Pathology, research and practice · 2026
H-DCA Net: Hierarchical dual-branch coordinate attention framework for multi-scale gastric histopathology diagnosis.
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Li Changyu, Liu Wei
Аннотация
BACKGROUND AND OBJECTIVE: Early and accurate diagnosis of gastric cancer is crucial for improving patient prognosis. However, conventional histopathological diagnosis is constrained by subjectivity and low efficiency. Current computer-aided diagnosis (CAD) systems often struggle with a "scale mismatch" problem, failing to simultaneously integrate macroscopic tissue structures and microscopic cellular details. To address this bottleneck, this study aims to develop a novel deep learning framework, named H-DCA Net, that mimics the multi-scale diagnostic workflow of pathologists to improve the accuracy and reliability of gastric cancer classification.
METHODS: We propose the Hierarchical Dual-Branch Coordinate Attention Network (H-DCA Net), a framework featuring two heterogeneous branches. A high-level semantic branch (based on EfficientNetV2-S with Efficient Channel Attention) captures global tissue-level patterns, while a local detail branch (based on MobileNetV3-L with Coordinate Attention) focuses on fine-grained cellular features. These complementary features are integrated via a hierarchical attention mechanism. The model was trained and validated on the public GasHisSDB dataset across three different image patch sizes (80 ×80, 120 ×120, and 160 ×160 pixels) using an independent test set strategy.
RESULTS: H-DCA Net demonstrated highly competitive performance across multiple scales. Specifically, on the 120 × sub-dataset, the model attained an Accuracy of 99.27 %, Precision of 99.3 %, Sensitivity of 99.2 %, and Specificity of 99.4 %. Similarly, high performance was observed at 160 × (Accuracy: 99.04 %, Sensitivity: 99.2 %, Specificity: 98.9 %) and 80 × (Accuracy: 98.62 %, Sensitivity: 98.6 %, Specificity: 98.6 %). Comparative analysis confirmed that H-DCA Net outperforms or matches state-of-the-art single-branch, dual-branch, and hybrid architectures, particularly excelling in scenarios with limited field of view (e.g., 80 ×).
CONCLUSIONS: The proposed H-DCA Net, through its synergistic dual-branch architecture and hierarchical attention mechanism, effectively integrates multi-scale pathological information. It serves as a high-precision foundation for an efficient, reliable, and interpretable auxiliary tool, offering robust support for automated gastric cancer screening and helping to reduce diagnostic subjectivity while improving workflow efficiency.
METHODS: We propose the Hierarchical Dual-Branch Coordinate Attention Network (H-DCA Net), a framework featuring two heterogeneous branches. A high-level semantic branch (based on EfficientNetV2-S with Efficient Channel Attention) captures global tissue-level patterns, while a local detail branch (based on MobileNetV3-L with Coordinate Attention) focuses on fine-grained cellular features. These complementary features are integrated via a hierarchical attention mechanism. The model was trained and validated on the public GasHisSDB dataset across three different image patch sizes (80 ×80, 120 ×120, and 160 ×160 pixels) using an independent test set strategy.
RESULTS: H-DCA Net demonstrated highly competitive performance across multiple scales. Specifically, on the 120 × sub-dataset, the model attained an Accuracy of 99.27 %, Precision of 99.3 %, Sensitivity of 99.2 %, and Specificity of 99.4 %. Similarly, high performance was observed at 160 × (Accuracy: 99.04 %, Sensitivity: 99.2 %, Specificity: 98.9 %) and 80 × (Accuracy: 98.62 %, Sensitivity: 98.6 %, Specificity: 98.6 %). Comparative analysis confirmed that H-DCA Net outperforms or matches state-of-the-art single-branch, dual-branch, and hybrid architectures, particularly excelling in scenarios with limited field of view (e.g., 80 ×).
CONCLUSIONS: The proposed H-DCA Net, through its synergistic dual-branch architecture and hierarchical attention mechanism, effectively integrates multi-scale pathological information. It serves as a high-precision foundation for an efficient, reliable, and interpretable auxiliary tool, offering robust support for automated gastric cancer screening and helping to reduce diagnostic subjectivity while improving workflow efficiency.