{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,3]],"date-time":"2026-06-03T15:41:14Z","timestamp":1780501274685,"version":"3.54.1"},"reference-count":113,"publisher":"Springer Science and Business Media LLC","issue":"22","license":[{"start":{"date-parts":[[2024,8,30]],"date-time":"2024-08-30T00:00:00Z","timestamp":1724976000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,8,30]],"date-time":"2024-08-30T00:00:00Z","timestamp":1724976000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Multimed Tools Appl"],"DOI":"10.1007\/s11042-024-20145-7","type":"journal-article","created":{"date-parts":[[2024,8,29]],"date-time":"2024-08-29T23:44:04Z","timestamp":1724975044000},"page":"25325-25364","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":15,"title":["Enhancing skin lesion diagnosis with data augmentation techniques: a review of the state-of-the-art"],"prefix":"10.1007","volume":"84","author":[{"given":"Aniket","family":"Patil","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2970-0345","authenticated-orcid":false,"given":"Anjula","family":"Mehto","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Saif","family":"Nalband","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,8,30]]},"reference":[{"issue":"4\/5","key":"20145_CR1","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1147\/JRD.2017.2708299","volume":"61","author":"NC Codella","year":"2017","unstructured":"Codella NC, Nguyen Q-B, Pankanti S, Gutman DA, Helba B, Halpern AC, Smith JR (2017) Deep learning ensembles for melanoma recognition in dermoscopy images. IBM J Res Dev 61(4\/5):5\u20131","journal-title":"IBM J Res Dev"},{"issue":"4","key":"20145_CR2","doi-asserted-by":"publisher","first-page":"484","DOI":"10.3390\/e22040484","volume":"22","author":"J-A Almaraz-Damian","year":"2020","unstructured":"Almaraz-Damian J-A, Ponomaryov V, Sadovnychiy S, Castillejos-Fernandez H (2020) Melanoma and nevus skin lesion classification using handcraft and deep learning feature fusion via mutual information measures. Entropy 22(4):484","journal-title":"Entropy"},{"key":"20145_CR3","doi-asserted-by":"crossref","unstructured":"Elgamal M (2013) Automatic skin cancer images classification. Int J Adv Comput Sci Appl 4(3)","DOI":"10.14569\/IJACSA.2013.040342"},{"issue":"1","key":"20145_CR4","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s40537-019-0197-0","volume":"6","author":"C Shorten","year":"2019","unstructured":"Shorten C, Khoshgoftaar TM (2019) A survey on image data augmentation for deep learning. J Big Data 6(1):1\u201348","journal-title":"J Big Data"},{"key":"20145_CR5","unstructured":"Brownlee J (2018) Better deep learning: train faster, reduce overfitting, and make better predictions. Machine Learning Mastery"},{"issue":"6","key":"20145_CR6","doi-asserted-by":"publisher","first-page":"1294","DOI":"10.1109\/TPAMI.2018.2837742","volume":"41","author":"Y Guo","year":"2018","unstructured":"Guo Y, Cai J, Jiang B, Zheng J et al (2018) Cnn-based real-time dense face reconstruction with inverse-rendered photo-realistic face images. IEEE Trans Pattern Anal Mach Intell 41(6):1294\u20131307","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"issue":"1","key":"20145_CR7","first-page":"1929","volume":"15","author":"N Srivastava","year":"2014","unstructured":"Srivastava N, Hinton G, Krizhevsky A, Sutskever I, Salakhutdinov R (2014) Dropout: a simple way to prevent neural networks from overfitting. J Mach Learn Res 15(1):1929\u20131958","journal-title":"J Mach Learn Res"},{"key":"20145_CR8","doi-asserted-by":"crossref","unstructured":"Farebrother, RW (1976) Further results on the mean square error of ridge regression. J R Stat Soc Series B Methodol 248\u2013250","DOI":"10.1111\/j.2517-6161.1976.tb01588.x"},{"issue":"10","key":"20145_CR9","doi-asserted-by":"publisher","first-page":"1348","DOI":"10.1002\/bjs.10895","volume":"105","author":"J Ranstam","year":"2018","unstructured":"Ranstam J, Cook J (2018) Lasso regression. J Brit Surg 105(10):1348\u20131348","journal-title":"J Brit Surg"},{"key":"20145_CR10","unstructured":"Simonyan K, Zisserman A (2014) Very deep convolutional networks for large-scale image recognition. arXiv:1409.1556"},{"key":"20145_CR11","doi-asserted-by":"crossref","unstructured":"He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 770\u2013778","DOI":"10.1109\/CVPR.2016.90"},{"key":"20145_CR12","doi-asserted-by":"crossref","unstructured":"Szegedy C, Vanhoucke V, Ioffe S, Shlens J, Wojna Z (2016) Rethinking the inception architecture for computer vision. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 2818\u20132826","DOI":"10.1109\/CVPR.2016.308"},{"key":"20145_CR13","doi-asserted-by":"crossref","unstructured":"Huang G, Liu Z, Van Der\u00a0Maaten L, Weinberger KQ (2017) Densely connected convolutional networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4700\u20134708","DOI":"10.1109\/CVPR.2017.243"},{"key":"20145_CR14","doi-asserted-by":"crossref","unstructured":"Bie Y, Luo L, Chen H (2024) Mica: Towards explainable skin lesion diagnosis via multi-level image-concept alignment. arXiv:2401.08527","DOI":"10.1609\/aaai.v38i2.27842"},{"key":"20145_CR15","doi-asserted-by":"publisher","first-page":"111624","DOI":"10.1016\/j.asoc.2024.111624","volume":"159","author":"KM Hosny","year":"2024","unstructured":"Hosny KM, Said W, Elmezain M, Kassem MA (2024) Explainable deep inherent learning for multi-classes skin lesion classification. Appl Soft Comput 159:111624","journal-title":"Appl Soft Comput"},{"issue":"1","key":"20145_CR16","doi-asserted-by":"publisher","first-page":"119","DOI":"10.1109\/TMI.2022.3204646","volume":"42","author":"G Yue","year":"2023","unstructured":"Yue G, Wei P, Zhou T, Jiang Q, Yan W, Wang T (2023) Toward multicenter skin lesion classification using deep neural network with adaptively weighted balance loss. IEEE Trans Med Imaging 42(1):119\u2013131. https:\/\/doi.org\/10.1109\/TMI.2022.3204646","journal-title":"IEEE Trans Med Imaging"},{"key":"20145_CR17","doi-asserted-by":"crossref","unstructured":"Nguyen V-T, Pham V-T, Tran T-T (2024) Ac-mambaseg: An adaptive convolution and mamba-based architecture for enhanced skin lesion segmentation. arXiv:2405.03011","DOI":"10.1007\/978-3-031-76197-3_2"},{"key":"20145_CR18","doi-asserted-by":"crossref","unstructured":"Mustafa S (2017) Feature selection using sequential backward method in melanoma recognition. In: 2017 13th International conference on electronics, computer and computation (ICECCO), pp 1\u20134. IEEE","DOI":"10.1109\/ICECCO.2017.8333341"},{"key":"20145_CR19","unstructured":"Matsunaga K, Hamada A, Minagawa A, Koga H (2017) Image classification of melanoma, nevus and seborrheic keratosis by deep neural network ensemble. arXiv:1703.03108"},{"key":"20145_CR20","unstructured":"D\u00edaz IG (2017) Incorporating the knowledge of dermatologists to convolutional neural networks for the diagnosis of skin lesions. arXiv:1703.01976"},{"key":"20145_CR21","doi-asserted-by":"crossref","unstructured":"Menegola A, Fornaciali M, Pires R, Bittencourt FV, Avila S, Valle E (2017) Knowledge transfer for melanoma screening with deep learning. In: 2017 IEEE 14th International symposium on biomedical imaging (ISBI 2017), pp 297\u2013300. IEEE","DOI":"10.1109\/ISBI.2017.7950523"},{"issue":"7639","key":"20145_CR22","doi-asserted-by":"publisher","first-page":"115","DOI":"10.1038\/nature21056","volume":"542","author":"A Esteva","year":"2017","unstructured":"Esteva A, Kuprel B, Novoa RA, Ko J, Swetter SM, Blau HM, Thrun S (2017) Dermatologist-level classification of skin cancer with deep neural networks. Nature 542(7639):115\u2013118","journal-title":"Nature"},{"key":"20145_CR23","doi-asserted-by":"crossref","unstructured":"Ronneberger O, Fischer P, Brox T (2015) U-net: Convolutional networks for biomedical image segmentation. In: Medical image computing and computer-assisted intervention\u2013MICCAI 2015: 18th International Conference, Munich, Germany, October 5-9, 2015, Proceedings, Part III 18. Springer, pp 234\u2013241","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"20145_CR24","doi-asserted-by":"publisher","first-page":"613981","DOI":"10.3389\/fcomp.2021.613981","volume":"3","author":"TM Quan","year":"2021","unstructured":"Quan TM, Hildebrand DGC, Jeong W-K (2021) Fusionnet: a deep fully residual convolutional neural network for image segmentation in connectomics. Front Comput Sci 3:613981","journal-title":"Front Comput Sci"},{"issue":"6","key":"20145_CR25","doi-asserted-by":"publisher","first-page":"84","DOI":"10.1145\/3065386","volume":"60","author":"A Krizhevsky","year":"2017","unstructured":"Krizhevsky A, Sutskever I, Hinton GE (2017) Imagenet classification with deep convolutional neural networks. Commun ACM 60(6):84\u201390","journal-title":"Commun ACM"},{"key":"20145_CR26","doi-asserted-by":"crossref","unstructured":"Liu H, Wang C, Peng Y (2021) Data augmentation with illumination correction in sematic segmentation. In: Journal of Physics: Conference Series, vol. 2025. IOP Publishing, p 012009","DOI":"10.1088\/1742-6596\/2025\/1\/012009"},{"key":"20145_CR27","doi-asserted-by":"crossref","unstructured":"Divon G, Tal A (2018) Viewpoint estimation\u2014insights & model. In: Proceedings of the European Conference on Computer Vision (ECCV), pp 252\u2013268","DOI":"10.1007\/978-3-030-01264-9_16"},{"key":"20145_CR28","doi-asserted-by":"crossref","unstructured":"Ning X, Nan F, Xu S, Yu L, Zhang L (2020) Multi-view frontal face image generation: a survey. Concurr Comput Pract Exp 6147","DOI":"10.1002\/cpe.6147"},{"key":"20145_CR29","doi-asserted-by":"crossref","unstructured":"Massa F, Marlet R, Aubry M (2016) Crafting a multi-task cnn for viewpoint estimation. arXiv:1609.03894","DOI":"10.5244\/C.30.91"},{"key":"20145_CR30","doi-asserted-by":"crossref","unstructured":"Zhong Z, Zheng L, Kang G, Li S, Yang Y (2020) Random erasing data augmentation. In: Proceedings of the AAAI conference on artificial intelligence, vol. 34, pp 13001\u201313008","DOI":"10.1609\/aaai.v34i07.7000"},{"issue":"10","key":"20145_CR31","doi-asserted-by":"publisher","first-page":"5479","DOI":"10.3390\/ijerph18105479","volume":"18","author":"M Dildar","year":"2021","unstructured":"Dildar M, Akram S, Irfan M, Khan HU, Ramzan M, Mahmood AR, Alsaiari SA, Saeed AHM, Alraddadi MO, Mahnashi MH (2021) Skin cancer detection: a review using deep learning techniques. Int J Environ Res Public Health 18(10):5479","journal-title":"Int J Environ Res Public Health"},{"issue":"2","key":"20145_CR32","doi-asserted-by":"publisher","first-page":"46","DOI":"10.3390\/jimaging9020046","volume":"9","author":"K Alomar","year":"2023","unstructured":"Alomar K, Aysel HI, Cai X (2023) Data augmentation in classification and segmentation: a survey and new strategies. J Imaging 9(2):46","journal-title":"J Imaging"},{"issue":"5","key":"20145_CR33","doi-asserted-by":"publisher","first-page":"545","DOI":"10.1111\/1754-9485.13261","volume":"65","author":"P Chlap","year":"2021","unstructured":"Chlap P, Min H, Vandenberg N, Dowling J, Holloway L, Haworth A (2021) A review of medical image data augmentation techniques for deep learning applications. J Med Imaging Radiat Oncol 65(5):545\u2013563","journal-title":"J Med Imaging Radiat Oncol"},{"key":"20145_CR34","doi-asserted-by":"crossref","unstructured":"Abdollahi B, Tomita N, Hassanpour S (2020) Data augmentation in training deep learning models for medical image analysis. Deep learners and deep learner descriptors for medical applications, 167\u2013180","DOI":"10.1007\/978-3-030-42750-4_6"},{"key":"20145_CR35","doi-asserted-by":"publisher","first-page":"91904","DOI":"10.1109\/ACCESS.2019.2925430","volume":"7","author":"W Lu","year":"2019","unstructured":"Lu W, Xing X, Cai B, Xu X (2019) Listwise view ranking for image cropping. IEEE Access 7:91904\u201391911","journal-title":"IEEE Access"},{"key":"20145_CR36","doi-asserted-by":"crossref","unstructured":"Ravishankar A, Anusha S, Akshatha H, Raj A, Jahnavi S, Madhura J (2017) A survey on noise reduction techniques in medical images. In: 2017 International Conference of Electronics, Communication and Aerospace Technology (ICECA), vol. 1. IEEE, pp 385\u2013389","DOI":"10.1109\/ICECA.2017.8203711"},{"key":"20145_CR37","doi-asserted-by":"crossref","unstructured":"Nazar\u00e9 TS, Costa GBP, Contato WA, Ponti M (2018) Deep convolutional neural networks and noisy images. In: Progress in pattern recognition, image analysis, computer vision, and applications: 22nd Iberoamerican Congress, CIARP 2017, Valpara\u00edso, Chile, November 7\u201310, 2017, Proceedings 22. Springer, pp 416\u2013424","DOI":"10.1007\/978-3-319-75193-1_50"},{"issue":"7","key":"20145_CR38","doi-asserted-by":"publisher","first-page":"274","DOI":"10.3390\/ijgi7070274","volume":"7","author":"S Boonprong","year":"2018","unstructured":"Boonprong S, Cao C, Chen W, Ni X, Xu M, Acharya BK (2018) The classification of noise-afflicted remotely sensed data using three machine-learning techniques: effect of different levels and types of noise on accuracy. ISPRS Int J Geo-Inf 7(7):274","journal-title":"ISPRS Int J Geo-Inf"},{"key":"20145_CR39","doi-asserted-by":"crossref","unstructured":"Boyat AK, Joshi BK (2015) A review paper: noise models in digital image processing. arXiv:1505.03489","DOI":"10.5121\/sipij.2015.6206"},{"key":"20145_CR40","unstructured":"Inoue H (2018) Data augmentation by pairing samples for images classification. arXiv:1801.02929"},{"key":"20145_CR41","doi-asserted-by":"crossref","unstructured":"Summers C, Dinneen MJ (2019) Improved mixed-example data augmentation. In: 2019 IEEE Winter conference on applications of computer vision (WACV). IEEE, pp 1262\u20131270","DOI":"10.1109\/WACV.2019.00139"},{"issue":"2","key":"20145_CR42","doi-asserted-by":"publisher","first-page":"358","DOI":"10.1109\/4.996","volume":"23","author":"N Kanopoulos","year":"1988","unstructured":"Kanopoulos N, Vasanthavada N, Baker RL (1988) Design of an image edge detection filter using the sobel operator. IEEE J Solid-State Circuits 23(2):358\u2013367","journal-title":"IEEE J Solid-State Circuits"},{"key":"20145_CR43","doi-asserted-by":"publisher","first-page":"679","DOI":"10.1109\/TPAMI.1986.4767851","volume":"6","author":"J Canny","year":"1986","unstructured":"Canny J (1986) A computational approach to edge detection. IEEE Trans Pattern Anal Mach Intell 6:679\u2013698","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"20145_CR44","doi-asserted-by":"crossref","unstructured":"Lee KW, Chin RKY (2020) The effectiveness of data augmentation for melanoma skin cancer prediction using convolutional neural networks. In: 2020 IEEE 2nd International conference on artificial intelligence in engineering and technology (IICAIET). IEEE, pp 1\u20136","DOI":"10.1109\/IICAIET49801.2020.9257859"},{"key":"20145_CR45","doi-asserted-by":"crossref","unstructured":"Combalia M, Hueto F, Puig S, Malvehy J, Vilaplana V (2020) Uncertainty estimation in deep neural networks for dermoscopic image classification. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition Workshops, pp 744\u2013745","DOI":"10.1109\/CVPRW50498.2020.00380"},{"key":"20145_CR46","unstructured":"Tan M, Le Q (2019) Efficientnet: Rethinking model scaling for convolutional neural networks. In: International conference on machine learning. PMLR, pp 6105\u20136114"},{"key":"20145_CR47","unstructured":"Gal Y, Ghahramani Z (2016) Dropout as a bayesian approximation: Representing model uncertainty in deep learning. In: International conference on machine learning, PMLR, pp 1050\u20131059"},{"key":"20145_CR48","doi-asserted-by":"publisher","first-page":"34","DOI":"10.1016\/j.neucom.2019.01.103","volume":"338","author":"G Wang","year":"2019","unstructured":"Wang G, Li W, Aertsen M, Deprest J, Ourselin S, Vercauteren T (2019) Aleatoric uncertainty estimation with test-time augmentation for medical image segmentation with convolutional neural networks. Neurocomputing 338:34\u201345","journal-title":"Neurocomputing"},{"key":"20145_CR49","unstructured":"Ayhan MS, Berens P (2022) Test-time data augmentation for estimation of heteroscedastic aleatoric uncertainty in deep neural networks. In: Medical imaging with deep learning"},{"key":"20145_CR50","doi-asserted-by":"crossref","unstructured":"Jiang Y, Huang R, Shi J, et al (2021) Efficientnet-based model with test time augmentation for cancer detection. In: 2021 IEEE 2nd International conference on big data, artificial intelligence and internet of things engineering (ICBAIE). IEEE, pp 548\u2013551","DOI":"10.1109\/ICBAIE52039.2021.9389825"},{"key":"20145_CR51","unstructured":"Le DN, Le HX, Ngo LT, Ngo HT (2020) Transfer learning with class-weighted and focal loss function for automatic skin cancer classification. arXiv:2009.05977"},{"key":"20145_CR52","doi-asserted-by":"publisher","first-page":"40536","DOI":"10.1109\/ACCESS.2020.2976045","volume":"8","author":"TA Putra","year":"2020","unstructured":"Putra TA, Rufaida SI, Leu J-S (2020) Enhanced skin condition prediction through machine learning using dynamic training and testing augmentation. IEEE Access 8:40536\u201340546","journal-title":"IEEE Access"},{"key":"20145_CR53","unstructured":"Perez F, Vasconcelos C, Avila S, Valle E (2018) Data augmentation for skin lesion analysis. In: OR 2.0 Context-aware operating theaters, computer assisted robotic endoscopy, clinical image-based procedures, and skin image analysis: first international workshop, OR 2.0 2018, 5th International Workshop, CARE 2018, 7th International Workshop, CLIP 2018, Third International Workshop, ISIC 2018, Held in Conjunction with MICCAI 2018, Granada, Spain, September 16 and 20, 2018, Proceedings 5. Springer, pp 303\u2013311"},{"key":"20145_CR54","doi-asserted-by":"publisher","first-page":"123056","DOI":"10.1016\/j.eswa.2023.123056","volume":"246","author":"R Sulthana","year":"2024","unstructured":"Sulthana R, Chamola V, Hussain Z, Albalwy F, Hussain A (2024) A novel end-to-end deep convolutional neural network based skin lesion classification framework. Expert Syst Appl 246:123056","journal-title":"Expert Syst Appl"},{"issue":"1","key":"20145_CR55","doi-asserted-by":"publisher","first-page":"180","DOI":"10.1007\/s12672-024-01043-8","volume":"15","author":"W Yuan","year":"2024","unstructured":"Yuan W, Du Z, Han S (2024) Semi-supervised skin cancer diagnosis based on self-feedback threshold focal learning. Discov Oncol 15(1):180","journal-title":"Discov Oncol"},{"key":"20145_CR56","doi-asserted-by":"crossref","unstructured":"Kuo S-J, Huang P-H, Lin C-C, Li J-L, Chang M-C (2024) Improving limited supervised foot ulcer segmentation using cross-domain augmentation. arXiv:2401.08422","DOI":"10.1109\/ICASSP48485.2024.10446498"},{"key":"20145_CR57","doi-asserted-by":"publisher","first-page":"107877","DOI":"10.1016\/j.compbiomed.2023.107877","volume":"169","author":"Z Xu","year":"2024","unstructured":"Xu Z, Wang S, Xu G, Liu Y, Yu M, Zhang H, Lukasiewicz T, Gu J (2024) Automatic data augmentation for medical image segmentation using adaptive sequence-length based deep reinforcement learning. Comput Biol Med 169:107877","journal-title":"Comput Biol Med"},{"key":"20145_CR58","doi-asserted-by":"publisher","first-page":"101430","DOI":"10.1016\/j.imu.2023.101430","volume":"44","author":"S Chamarthi","year":"2024","unstructured":"Chamarthi S, Fogelberg K, Brinker TJ, Niebling J (2024) Mitigating the influence of domain shift in skin lesion classification: a benchmark study of unsupervised domain adaptation methods. Inform Med Unlocked 44:101430","journal-title":"Inform Med Unlocked"},{"key":"20145_CR59","unstructured":"Chen X, Wang S, Long M, Wang, J (2019) Transferability vs. discriminability: Batch spectral penalization for adversarial domain adaptation. In: International conference on machine learning. PMLR, pp 1081\u20131090"},{"key":"20145_CR60","doi-asserted-by":"crossref","unstructured":"Tzeng E, Hoffman J, Saenko K, Darrell T (2017) Adversarial discriminative domain adaptation. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 7167\u20137176","DOI":"10.1109\/CVPR.2017.316"},{"key":"20145_CR61","unstructured":"Ganin Y, Lempitsky V (2015) Unsupervised domain adaptation by backpropagation. In: International conference on machine learning, PMLR. pp 1180\u20131189"},{"issue":"11","key":"20145_CR62","doi-asserted-by":"publisher","first-page":"4037","DOI":"10.1109\/TPAMI.2020.2992393","volume":"43","author":"L Jing","year":"2020","unstructured":"Jing L, Tian Y (2020) Self-supervised visual feature learning with deep neural networks: a survey. IEEE Trans Pattern Anal Mach Intell 43(11):4037\u20134058","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"issue":"4","key":"20145_CR63","doi-asserted-by":"publisher","first-page":"237","DOI":"10.4103\/jmss.JMSS_53_20","volume":"11","author":"F Mutepfe","year":"2021","unstructured":"Mutepfe F, Kalejahi BK, Meshgini S, Danishvar S (2021) Generative adversarial network image synthesis method for skin lesion generation and classification. J Med Signals Sens 11(4):237","journal-title":"J Med Signals Sens"},{"key":"20145_CR64","unstructured":"Goodfellow IJ, Pouget-Abadie J, Mirza M, Xu B, Warde-Farley D, Ozair S, Courville A, Bengio Y (2014) Generative adversarial networks"},{"key":"20145_CR65","doi-asserted-by":"publisher","first-page":"100628","DOI":"10.1016\/j.imu.2021.100628","volume":"24","author":"A Shahsavari","year":"2021","unstructured":"Shahsavari A, Ranjbari S, Khatibi T (2021) Proposing a novel cascade ensemble super resolution generative adversarial network (cesr-gan) method for the reconstruction of super-resolution skin lesion images. Inform Med Unlocked 24:100628","journal-title":"Inform Med Unlocked"},{"key":"20145_CR66","doi-asserted-by":"publisher","first-page":"106019","DOI":"10.1016\/j.cmpb.2021.106019","volume":"202","author":"T Shen","year":"2021","unstructured":"Shen T, Hao K, Gou C, Wang F-Y (2021) Mass image synthesis in mammogram with contextual information based on gans. Comput Methods Programs Biomed 202:106019","journal-title":"Comput Methods Programs Biomed"},{"key":"20145_CR67","first-page":"100040","volume":"5","author":"RF Blanco","year":"2021","unstructured":"Blanco RF, Rosado P, Vegas E, Reverter F (2021) Medical image editing in the latent space of generative adversarial networks. Intell-Based Med 5:100040","journal-title":"Intell-Based Med"},{"key":"20145_CR68","doi-asserted-by":"publisher","first-page":"102901","DOI":"10.1016\/j.bspc.2021.102901","volume":"69","author":"J Zhang","year":"2021","unstructured":"Zhang J, Yu L, Chen D, Pan W, Shi C, Niu Y, Yao X, Xu X, Cheng Y (2021) Dense gan and multi-layer attention based lesion segmentation method for covid-19 ct images. Biomed Signal Process Control 69:102901","journal-title":"Biomed Signal Process Control"},{"key":"20145_CR69","doi-asserted-by":"publisher","first-page":"106018","DOI":"10.1016\/j.cmpb.2021.106018","volume":"203","author":"T Pang","year":"2021","unstructured":"Pang T, Wong JHD, Ng WL, Chan CS (2021) Semi-supervised gan-based radiomics model for data augmentation in breast ultrasound mass classification. Comput Methods Programs Biomed 203:106018","journal-title":"Comput Methods Programs Biomed"},{"key":"20145_CR70","doi-asserted-by":"crossref","unstructured":"Isola P, Zhu J-Y, Zhou T, Efros AA (2017) Image-to-image translation with conditional adversarial networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 1125\u20131134","DOI":"10.1109\/CVPR.2017.632"},{"key":"20145_CR71","doi-asserted-by":"crossref","unstructured":"Wang T-C, Liu M-Y, Zhu J-Y, Tao A, Kautz J, Catanzaro B (2018) High-resolution image synthesis and semantic manipulation with conditional gans. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 8798\u20138807","DOI":"10.1109\/CVPR.2018.00917"},{"key":"20145_CR72","doi-asserted-by":"crossref","unstructured":"Park T, Liu M-Y, Wang T-C, Zhu J-Y (2019) Semantic image synthesis with spatially-adaptive normalization. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 2337\u20132346","DOI":"10.1109\/CVPR.2019.00244"},{"key":"20145_CR73","doi-asserted-by":"crossref","unstructured":"Ren Z, Guo Y, Stella XY, Whitney D (2021) Improve image-based skin cancer diagnosis with generative self-supervised learning. In: 2021 IEEE\/ACM Conference on connected health: applications, systems and engineering technologies (CHASE). IEEE, pp 23\u201334","DOI":"10.1109\/CHASE52844.2021.00011"},{"key":"20145_CR74","unstructured":"Snell J, Swersky K, Zemel R (2017) Prototypical networks for few-shot learning. Adv Neural Inf Process Syst 30"},{"key":"20145_CR75","doi-asserted-by":"crossref","unstructured":"Sung F, Yang Y, Zhang L, Xiang T, Torr PH, Hospedales TM (2018) Learning to compare: relation network for few-shot learning. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 1199\u20131208","DOI":"10.1109\/CVPR.2018.00131"},{"key":"20145_CR76","doi-asserted-by":"crossref","unstructured":"Sun Q, Liu Y, Chua T-S, Schiele B (2019) Meta-transfer learning for few-shot learning. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 403\u2013412","DOI":"10.1109\/CVPR.2019.00049"},{"key":"20145_CR77","unstructured":"Bissoto A, Perez F, Valle E, Avila S (2018) Skin lesion synthesis with generative adversarial networks. In: OR 2.0 Context-aware operating theaters, computer assisted robotic endoscopy, clinical image-based procedures, and skin image analysis: first international workshop, OR 2.0 2018, 5th International Workshop, CARE 2018, 7th International Workshop, CLIP 2018, Third International Workshop, ISIC 2018, Held in Conjunction with MICCAI 2018, Granada, Spain, September 16 and 20, 2018, Proceedings 5. Springer, pp 294\u2013302"},{"key":"20145_CR78","unstructured":"Ghorbani A, Natarajan V, Coz D, Liu Y (2020) Dermgan: Synthetic generation of clinical skin images with pathology. In: Machine learning for health workshop. PMLR, pp 155\u2013170"},{"key":"20145_CR79","doi-asserted-by":"crossref","unstructured":"Bisla D, Choromanska A, Berman RS, Stein JA, Polsky D (2019) Towards automated melanoma detection with deep learning: Data purification and augmentation. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition workshops, pp 0\u20130","DOI":"10.1109\/CVPRW.2019.00330"},{"key":"20145_CR80","doi-asserted-by":"crossref","unstructured":"Bissoto A, Valle E, Avila S (2021) Gan-based data augmentation and anonymization for skin-lesion analysis: A critical review. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 1847\u20131856","DOI":"10.1109\/CVPRW53098.2021.00204"},{"key":"20145_CR81","doi-asserted-by":"publisher","first-page":"15575","DOI":"10.1007\/s11042-019-7717-y","volume":"79","author":"F Pollastri","year":"2020","unstructured":"Pollastri F, Bolelli F, Paredes R, Grana C (2020) Augmenting data with gans to segment melanoma skin lesions. Multimed Tools Appl 79:15575\u201315592","journal-title":"Multimed Tools Appl"},{"key":"20145_CR82","unstructured":"Radford A, Metz L, Chintala S (2015) Unsupervised representation learning with deep convolutional generative adversarial networks. arXiv:1511.06434"},{"key":"20145_CR83","doi-asserted-by":"crossref","unstructured":"Yuan Y (2017) Automatic skin lesion segmentation with fully convolutional-deconvolutional networks. arXiv:1703.05165","DOI":"10.1109\/TMI.2017.2695227"},{"issue":"11","key":"20145_CR84","doi-asserted-by":"publisher","first-page":"2147","DOI":"10.3390\/diagnostics11112147","volume":"11","author":"B Ahmad","year":"2021","unstructured":"Ahmad B, Jun S, Palade V, You Q, Mao L, Zhongjie M (2021) Improving skin cancer classification using heavy-tailed student t-distribution in generative adversarial networks (ted-gan). Diagnostics 11(11):2147","journal-title":"Diagnostics"},{"key":"20145_CR85","doi-asserted-by":"publisher","first-page":"113922","DOI":"10.1016\/j.eswa.2020.113922","volume":"165","author":"ISA Abdelhalim","year":"2021","unstructured":"Abdelhalim ISA, Mohamed MF, Mahdy YB (2021) Data augmentation for skin lesion using self-attention based progressive generative adversarial network. Expert Syst Appl 165:113922","journal-title":"Expert Syst Appl"},{"key":"20145_CR86","doi-asserted-by":"publisher","first-page":"103882","DOI":"10.1016\/j.cviu.2023.103882","volume":"238","author":"M Pennisi","year":"2024","unstructured":"Pennisi M, Salanitri FP, Bellitto G, Casella B, Aldinucci M, Palazzo S, Spampinato C (2024) Feder: Federated learning through experience replay and privacy-preserving data synthesis. Comput Vis Image Underst 238:103882","journal-title":"Comput Vis Image Underst"},{"issue":"2","key":"20145_CR87","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3298981","volume":"10","author":"Q Yang","year":"2019","unstructured":"Yang Q, Liu Y, Chen T, Tong Y (2019) Federated machine learning: concept and applications. ACM Trans Intell Syst Technol (TIST) 10(2):1\u201319","journal-title":"ACM Trans Intell Syst Technol (TIST)"},{"issue":"2","key":"20145_CR88","doi-asserted-by":"publisher","first-page":"285","DOI":"10.1037\/0033-295X.97.2.285","volume":"97","author":"R Ratcliff","year":"1990","unstructured":"Ratcliff R (1990) Connectionist models of recognition memory: constraints imposed by learning and forgetting functions. Psychol Rev 97(2):285","journal-title":"Psychol Rev"},{"issue":"2","key":"20145_CR89","doi-asserted-by":"publisher","first-page":"123","DOI":"10.1080\/09540099550039318","volume":"7","author":"A Robins","year":"1995","unstructured":"Robins A (1995) Catastrophic forgetting, rehearsal and pseudorehearsal. Connection Sci 7(2):123\u2013146","journal-title":"Connection Sci"},{"key":"20145_CR90","unstructured":"Rolnick D, Ahuja A, Schwarz J, Lillicrap T, Wayne G (2019) Experience replay for continual learning. Adv Neural Inf Process Syst 32"},{"key":"20145_CR91","first-page":"15920","volume":"33","author":"P Buzzega","year":"2020","unstructured":"Buzzega P, Boschini M, Porrello A, Abati D, Calderara S (2020) Dark experience for general continual learning: a strong, simple baseline. Adv Neural Inf Process Syst 33:15920\u201315930","journal-title":"Adv Neural Inf Process Syst"},{"key":"20145_CR92","unstructured":"Mirza M, Osindero S (2014) Conditional generative adversarial nets. arXiv:1411.1784"},{"key":"20145_CR93","doi-asserted-by":"crossref","unstructured":"Karras T, Laine S, Aittala M, Hellsten J, Lehtinen J, Aila T (2020) Analyzing and improving the image quality of stylegan. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 8110\u20138119","DOI":"10.1109\/CVPR42600.2020.00813"},{"key":"20145_CR94","unstructured":"Hou L, Cao Q, Yuan Y, Zhao S, Ma C, Pan S, Wan P, Wang Z, Shen H, Cheng X (2024) Augmentation-aware self-supervision for data-efficient gan training. Adv Neural Inf Process Syst 36"},{"key":"20145_CR95","doi-asserted-by":"crossref","unstructured":"Farooq MA, Yao W, Schukat M, Little MA, Corcoran P (2024) Derm-t2im: Harnessing synthetic skin lesion data via stable diffusion models for enhanced skin disease classification using vit and cnn. arXiv:2401.05159","DOI":"10.1109\/EMBC53108.2024.10781852"},{"issue":"6","key":"20145_CR96","doi-asserted-by":"publisher","first-page":"362","DOI":"10.1016\/j.compmedimag.2007.01.003","volume":"31","author":"ME Celebi","year":"2007","unstructured":"Celebi ME, Kingravi HA, Uddin B, Iyatomi H, Aslandogan YA, Stoecker WV, Moss RH (2007) A methodological approach to the classification of dermoscopy images. Comput Med Imaging Graph 31(6):362\u2013373","journal-title":"Comput Med Imaging Graph"},{"issue":"16","key":"20145_CR97","doi-asserted-by":"publisher","first-page":"2187","DOI":"10.1016\/j.patrec.2011.06.015","volume":"32","author":"G Capdehourat","year":"2011","unstructured":"Capdehourat G, Corez A, Bazzano A, Alonso R, Mus\u00e9 P (2011) Toward a combined tool to assist dermatologists in melanoma detection from dermoscopic images of pigmented skin lesions. Pattern Recognit Lett 32(16):2187\u20132196","journal-title":"Pattern Recognit Lett"},{"issue":"7","key":"20145_CR98","doi-asserted-by":"publisher","first-page":"1747","DOI":"10.3390\/diagnostics12071747","volume":"12","author":"C-C Chang","year":"2022","unstructured":"Chang C-C, Li Y-Z, Wu H-C, Tseng M-H (2022) Melanoma detection using xgb classifier combined with feature extraction and k-means smote techniques. Diagnostics 12(7):1747","journal-title":"Diagnostics"},{"issue":"7","key":"20145_CR99","doi-asserted-by":"publisher","first-page":"2179","DOI":"10.3390\/cancers15072179","volume":"15","author":"M Tahir","year":"2023","unstructured":"Tahir M, Naeem A, Malik H, Tanveer J, Naqvi RA, Lee S-W (2023) Dscc_net: Multi-classification deep learning models for diagnosing of skin cancer using dermoscopic images. Cancers 15(7):2179","journal-title":"Cancers"},{"issue":"6","key":"20145_CR100","doi-asserted-by":"publisher","first-page":"271","DOI":"10.1016\/S2589-7500(19)30123-2","volume":"1","author":"X Liu","year":"2019","unstructured":"Liu X, Faes L, Kale AU, Wagner SK, Fu DJ, Bruynseels A, Mahendiran T, Moraes G, Shamdas M, Kern C et al (2019) A comparison of deep learning performance against health-care professionals in detecting diseases from medical imaging: a systematic review and meta-analysis. Lancet Digit Health 1(6):271\u2013297","journal-title":"Lancet Digit Health"},{"key":"20145_CR101","unstructured":"Krizhevsky A, Sutskever I, Hinton GE (2012) Imagenet classification with deep convolutional neural networks. Adv Neural Inf Process Syst 25"},{"key":"20145_CR102","unstructured":"Marcus G, Davis E (2019) Rebooting AI: building artificial intelligence we can trust. Vintage"},{"issue":"1","key":"20145_CR103","doi-asserted-by":"publisher","first-page":"3358","DOI":"10.1038\/s41598-019-40041-7","volume":"9","author":"JW Wei","year":"2019","unstructured":"Wei JW, Tafe LJ, Linnik YA, Vaickus LJ, Tomita N, Hassanpour S (2019) Pathologist-level classification of histologic patterns on resected lung adenocarcinoma slides with deep neural networks. Sci Rep 9(1):3358","journal-title":"Sci Rep"},{"key":"20145_CR104","unstructured":"Yosinski J, Clune J, Nguyen A, Fuchs T, Lipson H (2015) Understanding neural networks through deep visualization. arXiv:1506.06579"},{"key":"20145_CR105","doi-asserted-by":"crossref","unstructured":"Nasr-Esfahani E, Samavi S, Karimi N, Soroushmehr SMR, Jafari MH, Ward K, Najarian K (2016) Melanoma detection by analysis of clinical images using convolutional neural network. In: 2016 38th Annual international conference of the IEEE engineering in medicine and biology society (EMBC). IEEE, pp 1373\u20131376","DOI":"10.1109\/EMBC.2016.7590963"},{"key":"20145_CR106","unstructured":"Saad\u00a0Ali I, Farouk\u00a0Mohamed M, Bassyouni\u00a0Mahdy Y (2019) Data augmentation for skin lesion using self-attention based progressive generative adversarial network. arXiv e-prints, 1910"},{"key":"20145_CR107","doi-asserted-by":"crossref","unstructured":"Freeman K, Dinnes J, Chuchu N, Takwoingi Y, Bayliss SE, Matin RN, Jain A, Walter FM, Williams HC, Deeks JJ (2020) Algorithm based smartphone apps to assess risk of skin cancer in adults: systematic review of diagnostic accuracy studies. bmj. 368","DOI":"10.1136\/bmj.m127"},{"key":"20145_CR108","doi-asserted-by":"publisher","first-page":"8659","DOI":"10.1109\/ACCESS.2021.3049600","volume":"9","author":"C Zhao","year":"2021","unstructured":"Zhao C, Shuai R, Ma L, Liu W, Hu D, Wu M (2021) Dermoscopy image classification based on stylegan and densenet201. Ieee Access 9:8659\u20138679","journal-title":"Ieee Access"},{"key":"20145_CR109","doi-asserted-by":"crossref","unstructured":"Shanmugam D, Blalock D, Balakrishnan G, Guttag J (2021) Better aggregation in test-time augmentation. In: Proceedings of the IEEE\/CVF international conference on computer vision, pp 1214\u20131223","DOI":"10.1109\/ICCV48922.2021.00125"},{"key":"20145_CR110","unstructured":"Goodfellow I (2016) Nips 2016 tutorial: Generative adversarial networks. arXiv:1701.00160"},{"key":"20145_CR111","doi-asserted-by":"crossref","unstructured":"He T, Han P, Duan S, Wang Z, Wu W, Liu C, Han J (2024) Generative data augmentation with differential privacy for non-iid problem in decentralized clinical machine learning. Future Gener Comput Syst","DOI":"10.1016\/j.future.2024.05.048"},{"issue":"2","key":"20145_CR112","doi-asserted-by":"publisher","first-page":"23049","DOI":"10.1002\/ima.23049","volume":"34","author":"L Liu","year":"2024","unstructured":"Liu L, Zhang X, Xu Z (2024) An adaptive weight search method based on the grey wolf optimizer algorithm for skin lesion ensemble classification. Int J Imaging Syst Technol 34(2):23049","journal-title":"Int J Imaging Syst Technol"},{"key":"20145_CR113","unstructured":"Haggerty H, Chandra R (2024) Self-supervised learning for skin cancer diagnosis with limited training data. arXiv:2401.00692"}],"container-title":["Multimedia Tools and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-024-20145-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11042-024-20145-7\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-024-20145-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,6]],"date-time":"2025-09-06T00:27:40Z","timestamp":1757118460000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11042-024-20145-7"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,8,30]]},"references-count":113,"journal-issue":{"issue":"22","published-online":{"date-parts":[[2025,7]]}},"alternative-id":["20145"],"URL":"https:\/\/doi.org\/10.1007\/s11042-024-20145-7","relation":{},"ISSN":["1573-7721"],"issn-type":[{"value":"1573-7721","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,8,30]]},"assertion":[{"value":"26 August 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"10 July 2024","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"18 August 2024","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"30 August 2024","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare that they have no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of Interest"}},{"value":"Not applicable","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical Approval"}}]}}