HGNet: High-Order Spatial Awareness Hypergraph and Multi-Scale Context Attention Network for Colorectal Polyp Detection
Journal:
arXiv
Published Date:
Jul 7, 2025
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% [email protected], significantly improving small lesion
differentiation and clinical interpretability. The source code will be made
publicly available upon publication of this paper.
