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Rethinking Ultrasound Augmentation: A Physics-Inspired Approach
[article]
2021
arXiv
pre-print
Medical Ultrasound (US), despite its wide use, is characterized by artifacts and operator dependency. Those attributes hinder the gathering and utilization of US datasets for the training of Deep Neural Networks used for Computer-Assisted Intervention Systems. Data augmentation is commonly used to enhance model generalization and performance. However, common data augmentation techniques, such as affine transformations do not align with the physics of US and, when used carelessly can lead to
arXiv:2105.02188v1
fatcat:v4f4uoomavbgrnuq5mwnoktlmi