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• W3203200434 abstract "Medical Ultrasound (US), despite its wide use, is characterized by artefacts 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 unrealistic US images. To this end, we propose a set of physics-inspired transformations, including deformation, reverb and signal-to-noise ratio, that we apply on US B-mode images for data augmentation. We evaluate our method on a new spine US dataset for the tasks of bone segmentation and classification." @default.
• W3203200434 created "2021-10-11" @default.
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• W3203200434 date "2021-01-01" @default.
• W3203200434 modified "2023-10-03" @default.
• W3203200434 title "Rethinking Ultrasound Augmentation: A Physics-Inspired Approach" @default.
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• W3203200434 doi "https://doi.org/10.1007/978-3-030-87237-3_66" @default.
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