RAIFE-Net: A Region-Aware Implicit Feature Extraction Network for Acupoint Detection

Acupuncture, the core part of Traditional Chinese Medicine, relies on the accuracy of acupoint detection. But most of existing detection networks can’t achieve precise acupoint detection, whose challenges arise from posture-induced occlusion or edge effects, background clutter, and the lack of prominent surface cues for most acupoints. To address the issues, an Region-Aware Implicit Feature Extraction Network (RAIFE-Net) is proposed to enhance feature awareness and spatial reasoning for precise acupoint detection. RAIFE-Net has three fundamental components. Firstly, a Evolvable Gradient Convolution is designed to perceive gradient variations to refine edge features, which can motivate model to precisely recognize the acupoints appear near the edge of body. Secondly, the Saliency-Driven Feature Selector is able to highlight salient feature of acupoint regions by generating an attention mask which is available to suppress the interference of clutter background. At last, a Structured Position-Aware Relational Loss is developed to optimize spatial perception by constructing coordinate relationship for acupoint positions, enabling the network to accurately infer the relative positions among acupoints and improve detection precision. In the experiments, the proposed RAIFE-Net achieves state-of-the-art performance on the clinical acupoint dataset constructed for this study and public dataset.