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<p>Convolutional Neural Networks (CNNs) have gained significant attention for medical image segmentation due to their outstanding performance, becoming the standard approach for these tasks. However, CNNs are limited in capturing long-range dependencies and spatial correlations due to the nature of convolutional operations. Although Transformers were designed to address these issues, they struggle to effectively capture low-level features. Multi-scale representations, which account for both fine-grained object details and broader context, have proven to be effective. Motivated by this, we propose a multi-scale CNN-Transformer parallel fusion network. The CNN branch employs a standard encoder-decoder structure to extract local features, while the Transformer branch leverages a pre-trained PVTv2-B3 to capture global features. To improve the CNN branch, we introduce the Linear Space Deep Aggregation Module (LSDAM) to replace traditional skip connections. For the Transformer, we introduce a multi-level Progressive Decoder, eliminating skip connections between the encoder and decoder, and instead use an Intermediate Decoder module for improved feature extraction. The Adaptive Residual Weighted Fuser (ARWF) is employed to fuse features from both branches, compensating for the CNN’s limited global feature extraction and the Transformer’s inability to capture local features. A Final Decoder is added to enhance the model’s decision-making capability and further improve segmentation performance. Rigorous experiments on three publicly available datasets demonstrate that the HCT-Net model significantly outperforms state-of-the-art methods, achieving superior segmentation accuracy.</p>

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HCT-Net: hybrid CNN-transformer network with multi-scale feature aggregation and progressive decode for medical image segmentation

  • 期刊论文

  • 发表日期：

    2026年2月2日

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  • Shuo Wang,
  • XiaoYang Zhang,
  • Lu Ren,
  • Jinjiang Li

摘要

Convolutional Neural Networks (CNNs) have gained significant attention for medical image segmentation due to their outstanding performance, becoming the standard approach for these tasks. However, CNNs are limited in capturing long-range dependencies and spatial correlations due to the nature of convolutional operations. Although Transformers were designed to address these issues, they struggle to effectively capture low-level features. Multi-scale representations, which account for both fine-grained object details and broader context, have proven to be effective. Motivated by this, we propose a multi-scale CNN-Transformer parallel fusion network. The CNN branch employs a standard encoder-decoder structure to extract local features, while the Transformer branch leverages a pre-trained PVTv2-B3 to capture global features. To improve the CNN branch, we introduce the Linear Space Deep Aggregation Module (LSDAM) to replace traditional skip connections. For the Transformer, we introduce a multi-level Progressive Decoder, eliminating skip connections between the encoder and decoder, and instead use an Intermediate Decoder module for improved feature extraction. The Adaptive Residual Weighted Fuser (ARWF) is employed to fuse features from both branches, compensating for the CNN’s limited global feature extraction and the Transformer’s inability to capture local features. A Final Decoder is added to enhance the model’s decision-making capability and further improve segmentation performance. Rigorous experiments on three publicly available datasets demonstrate that the HCT-Net model significantly outperforms state-of-the-art methods, achieving superior segmentation accuracy.