Abstract
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% mAP@0.5, significantly improving small lesion differentiation and clinical interpretability. The source code will be made publicly available upon publication of this paper.
Abstract (translated)
结直肠癌（CRC）与结直肠息肉的恶性转化密切相关，因此早期检测至关重要。然而，目前的模型在检测小病变、准确确定边界以及提供可解释性决策方面存在困难。为了解决这些问题，我们提出了HGNet，该网络结合了高阶空间感知超图和多尺度上下文注意力机制。 关键创新包括： 1. **高效多尺度上下文注意（EMCA）模块**：增强了病灶特征表示和边界建模。 2. **在检测头部之前部署空间超图卷积模块**：以捕获节点之间的高阶空间关系。 3. **应用迁移学习**：以应对医疗图像数据的稀缺性问题。 4. **特征类激活图（Eigen-CAM）**：用于决策可视化。 实验结果显示，HGNet达到了94%的准确率、90.6%的召回率和90%mAP@0.5，在小病变区分和临床可解释性方面有显著提升。该论文发表后，源代码将公开发布。
URL
https://arxiv.org/abs/2507.04880
