  We introduce Neural Representation of Distribution (NeRD) technique, a module
for convolutional neural networks (CNNs) that can estimate the feature
distribution by optimizing an underlying function mapping image coordinates to
the feature distribution. Using NeRD, we propose an end-to-end deep learning
model for medical image segmentation that can compensate the negative impact of
feature distribution shifting issue caused by commonly used network operations
such as padding and pooling. An implicit function is used to represent the
parameter space of the feature distribution by querying the image coordinate.
With NeRD, the impact of issues such as over-segmenting and missing have been
reduced, and experimental results on the challenging white matter lesion
segmentation and left atrial segmentation verify the effectiveness of the
proposed method. The code is available via https://github.com/tinymilky/NeRD.
