Importance of localized dilatation and distensibility in identifying determinants of thoracic aortic aneurysm with neural operators

doi:10.1371/journal.pcbi.1013550

Importance of localized dilatation and distensibility in identifying determinants of thoracic aortic aneurysm with neural operators

Fig 3

Schematic representation of the UNet architecture.

Maps of dilatation (d) and distensibility () are encoded via successive layers of two-dimensional convolution (Conv2D), group normalization, and Gaussian Error Linear Unit (GELU) activation. Down- and up-sampling the input by factors of 2 is achieved through two-dimensional max-pooling operations (Maxpool2D) and two-dimensional transpose convolutional operations (Conv2DTranspose), respectively. Finally, skip connections are implemented to propagate information from earlier layers.

doi: https://doi.org/10.1371/journal.pcbi.1013550.g003