M 2 C A - Net : Multi-scale and multi-frequency channel attentional neural network for invasive coronary angiography...

Accurate segmentation of invasive coronary angiography (ICA) images is crucial for diagnosing of coronary artery disease (CAD). While existing deep learning-based segmentation models have shown promising results, most operate solely in the spatial domain and overlook informative cues available in the frequency domain. To address this limitation, we design a multi-scale and multi-frequency channel attention neural network ( $M^{2}CA$ - $\mathrm{Net}$ ), which fuses spatial and frequency information to enhance ICA image segmentation. Specifically, we introduce a multi-frequency channel attention (MCA) block based on 2D discrete cosine transform (2D DCT) to extract global frequency representations, enhancing channel discrimination. Combined with multi-scale convolutions, this design facilitates effective fusion of spatial and frequency-domain features. We validate our model on both public and clinical datasets, where $M^{2}CA$ - $\mathrm{Net}$ achieves superior segmentation performance and outperforms several state-of-the-art architectures.