-3_22"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"1172","DOI":"10.1016\/j.patcog.2008.09.007","article-title":"A multi-direction GVF snake for the segmentation of skin cancer images","volume":"42","author":"Tang","year":"2009","journal-title":"Pattern Recognit."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Jafari, M.H., Samavi, S., Soroushmehr, S.M.R., Mohaghegh, H., Karimi, N., and Najarian, K. (2016, January 25\u201328). Set of descriptors for skin cancer diagnosis using non-dermoscopic color images. Proceedings of the IEEE International Conference on Image Processing (ICIP), Phoenix, AZ, USA.","DOI":"10.1109\/ICIP.2016.7532837"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Ali, A.R., Couceiro, M.S., and Hassenian, A.E. (2014, January 14\u201316). Melanoma detection using fuzzy C-means clustering coupled with mathematical morphology. Proceedings of the International Conference on Hybrid Intelligent Systems (HIS), Hawally, Kuwait.","DOI":"10.1109\/HIS.2014.7086175"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2016, January 27\u201330). Deep residual learning for image recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_25","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_26","doi-asserted-by":"crossref","unstructured":"Maninis, K.K., Caelles, S., Pont-Tuset, J., and Van Gool, L. (2018, January 18\u201323). Deep extreme cut: From extreme points to object segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00071"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Awan, M.J., Rahim, M.S.M., Salim, N., Mohammed, M.A., Garcia-Zapirain, B., and Abdulkareem, K.H. (2021). Efficient Detection of Knee Anterior Cruciate Ligament from Magnetic Resonance Imaging Using Deep Learning Approach. Diagnostics, 11.","DOI":"10.3390\/diagnostics11010105"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Jafari, M.H., Karimi, N., Nasr-Esfahani, E., Samavi, S., Soroushmehr, S.M.R., Ward, K., and Najarian, K. (2016, January 4\u20138). Skin lesion segmentation in clinical images using deep learning. Proceedings of the International Conference on Pattern Recognition (ICPR), Cancun, Mexico.","DOI":"10.1109\/ICPR.2016.7899656"},{"key":"ref_29","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_30","unstructured":"Berseth, M. (2017). ISIC 2017-Skin Lesion Analysis Towards Melanoma Detection. arXiv."},{"key":"ref_31","unstructured":"Chang, H. (2017). Skin cancer reorganization and classification with deep neural network. arXiv."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Liu, L., Mou, L., Zhu, X.X., and Mandal, M. (2019, January 5\u20138). Skin Lesion Segmentation Based on Improved U-net. Proceedings of the 2019 IEEE Canadian Conference of Electrical and Computer Engineering (CCECE), Edmonton, AB, Canada.","DOI":"10.1109\/CCECE.2019.8861848"},{"key":"ref_33","unstructured":"Abhishek, K., Hamarneh, G., and Drew, M.S. (2020, January 14\u201319). Illumination-based Transformations Improve Skin Lesion Segmentation in Dermoscopic Images. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition Workshops, Seattle, WA, USA."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Yuan, Y. (2017). Automatic skin lesion segmentation with fully convolutional-deconvolutional networks. arXiv.","DOI":"10.1109\/TMI.2017.2695227"},{"key":"ref_35","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_36","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 Recognit."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Sarker, M.M.K., Rashwan, H.A., Akram, F., Banu, S.F., Saleh, A., Singh, V.K., Chowdhury, F.U., Abdulwahab, S., Romani, S., and Radeva, P. (2018, January 16\u201320). SLSDeep: Skin lesion segmentation based on dilated residual and pyramid pooling networks. In 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_38","doi-asserted-by":"crossref","unstructured":"Cheng, T., Wang, X., Huang, L., and Liu, W. (2020, January 23\u201328). Boundary-preserving mask R-CNN. Proceedings of the European Conference on Computer Vision, Glasgow, UK.","DOI":"10.1007\/978-3-030-58568-6_39"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Kim, M., Woo, S., Kim, D., and Kweon, I.S. (2021, January 5\u20139). The devil is in the boundary: Exploiting boundary representation for basis-based instance segmentation. Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision, Waikoloa, HI, USA.","DOI":"10.1109\/WACV48630.2021.00097"},{"key":"ref_40","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_41","unstructured":"Schaefer, S., McPhail, T., and Warren, J. (August, January 30). Image deformation using moving least squares. Proceedings of the ACM Transactions on Graphics (TOG), Boston, MA, USA."},{"key":"ref_42","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_43","doi-asserted-by":"crossref","first-page":"77037","DOI":"10.1109\/ACCESS.2019.2921815","article-title":"Dense-residual network with adversarial learning for skin lesion segmentation","volume":"7","author":"Tu","year":"2019","journal-title":"IEEE Access"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"4171","DOI":"10.1109\/ACCESS.2019.2960504","article-title":"Skin lesion segmentation in dermoscopic images with ensemble deep learning methods","volume":"8","author":"Goyal","year":"2019","journal-title":"IEEE Access"},{"key":"ref_45","unstructured":"Ribeiro, V., Avila, S., and Valle, E. (2019). Handling inter-annotator agreement for automated skin lesion segmentation. arXiv."}],"container-title":["Journal of Imaging"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2313-433X\/7\/4\/67\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,13]],"date-time":"2025-10-13T13:33:36Z","timestamp":1760362416000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2313-433X\/7\/4\/67"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,4,2]]},"references-count":45,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2021,4]]}},"alternative-id":["jimaging7040067"],"URL":"https:\/\/doi.org\/10.3390\/jimaging7040067","relation":{},"ISSN":["2313-433X"],"issn-type":[{"value":"2313-433X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,4,2]]}}}