{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,8]],"date-time":"2026-09-08T14:22:04Z","timestamp":1788877324931,"version":"build-2803163510"},"reference-count":72,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2024,11,20]],"date-time":"2024-11-20T00:00:00Z","timestamp":1732060800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Research Foundation of Korea (NRF)","award":["RS-2023-00243034"],"award-info":[{"award-number":["RS-2023-00243034"]}]},{"name":"National Research Foundation of Korea (NRF)","award":["UoSM\/SF2022\/4"],"award-info":[{"award-number":["UoSM\/SF2022\/4"]}]},{"name":"National Research Foundation of Korea (NRF)","award":["590111153"],"award-info":[{"award-number":["590111153"]}]},{"name":"National Research Foundation of Korea (NRF)","award":["2021-0-00755"],"award-info":[{"award-number":["2021-0-00755"]}]},{"name":"Korean government (MSIT)","award":["RS-2023-00243034"],"award-info":[{"award-number":["RS-2023-00243034"]}]},{"name":"Korean government (MSIT)","award":["UoSM\/SF2022\/4"],"award-info":[{"award-number":["UoSM\/SF2022\/4"]}]},{"name":"Korean government (MSIT)","award":["590111153"],"award-info":[{"award-number":["590111153"]}]},{"name":"Korean government (MSIT)","award":["2021-0-00755"],"award-info":[{"award-number":["2021-0-00755"]}]},{"name":"University of Southampton Malaysia (UoSM)","award":["RS-2023-00243034"],"award-info":[{"award-number":["RS-2023-00243034"]}]},{"name":"University of Southampton Malaysia (UoSM)","award":["UoSM\/SF2022\/4"],"award-info":[{"award-number":["UoSM\/SF2022\/4"]}]},{"name":"University of Southampton Malaysia (UoSM)","award":["590111153"],"award-info":[{"award-number":["590111153"]}]},{"name":"University of Southampton Malaysia (UoSM)","award":["2021-0-00755"],"award-info":[{"award-number":["2021-0-00755"]}]},{"name":"Institute of Information &amp; communications Technology Planning &amp; Evaluation (IITP)","award":["RS-2023-00243034"],"award-info":[{"award-number":["RS-2023-00243034"]}]},{"name":"Institute of Information &amp; communications Technology Planning &amp; Evaluation (IITP)","award":["UoSM\/SF2022\/4"],"award-info":[{"award-number":["UoSM\/SF2022\/4"]}]},{"name":"Institute of Information &amp; communications Technology Planning &amp; Evaluation (IITP)","award":["590111153"],"award-info":[{"award-number":["590111153"]}]},{"name":"Institute of Information &amp; communications Technology Planning &amp; Evaluation (IITP)","award":["2021-0-00755"],"award-info":[{"award-number":["2021-0-00755"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Bioengineering"],"abstract":"<jats:p>Classifying and segmenting skin cancer represent pivotal objectives for automated diagnostic systems that utilize dermoscopy images. However, these tasks present significant challenges due to the diverse shape variations of skin lesions and the inherently fuzzy nature of dermoscopy images, including low contrast and the presence of artifacts. Given the robust correlation between the classification of skin lesions and their segmentation, we propose that employing a combined learning method holds the promise of considerably enhancing the performance of both tasks. In this paper, we present a unified multi-task learning strategy that concurrently classifies abnormalities of skin lesions and allows for the joint segmentation of lesion boundaries. This approach integrates an optimization technique known as joint reverse learning, which fosters mutual enhancement through extracting shared features and limiting task dominance across the two tasks. The effectiveness of the proposed method was assessed using two publicly available datasets, ISIC 2016 and PH2, which included melanoma and benign skin cancers. In contrast to the single-task learning strategy, which solely focuses on either classification or segmentation, the experimental findings demonstrated that the proposed network improves the diagnostic capability of skin tumor screening and analysis. The proposed method achieves a significant segmentation performance on skin lesion boundaries, with Dice Similarity Coefficients (DSC) of 89.48% and 88.81% on the ISIC 2016 and PH2 datasets, respectively. Additionally, our multi-task learning approach enhances classification, increasing the F1 score from 78.26% (baseline ResNet50) to 82.07% on ISIC 2016 and from 82.38% to 85.50% on PH2. This work showcases its potential applicability across varied clinical scenarios.<\/jats:p>","DOI":"10.3390\/bioengineering11111173","type":"journal-article","created":{"date-parts":[[2024,11,20]],"date-time":"2024-11-20T10:37:35Z","timestamp":1732099055000},"page":"1173","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["A Unified Multi-Task Learning Model with Joint Reverse Optimization for Simultaneous Skin Lesion Segmentation and Diagnosis"],"prefix":"10.3390","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1548-965X","authenticated-orcid":false,"given":"Mohammed A.","family":"Al-masni","sequence":"first","affiliation":[{"name":"Department of Artificial Intelligence and Data Science, College of AI Convergence, Sejong University, Seoul 05006, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Abobakr Khalil","family":"Al-Shamiri","sequence":"additional","affiliation":[{"name":"School of Computer Science, University of Southampton Malaysia, Iskandar Puteri 79100, Johor, Malaysia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9007-6284","authenticated-orcid":false,"given":"Dildar","family":"Hussain","sequence":"additional","affiliation":[{"name":"Department of Artificial Intelligence and Data Science, College of AI Convergence, Sejong University, Seoul 05006, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0002-9386","authenticated-orcid":false,"given":"Yeong Hyeon","family":"Gu","sequence":"additional","affiliation":[{"name":"Department of Artificial Intelligence and Data Science, College of AI Convergence, Sejong University, Seoul 05006, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2024,11,20]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"12","DOI":"10.3322\/caac.21820","article-title":"Cancer statistics, 2024","volume":"74","author":"Siegel","year":"2024","journal-title":"CA Cancer J. Clin."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"301","DOI":"10.3322\/caac.20074","article-title":"The Evolution of Melanoma Diagnosis: 25 Years Beyond the ABCDs","volume":"60","author":"Rigel","year":"2010","journal-title":"CA Cancer J. Clin."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"551","DOI":"10.1016\/S0190-9622(94)70061-3","article-title":"The Abcd Rule of Dermatoscopy\u2014High Prospective Value in the Diagnosis of Doubtful Melanocytic Skin-Lesions","volume":"30","author":"Nachbar","year":"1994","journal-title":"J. Am. Acad. Dermatol."},{"key":"ref_4","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_5","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_6","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_7","doi-asserted-by":"crossref","first-page":"10","DOI":"10.1016\/j.media.2019.02.010","article-title":"Medical image classification using synergic deep learning","volume":"54","author":"Zhang","year":"2019","journal-title":"Med. Image Anal."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Al-masni, M.A., Kim, D.H., and Kim, T.S. (2020). Multiple skin lesions diagnostics via integrated deep convolutional networks for segmentation and classification. Comput. Methods Programs Biomed., 190.","DOI":"10.1016\/j.cmpb.2020.105351"},{"key":"ref_9","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_10","doi-asserted-by":"crossref","unstructured":"Chen, S., Wang, Z., Shi, J., Liu, B., and Yu, N. (2018, January 24). A multi-task framework with feature passing module for skin lesion classification and segmentation. Proceedings of the IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018), Washington, DC, USA.","DOI":"10.1109\/ISBI.2018.8363769"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Zhang, Z., Gao, J., Li, S., and Wang, H. (2024). RMCNet: A Liver Cancer Segmentation Network Based on 3D Multi-Scale Convolution, Attention, and Residual Path. Bioengineering, 11.","DOI":"10.3390\/bioengineering11111073"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Xu, Y., Quan, R., Xu, W., Huang, Y., Chen, X., and Liu, F. (2024). Advances in Medical Image Segmentation: A Comprehensive Review of Traditional, Deep Learning and Hybrid Approaches. Bioengineering, 11.","DOI":"10.3390\/bioengineering11101034"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Ramakrishnan, V., Artinger, A., Daza Barragan, L.A., Daza, J., Winter, L., Niedermair, T., Itzel, T., Arbelaez, P., Teufel, A., and Cotarelo, C.L. (2024). Nuclei Detection and Segmentation of Histopathological Images Using a Feature Pyramidal Network Variant of a Mask R-CNN. Bioengineering, 11.","DOI":"10.3390\/bioengineering11100994"},{"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":"2019","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"ref_15","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_16","doi-asserted-by":"crossref","unstructured":"Xie, F.Y., Yang, J.W., Liu, J., Jiang, Z.G., Zheng, Y.S., and Wang, Y.K. (2020). Skin lesion segmentation using high-resolution convolutional neural network. Comput. Methods Programs Biomed., 186.","DOI":"10.1016\/j.cmpb.2019.105241"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"109524","DOI":"10.1016\/j.patcog.2023.109524","article-title":"MSCA-Net: Multi-scale contextual attention network for skin lesion segmentation","volume":"139","author":"Sun","year":"2023","journal-title":"Pattern Recogn."},{"key":"ref_18","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":"2021","journal-title":"IEEE Trans. Med. Imaging"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"145","DOI":"10.1109\/JBHI.2022.3162342","article-title":"ICL-Net: Global and Local Inter-Pixel Correlations Learning Network for Skin Lesion Segmentation","volume":"27","author":"Cao","year":"2023","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Al-masni, M.A., and Kim, D.H. (2021). CMM-Net: Contextual multi-scale multi-level network for efficient biomedical image segmentation. Sci. Rep., 11.","DOI":"10.1038\/s41598-021-89686-3"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"102327","DOI":"10.1016\/j.media.2021.102327","article-title":"FAT-Net: Feature adaptive transformers for automated skin lesion segmentation","volume":"76","author":"Wu","year":"2022","journal-title":"Med. Image Anal."},{"key":"ref_22","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_23","doi-asserted-by":"crossref","unstructured":"Zhu, W.H., Tian, J.Y., Chen, M.Z., Chen, L.N., and Chen, J.X. (2024). MSS-UNet: A Multi-Spatial-Shift MLP-based UNet for skin lesion segmentation. Comput. Biol. Med., 168.","DOI":"10.1016\/j.compbiomed.2023.107719"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Li, H., Ding, J., Shi, X., Zhang, Q., Yu, P., and Li, H. (2024). D-SAT: Dual semantic aggregation transformer with dual attention for medical image segmentation. Phys. Med. Biol., 69.","DOI":"10.1088\/1361-6560\/acf2e5"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Chen, L., Li, J., Zou, Y., and Wang, T. (2024). ETU-Net: Edge enhancement-guided U-Net with transformer for skin lesion segmentation. Phys. Med. Biol., 69.","DOI":"10.1088\/1361-6560\/ad13d2"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Feng, X., Lin, J.Y., Feng, C.M., and Lu, G.M. (2024). GAN inversion-based semi-supervised learning for medical image segmentation. Biomed. Signal Process. Control., 88.","DOI":"10.1016\/j.bspc.2023.105536"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Li, X.F., Peng, B., Hu, J., Ma, C.Y., Yang, D.P., and Xie, Z.Y. (2024). USL-Net: Uncertainty self-learning network for unsupervised skin lesion segmentation. Biomed. Signal Process. Control, 89.","DOI":"10.1016\/j.bspc.2023.105769"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"997","DOI":"10.1016\/j.bbe.2021.05.010","article-title":"An automated skin melanoma detection system with melanoma-index based on entropy features","volume":"41","author":"Cheong","year":"2021","journal-title":"Biocybern. Biomed. Eng."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"456","DOI":"10.1016\/j.bbe.2018.03.005","article-title":"Improvement in the diagnosis of melanoma and dysplastic lesions by introducing ABCD-PDT features and a hybrid classifier","volume":"38","author":"Zakeri","year":"2018","journal-title":"Biocybern. Biomed. Eng."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Hameed, N., Shabut, A., and Hossain, M.A. (2018, January 19\u201321). A Computer-aided diagnosis system for classifying prominent skin lesions using machine learning. Proceedings of the 10th Computer Science and Electronic Engineering (CEEC), Colchester, UK.","DOI":"10.1109\/CEEC.2018.8674183"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"112961","DOI":"10.1016\/j.eswa.2019.112961","article-title":"Multi-class multi-level classification algorithm for skin lesions classification using machine learning techniques","volume":"141","author":"Hameed","year":"2020","journal-title":"Expert. Syst. Appl."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"849","DOI":"10.1109\/TMI.2016.2633551","article-title":"Melanoma Classification on Dermoscopy Images Using a Neural Network Ensemble Model","volume":"36","author":"Xie","year":"2017","journal-title":"IEEE Trans. Med. Imaging"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"25517","DOI":"10.1007\/s11042-021-10858-4","article-title":"Fuzzy decision ontology for melanoma diagnosis using KNN classifier","volume":"80","author":"Abbes","year":"2021","journal-title":"Multimed. Tools Appl."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Sun, J., Yao, K., Huang, G.Y., Zhang, C.R., Leach, M., Huang, K.Z., and Yang, X. (2023). Machine Learning Methods in Skin Disease Recognition: A Systematic Review. Processes, 11.","DOI":"10.3390\/pr11041003"},{"key":"ref_35","first-page":"100036","article-title":"An enhanced technique of skin cancer classification using deep convolutional neural network with transfer learning models","volume":"5","author":"Ali","year":"2021","journal-title":"Mach. Learn. Appl."},{"key":"ref_36","first-page":"3285","article-title":"Machine learning approach in melanoma cancer stage detection","volume":"34","author":"Patil","year":"2022","journal-title":"J. King Saud Univ. Comput. Inf. Sci."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Riaz, S., Naeem, A., Malik, H., Naqvi, R.A., and Loh, W.K. (2023). Federated and Transfer Learning Methods for the Classification of Melanoma and Nonmelanoma Skin Cancers: A Prospective Study. Sensors, 23.","DOI":"10.3390\/s23208457"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Mahbod, A., Tschandl, P., Langs, G., Ecker, R., and Ellinger, I. (2020). The effects of skin lesion segmentation on the performance of dermatoscopic image classification. Comput. Methods Programs Biomed., 197.","DOI":"10.1016\/j.cmpb.2020.105725"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Dhivyaa, C.R., Sangeetha, K., Balamurugan, M., Amaran, S., Vetriselvi, T., and Johnpaul, P. (2020). Skin lesion classification using decision trees and random forest algorithms. J. Ambient. Intell. Humaniz. Comput., 1\u201313.","DOI":"10.1007\/s12652-020-02675-8"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"107922","DOI":"10.1016\/j.measurement.2020.107922","article-title":"Skin disease detection and segmentation using dynamic graph cut algorithm and classification through Naive Bayes classifier","volume":"163","author":"Balaji","year":"2020","journal-title":"Measurement"},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Batista, L.G., Bugatti, P.H., and Saito, P.T.M. (2022). Classification of Skin Lesion through Active Learning Strategies. Comput. Methods Programs Biomed., 226.","DOI":"10.1016\/j.cmpb.2022.107122"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"547","DOI":"10.1109\/JBHI.2018.2806962","article-title":"DermaKNet: Incorporating the Knowledge of Dermatologists to Convolutional Neural Networks for Skin Lesion Diagnosis","volume":"23","year":"2019","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"34885","DOI":"10.1007\/s11042-023-14892-2","article-title":"An automated multi-class skin lesion diagnosis by embedding local and global features of Dermoscopy images","volume":"82","author":"Kadirappa","year":"2023","journal-title":"Multimed. Tools Appl."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"2482","DOI":"10.1109\/TMI.2020.2972964","article-title":"A Mutual Bootstrapping Model for Automated Skin Lesion Segmentation and Classification","volume":"39","author":"Xie","year":"2020","journal-title":"IEEE Trans. Med. Imaging"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"295","DOI":"10.1016\/j.bbe.2022.02.002","article-title":"Whole kidney and renal cortex segmentation in contrast-enhanced MRI using a joint classification and segmentation convolutional neural network","volume":"42","author":"Klepaczko","year":"2022","journal-title":"Biocybern. Biomed. Eng."},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Oliveira, B., Torres, H.R., Morais, P., Veloso, F., Baptista, A.L., Fonseca, J.C., and Vila\u00e7a, J.L. (2023). A multi-task convolutional neural network for classification and segmentation of chronic venous disorders. Sci. Rep., 13.","DOI":"10.1038\/s41598-022-27089-8"},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Al-masni, M.A., and Al-Shamiri, A.K. (2023, January 10\u201312). Joint segmentation and recognition of melanoma skin lesions via multi-task learning. Proceedings of the International Conference on Green Energy, Computing and Intelligent Technology (GEn-CITy 2023), Iskandar Puteri, Malaysia.","DOI":"10.1049\/icp.2023.1799"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"106881","DOI":"10.1016\/j.asoc.2020.106881","article-title":"Cascade knowledge diffusion network for skin lesion diagnosis and segmentation","volume":"99","author":"Jin","year":"2021","journal-title":"Appl. Soft Comput."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"2912","DOI":"10.1109\/JBHI.2020.2973614","article-title":"An End-to-End Multi-Task Deep Learning Framework for Skin Lesion Analysis","volume":"24","author":"Song","year":"2020","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"120174","DOI":"10.1016\/j.eswa.2023.120174","article-title":"Joint segmentation and classification of skin lesions via a multi-task learning convolutional neural network","volume":"230","author":"He","year":"2023","journal-title":"Expert. Syst. Appl."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"108995","DOI":"10.1016\/j.patcog.2022.108995","article-title":"Decoupling multi-task causality for improved skin lesion segmentation and classification","volume":"133","author":"Song","year":"2023","journal-title":"Pattern Recogn."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/TIM.2022.3154804","article-title":"Portable microwave reflectometry system for skin sensing","volume":"71","author":"Cataldo","year":"2022","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/TIM.2020.3038491","article-title":"A microwave measuring system for detecting and localizing anomalies in metallic pipelines","volume":"70","author":"Cataldo","year":"2020","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Rana, S.P., Dey, M., Tiberi, G., Sani, L., Vispa, A., Raspa, G., Duranti, M., Ghavami, M., and Dudley, S. (2019). Machine learning approaches for automated lesion detection in microwave breast imaging clinical data. Sci. Rep., 9.","DOI":"10.1038\/s41598-019-46974-3"},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"13","DOI":"10.1038\/s44303-024-00012-8","article-title":"Emerging paradigms in microwave imaging technology for biomedical applications: Unleashing the power of artificial intelligence","volume":"2","author":"Khalid","year":"2024","journal-title":"npj Imaging"},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"114911","DOI":"10.1016\/j.measurement.2024.114911","article-title":"Integrating microwave reflectometry and deep learning imaging for in-vivo skin cancer diagnostics","volume":"235","author":"Cataldo","year":"2024","journal-title":"Measurement"},{"key":"ref_57","unstructured":"Gutman, D., Codella, N.C.F., Celebi, E., Helba, B., Marchetti, M., Mishra, N., and Halpern, A. (2016). Skin Lesion Analysis toward Melanoma Detection: A Challenge at the International Symposium on Biomedical Imaging (ISBI) 2016, hosted by the International Skin Imaging Collaboration (ISIC). arXiv."},{"key":"ref_58","doi-asserted-by":"crossref","unstructured":"Mendon\u00e7a, T., Ferreira, P.M., Marques, J., Marcal, A.R.S., and Rozeira, J. (2013, January 3\u20137). PH\u00b2\u2014A dermoscopic image database for research and benchmarking. Proceedings of the 35th International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Osaka, Japan.","DOI":"10.1109\/EMBC.2013.6610779"},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"41","DOI":"10.1023\/A:1007379606734","article-title":"Multitask Learning","volume":"28","author":"Caruana","year":"1997","journal-title":"Mach. Learn."},{"key":"ref_60","first-page":"27503","article-title":"Efficiently identifying task groupings for multi-task learning","volume":"34","author":"Fifty","year":"2021","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_61","unstructured":"Crawshaw, M. (2020). Multi-task learning with deep neural networks: A survey. arXiv."},{"key":"ref_62","doi-asserted-by":"crossref","unstructured":"Zhao, Y., Wang, X., Che, T., Bao, G., and Li, S. (2023). Multi-task deep learning for medical image computing and analysis: A review. Comput. Biol. Med., 153.","DOI":"10.1016\/j.compbiomed.2022.106496"},{"key":"ref_63","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 18th International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI), Munich, Germany.","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"ref_64","doi-asserted-by":"crossref","unstructured":"Isensee, F., Petersen, J., Klein, A., Zimmerer, D., Jaeger, P.F., Kohl, S., Wasserthal, J., Koehler, G., Norajitra, T., and Wirkert, S. (2018). nnU-Net: Self-adapting Framework for U-Net-Based Medical Image Segmentation. arXiv.","DOI":"10.1007\/978-3-658-25326-4_7"},{"key":"ref_65","unstructured":"Springenberg, J.T., Dosovitskiy, A., Brox, T., and Riedmiller, M. (2015). Striving for simplicity: The all convolutional net. arXiv."},{"key":"ref_66","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 15th Proceedings of the European Conference on Computer Vision (ECCV), Munich, Germany.","DOI":"10.1007\/978-3-030-01234-2_1"},{"key":"ref_67","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 (CVPR), Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00745"},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"119411","DOI":"10.1016\/j.neuroimage.2022.119411","article-title":"Stacked U-Nets with Self-Assisted Priors Towards Robust Correction of Rigid Motion Artifact in Brain MRI","volume":"259","author":"Lee","year":"2022","journal-title":"Neuroimage"},{"key":"ref_69","doi-asserted-by":"crossref","unstructured":"Perera, V., Chung, T., Kollar, T., and Strubell, E. (2018, January 2\u20137). Multi-Task Learning for Parsing the Alexa Meaning Representation Language. Proceedings of the 32 AAAI Conference on Artificial Intelligence, New Orleans, LA, USA.","DOI":"10.1609\/aaai.v32i1.12019"},{"key":"ref_70","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2016, January 27\u201323). Deep Residual Learning for Image Recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_71","doi-asserted-by":"crossref","unstructured":"Wang, Y., Su, J., Xu, Q.Y., and Zhong, Y.X. (2023). A Collaborative Learning Model for Skin Lesion Segmentation and Classification. Diagnostics, 13.","DOI":"10.3390\/diagnostics13050912"},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"2870","DOI":"10.1109\/JBHI.2020.2977013","article-title":"GP-CNN-DTEL: Global-Part CNN Model With Data-Transformed Ensemble Learning for Skin Lesion Classification","volume":"24","author":"Tang","year":"2020","journal-title":"IEEE J. Biomed. Health Inform."}],"container-title":["Bioengineering"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2306-5354\/11\/11\/1173\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T16:36:10Z","timestamp":1760114170000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2306-5354\/11\/11\/1173"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,11,20]]},"references-count":72,"journal-issue":{"issue":"11","published-online":{"date-parts":[[2024,11]]}},"alternative-id":["bioengineering11111173"],"URL":"https:\/\/doi.org\/10.3390\/bioengineering11111173","relation":{},"ISSN":["2306-5354"],"issn-type":[{"value":"2306-5354","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,11,20]]}}}