- -

Augmenting data with GANs to segment melanoma skin lesions

RiuNet: Repositorio Institucional de la Universidad Politécnica de Valencia

Compartir/Enviar a

Citas

Estadísticas

  • Estadisticas de Uso

Augmenting data with GANs to segment melanoma skin lesions

Mostrar el registro completo del ítem

Pollastri, F.; Bolelli, F.; Paredes Palacios, R.; Grana, C. (2020). Augmenting data with GANs to segment melanoma skin lesions. Multimedia Tools and Applications. 79(21-22):15575-15592. https://doi.org/10.1007/s11042-019-7717-y

Por favor, use este identificador para citar o enlazar este ítem: http://hdl.handle.net/10251/168114

Ficheros en el ítem

Metadatos del ítem

Título: Augmenting data with GANs to segment melanoma skin lesions
Autor: Pollastri, Federico Bolelli, Federico Paredes Palacios, Roberto Grana, Costantino
Entidad UPV: Universitat Politècnica de València. Departamento de Sistemas Informáticos y Computación - Departament de Sistemes Informàtics i Computació
Fecha difusión:
Resumen:
[EN] This paper presents a novel strategy that employs Generative Adversarial Networks (GANs) to augment data in the skin lesion segmentation task, which is a fundamental first step in the automated melanoma detection ...[+]
Palabras clave: Deep learning , Convolutional neural networks , Adversarial learning , Skin lesion segmentation
Derechos de uso: Cerrado
Fuente:
Multimedia Tools and Applications. (issn: 1380-7501 )
DOI: 10.1007/s11042-019-7717-y
Editorial:
Springer-Verlag
Versión del editor: https://doi.org/10.1007/s11042-019-7717-y
Tipo: Artículo

References

Antoniou A, Storkey A, Edwards H (2017) Data augmentation generative adversarial networks, arXiv: 1711.04340

Baur C, Albarqouni S, Navab N (2018) MelanoGANs: high resolution skin lesion synthesis with GANs, arXiv: 1804.04338

Bengio Y, Courville A, Vincent P (2013) Representation learning: a review and new perspectives. IEEE Trans Pattern Anal Mach Intell 35(8):1798–1828 [+]
Antoniou A, Storkey A, Edwards H (2017) Data augmentation generative adversarial networks, arXiv: 1711.04340

Baur C, Albarqouni S, Navab N (2018) MelanoGANs: high resolution skin lesion synthesis with GANs, arXiv: 1804.04338

Bengio Y, Courville A, Vincent P (2013) Representation learning: a review and new perspectives. IEEE Trans Pattern Anal Mach Intell 35(8):1798–1828

Bolelli F, Baraldi L, Cancilla M, Grana C (2018) Connected components labeling on DRAGs. In: International conference on pattern recognition

Bolelli F, Cancilla M, Grana C (2017) Two more strategies to speed up connected components labeling algorithms. In: International conference on image analysis and processing. Springer, pp 48–58

Celebi ME, Wen Q, Iyatomi H, Shimizu K, Zhou H, Schaefer G (2015) A state-of-the-art survey on lesion border detection in dermoscopy images. Dermoscopy Image Analysis 10:97–129

Codella NC, Gutman D, Celebi ME, Helba B, Marchetti MA, Dusza SW, Kalloo A, Liopyris K, Mishra N, Kittler H et al (2017) 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), arXiv: 1710.05006

Denton EL, Chintala S, Fergus R et al (2015) Deep generative image models using a Laplacian pyramid of adversarial networks. In: Advances in neural information processing systems, pp 1486–1494

Goodfellow I, Pouget-Abadie J, Mirza M, Xu B, Warde-Farley D, Ozair S, Courville A, Bengio Y (2014) Generative adversarial nets. In: Advances in neural information processing systems, pp 2672–2680

Ioffe S, Szegedy C (2015) Batch normalization: accelerating deep network training by reducing internal covariate shift, arXiv: 1502.03167

Kingma DP, Ba J (2014) Adam: a method for stochastic optimization, arXiv: 1412.6980

Kittler H, Pehamberger H, Wolff K, Binder M (2002) Diagnostic accuracy of dermoscopy. Lancet Oncol 3(3):159–165

Krizhevsky A, Sutskever I, Hinton GE (2012) ImageNet classification with deep convolutional neural networks. In: Advances in neural information processing systems, pp 1097–1105

Lipkus AH (1999) A proof of the triangle inequality for the Tanimoto distance. J Math Chem 26(1):263–265. https://doi.org/10.1023/A:1019154432472

Litjens G, Kooi T, Bejnordi BE, Setio AAA, Ciompi F, Ghafoorian M, Van Der Laak JA, Van Ginneken B, Sánchez CI (2017) A survey on deep learning in medical image analysis. Med Image Anal 42:60–88

Liu Y, Nie L, Han L, Zhang L, Rosenblum DS (2015) Action2Activity: recognizing complex activities from sensor data. In: Twenty-fourth international joint conference on artificial intelligence

Liu Y, Nie L, Liu L, Rosenblum DS (2016) From action to activity: sensor-based activity recognition. Neurocomputing 181:108–115

Long J, Shelhamer E, Darrell T (2015) Fully convolutional networks for semantic segmentation. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 3431–3440

Maas AL, Hannun AY, Ng AY (2013) Rectifier nonlinearities improve neural network acoustic models. In: ICML Workshop on deep learning for audio, speech, and language processing (WDLASL 2013)

Mishkin D, Matas J (2016) All you need is a good init. In: International conference on learning representations (ICLR) 2016

Neff T, Payer C, Štern D, Urschler M (2017) Generative adversarial network based synthesis for supervised medical image segmentation. In: OAGM & ARW Joint workshop 2017 on “vision, automation & robotics”. Verlag der Technischen Universität Graz

Pollastri F, Bolelli F, Grana C (2018) Improving skin lesion segmentation with generative adversarial networks. In: 31St international symposium on computer-based medical systems

Radford A, Metz L, Chintala S (2015) Unsupervised representation learning with deep convolutional generative adversarial networks,. arXiv: 1511.06434

Ronneberger O, Fischer P, Brox T (2015) U-net: convolutional networks for biomedical image segmentation. In: International conference on medical image computing and computer-assisted intervention. Springer, pp 234–241

Springenberg JT (2015) Unsupervised and semi-supervised learning with categorical generative adversarial networks, arXiv: 1511.06390

Yuan Y, Chao M, Lo YC (2017) Automatic skin lesion segmentation with fully convolutional-deconvolutional networks, arXiv: 1703.05165

Zeiler MD, Taylor GW, Fergus R (2011) Adaptive deconvolutional networks for mid and high level feature learning. In: 2011 IEEE international conference on computer vision (ICCV). IEEE, pp 2018–2025

Zheng Z, Zheng L, Yang Y (2017) Unlabeled samples generated by GAN improve the person re-identification baseline in vitro, vol 3. arXiv: 1701.07717

[-]

recommendations

 

Este ítem aparece en la(s) siguiente(s) colección(ones)

Mostrar el registro completo del ítem