HYATT-Net is Grand: A Hybrid Attention Network for Performant Anatomical Landmark Detection

Anatomical landmark detection (ALD) from a medical image is crucial for a wide array of clinical applications. While existing methods achieve quite some success in ALD, they often struggle to balance global context with computational efficiency, particularly with high-resolution images, thereby leading to the rise of a natural question: where is the performance limit of ALD? In this paper, we aim to forge performant ALD by proposing a {\bf HY}brid {\bf ATT}ention {\bf Net}work (HYATT-Net) with the following designs: (i) A novel hybrid architecture that integrates CNNs and Transformers. Its core is the BiFormer module, utilizing Bi-Level Routing Attention for efficient attention to relevant image regions. This, combined with Attention Residual Module(ARM), enables precise local feature refinement guided by the global context. (ii) A Feature Fusion Correction Module that aggregates multi-scale features and thus mitigates a resolution loss. Deep supervision with a mean-square error loss on multi-resolution heatmaps optimizes the model. Experiments on five diverse datasets demonstrate state-of-the-art performance, surpassing existing methods in accuracy, robustness, and efficiency. The HYATT-Net provides a promising solution for accurate and efficient ALD in complex medical images. Our codes and data are already released at: \url{https://github.com/ECNUACRush/HYATT-Net}.

Hybrid Attention Network: An efficient approach for anatomy-free landmark detection

Accurate anatomical landmark detection in medical images is crucial for clinical applications. Existing methods often struggle to balance global context with computational efficiency, particularly with high-resolution images. This paper introduces the Hybrid Attention Network(HAN), a novel hybrid architecture integrating CNNs and Transformers. Its core is the BiFormer module, utilizing Bi-Level Routing Attention (BRA) for efficient attention to relevant image regions. This, combined with Convolutional Attention Blocks (CAB) enhanced by CBAM, enables precise local feature refinement guided by the global context. A Feature Fusion Correction Module (FFCM) integrates multi-scale features, mitigating resolution loss. Deep supervision with MSE loss on multi-resolution heatmaps optimizes the model. Experiments on five diverse datasets demonstrate state-of-the-art performance, surpassing existing methods in accuracy, robustness, and efficiency. The HAN provides a promising solution for accurate and efficient anatomical landmark detection in complex medical images. Our codes and data will be released soon at: \url{https://github.com/MIRACLE-Center/}.