  It is important to extract good features using an encoder to realize semantic
segmentation with high accuracy. Although loss function is optimized in
training deep neural network, far layers from the layers for computing loss
function are difficult to train. Skip connection is effective for this problem
but there are still far layers from the loss function. In this paper, we
propose the Feature Random Enhancement Module which enhances the features
randomly in only training. By emphasizing the features at far layers from loss
function, we can train those layers well and the accuracy was improved. In
experiments, we evaluated the proposed module on two kinds of cell image
datasets, and our module improved the segmentation accuracy without increasing
computational cost in test phase.
