A Novel Downsampling Strategy Based on Information Complementarity for Medical Image Segmentation

TL;DR

This paper introduces Hybrid Pooling Downsampling (HPD), a novel method that improves medical image segmentation by better preserving spatial details through information complementarity, outperforming traditional downsampling techniques.

Contribution

The study proposes a new downsampling approach based on MinMaxPooling that retains more image contrast and details, enhancing segmentation accuracy in CNNs.

Findings

HPD outperforms traditional methods in segmentation accuracy.

DSC coefficient increases by 0.5% on average with HPD.

HPD provides an efficient solution for semantic segmentation.

Abstract

In convolutional neural networks (CNNs), downsampling operations are crucial to model performance. Although traditional downsampling methods (such as maximum pooling and cross-row convolution) perform well in feature aggregation, receptive field expansion, and computational reduction, they may lead to the loss of key spatial information in semantic segmentation tasks, thereby affecting the pixel-by-pixel prediction accuracy.To this end, this study proposes a downsampling method based on information complementarity - Hybrid Pooling Downsampling (HPD). The core is to replace the traditional method with MinMaxPooling, and effectively retain the light and dark contrast and detail features of the image by extracting the maximum value information of the local area.Experiment on various CNN architectures on the ACDC and Synapse datasets show that HPD outperforms traditional methods in segmentation performance, and increases the DSC coefficient by 0.5% on average. The results show that the HPD module provides an efficient solution for semantic segmentation tasks.