Segmentation with Super Images: A New 2D Perspective on 3D Medical Image Analysis. (arXiv:2205.02847v1 [eess.IV])

Web: http://arxiv.org/abs/2205.02847

Deep learning is showing an increasing number of audience in medical imaging
research. In the segmentation task of medical images, we oftentimes rely on
volumetric data, and thus require the use of 3D architectures which are praised
for their ability to capture more features from the depth dimension. Yet, these
architectures are generally more ineffective in time and compute compared to
their 2D counterpart on account of 3D convolutions, max pooling,
up-convolutions, and other operations used in these networks. Moreover, …

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