MCPNet:Masked Coordinate Pooling-based Attention Network for Medical Landmark Detection

This study introduces a masked coordinate pooling-basedattention network (MCPNet), which is a novel framework designed toimprove medical landmark detection. Coordinate features are essentialin accurately localizing medical landmarks. Recently, many studies haveused coordinate information in final regression heads; however, its directincorporation into the attention mechanism remains largely unexplored.To address this issue, we propose MCPNet, which directly extracts posi-tional features from CNN feature maps and analyzes them in the coordi-nate domain. Specifically, MCPNet employs learnable masks and coordi-nate maps to effectively encode diverse and richer anatomical positionalcues of the landmarks. Moreover, the proposed mask maps are utilizedwhen transforming the analyzed coordinate features back into the origi-nal CNN feature space, ensuring smooth reconstruction within the net-work. Extensive experiments on four public benchmark datasets demon-strate that the proposed MCPNet outperforms state-of-the-art methods,offering improved accuracy and efficiency.