  Existing studies tend tofocus onmodel modifications and integration with
higher accuracy, which improve performance but also carry huge computational
costs, resulting in longer detection times. Inmedical imaging, the use of time
is extremely sensitive. And at present most of the semantic segmentation models
have encoder-decoder structure or double branch structure. Their several times
of the pooling use with high-level semantic information extraction operation
cause information loss although there si a reverse pooling or other similar
action to restore information loss of pooling operation. In addition, we notice
that visual attention mechanism has superior performance on a variety of tasks.
Given this, this paper proposes non-pooling network(NPNet), non-pooling
commendably reduces the loss of information and attention enhancement m o d u l
e ( A M ) effectively increases the weight of useful information. The method
greatly reduces the number of parametersand computation costs by the shallow
neural network structure. We evaluate the semantic segmentation model of our
NPNet on three benchmark datasets comparing w i t h multiple current
state-of-the-art(SOTA) models, and the implementation results show thatour
NPNetachieves SOTA performance, with an excellent balance between accuracyand
speed.
