Non-pooling Network for medical image segmentation

TL;DR

This paper introduces NPNet, a non-pooling neural network for medical image segmentation that reduces information loss and computational costs while achieving state-of-the-art accuracy and speed.

Contribution

NPNet is a novel non-pooling architecture with attention enhancement that minimizes information loss and computational complexity in medical image segmentation.

Findings

Achieves state-of-the-art performance on three benchmark datasets.

Reduces parameters and computation costs significantly.

Balances accuracy and speed effectively.

Abstract

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.