| [144202] |
| Title: SpeckleGAN: a generative adversarial network with an adaptive speckle layer to augment limited training data for ultrasound image processing. <em>International Journal of Computer Assisted Radiology and Surgery</em> |
| Written by: L. Bargsten and A. Schlaefer |
| in: <em>International Journal of Computer Assisted Radiology and Surgery</em>. Sept (2020). |
| Volume: <strong>15</strong>. Number: (9), |
| on pages: 1427-1436 |
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| DOI: 10.1007/s11548-020-02203-1 |
| URL: https://doi.org/10.1007/s11548-020-02203-1 |
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Abstract: 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 learning, but these are often not available in the case of medical image data. One way to overcome this limitation is to generate synthetic training data, e.g., by performing simulations to artificially augment the dataset. However, simulations require domain knowledge and are limited by the complexity of the underlying physical model. Another method to perform data augmentation is the generation of images by means of neural networks