Cargando…
SpeckleGAN: a generative adversarial network with an adaptive speckle layer to augment limited training data for ultrasound image processing
PURPOSE: In the field of medical image analysis, deep learning methods gained huge attention over the last years. This can be explained by their often improved performance compared to classic explicit algorithms. In order to work well, they need large amounts of annotated data for supervised learnin...
Autores principales: | Bargsten, Lennart, Schlaefer, Alexander |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer International Publishing
2020
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7419454/ https://www.ncbi.nlm.nih.gov/pubmed/32556953 http://dx.doi.org/10.1007/s11548-020-02203-1 |
Ejemplares similares
-
Capsule networks for segmentation of small intravascular ultrasound image datasets
por: Bargsten, Lennart, et al.
Publicado: (2021) -
Blood Flow Prediction in Multi-Exposure Speckle Contrast Imaging Using Conditional Generative Adversarial Network
por: Jain, Pankaj, et al.
Publicado: (2023) -
Propensity score synthetic augmentation matching using generative adversarial networks (PSSAM-GAN)
por: Ghosh, Shantanu, et al.
Publicado: (2021) -
Evaluation of GAN-Based Model for Adversarial Training
por: Zhao, Weimin, et al.
Publicado: (2023) -
Speckle metrology
por: Erf, R
Publicado: (1978)