CCE-Net: A Lightweight Context Contrast Enhancement Network and Its Application in Medical Image Segmentation.

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Abstract

Efficient and accurate image segmentation models play a vital role in medical image segmentation, however, high computational cost of traditional models limits clinical deployment. Based on pyramid visual transformers and convolutional neural networks, this paper proposes a lightweight Context Contrast Enhancement Network (CCE-Net) that ensures efficient inference and achieves accurate segmentation through the contextual feature synergy mechanism and feature contrast enhancement strategy. The Local Context Fusion Enhancement module is designed to obtain more specific local detail information through cross-layer context fusion and bridge the semantic gap between the encoder and decoder. The Deep Feature Multi-scale Extraction module is proposed to fully extract the comprehensive information about the deepest features in the bottleneck layer of the model and provide more accurate global contextual features for the decoder. The Detail Contrast Enhancement Decoder module is designed to effectively solve the inherent problems of missing image details and blurred edges through adaptive dual-branch feature fusion and frequency-domain contrast enhancement operations. Experiments show that CCE-Net only requires 5.40M parameters and 0.80G FLOPs, and the average Dice coefficients on the Synapse and ACDC datasets are 82.25% and 91.88%, respectively, which are 37%-62% less than the parameters of mainstream models, promoting the transformation of lightweight medical AI models from laboratory research to clinical practice.

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