  A large portion of volumetric medical data, especially magnetic resonance
imaging (MRI) data, is anisotropic, as the through-plane resolution is
typically much lower than the in-plane resolution. Both 3D and purely 2D deep
learning-based segmentation methods are deficient in dealing with such
volumetric data since the performance of 3D methods suffers when confronting
anisotropic data, and 2D methods disregard crucial volumetric information.
Insufficient work has been done on 2.5D methods, in which 2D convolution is
mainly used in concert with volumetric information. These models focus on
learning the relationship across slices, but typically have many parameters to
train. We offer a Cross-Slice Attention Module (CSAM) with minimal trainable
parameters, which captures information across all the slices in the volume by
applying semantic, positional, and slice attention on deep feature maps at
different scales. Our extensive experiments using different network
architectures and tasks demonstrate the usefulness and generalizability of
CSAM. Associated code is available at https://github.com/aL3x-O-o-Hung/CSAM.
