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A deep learning spatial-temporal framework for detecting surgical tools in laparoscopic videos
(2021)
Harmonic Analysis for the Separation of Perfusion and Respiration in Electrical Impedance Tomography
(2021)
Comparison of Geometrical Lung Models to Calculate Tidal Volumes during Spontaneous Breathing
(2021)
Comparison of a histology based multi layer artery model to its simplified axisymmetric model
(2021)
Atomic Layer Deposition of Bioactive TiO2 Thin Films on Polyetheretherketone for Orthopedic Implants
(2021)
A unified strategy for the synthesis of amorfrutins A and B and evaluation of their cytotoxicity
(2021)
Collecting real-world data for the training of neural networks is enormously time-consuming and expensive. As such, the concept of virtualizing the domain and creating synthetic data has been analyzed in many instances. This virtualization offers many possibilities of changing the domain, and with that, enabling the relatively fast creation of data. It also offers the chance to enhance necessary augmentations with additional semantic information when compared with conventional augmentation methods. This raises the question of whether such semantic changes, which can be seen as augmentations of the virtual domain, contribute to better results for neural networks, when trained with data augmented this way. In this paper, a virtual dataset is presented, including semantic augmentations and automatically generated annotations, as well as a comparison between semantic and conventional augmentation for image data. It is determined that the results differ only marginally for neural network models trained with the two augmentation approaches.