MNet: Rethinking 2D/3D Networks for Anisotropic Medical Image Segmentation. (arXiv:2205.04846v1 [eess.IV])

The nature of thick-slice scanning causes severe inter-slice discontinuities
of 3D medical images, and the vanilla 2D/3D convolutional neural networks
(CNNs) fail to represent sparse inter-slice information and dense intra-slice
information in a balanced way, leading to severe underfitting to inter-slice
features (for vanilla 2D CNNs) and overfitting to noise from long-range slices
(for vanilla 3D CNNs). In this work, a novel mesh network (MNet) is proposed to
balance the spatial representation inter axes via learning. 1) Our MNet
latently …

3d arxiv image medical networks segmentation