HGNet提升结直肠息肉检测精度，尤其擅长小病灶识别与结果解释。
HGNet: High-Order Spatial Awareness Hypergraph and Multi-Scale Context Attention Network for Colorectal Polyp Detection
- 构建高阶空间超图与多尺度注意力机制，增强特征表达与边界建模。
- 在CVC-ClinicDB等数据集上达94%准确率、90.6%召回率和90% [email protected]。
- 引入可解释性可视化方法，适合临床辅助诊断场景使用。
结直肠癌（CRC）与结直肠息肉的恶性转化密切相关，早期检测至关重要。然而，现有模型在小病灶检测、边界精确定位及决策可解释性方面仍存挑战。为此，本文提出HGNet，融合高阶空间感知超图与多尺度上下文注意力机制。关键创新包括：(1) 提出高效多尺度上下文注意力（EMCA）模块，增强病变特征表示与边界建模能力；(2) 在检测头前引入空间超图卷积模块，捕获节点间的高阶空间关系；(3) 采用迁移学习缓解医学图像数据稀缺问题；(4) 使用特征类激活图（Eigen-CAM）实现决策可视化。实验表明，HGNet在多个公开数据集上达到94%准确率、90.6%召回率与90% [email protected]，显著提升小病灶区分能力与临床可解释性。代码将在论文发表后公开。
原文摘要 · Abstract (English)
Colorectal cancer (CRC) is closely linked to the malignant transformation of colorectal polyps, making early detection essential. However, current models struggle with detecting small lesions, accurately localizing boundaries, and providing interpretable decisions. To address these issues, we propose HGNet, which integrates High-Order Spatial Awareness Hypergraph and Multi-Scale Context Attention. Key innovations include: (1) an Efficient Multi-Scale Context Attention (EMCA) module to enhance lesion feature representation and boundary modeling; (2) the deployment of a spatial hypergraph convolution module before the detection head to capture higher-order spatial relationships between nodes; (3) the application of transfer learning to address the scarcity of medical image data; and (4) Eigen Class Activation Map (Eigen-CAM) for decision visualization. Experimental results show that HGNet achieves 94% accuracy, 90.6% recall, and 90% [email protected], significantly improving small lesion differentiation and clinical interpretability. The source code will be made publicly available upon publication of this paper.
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