HYATT-Net is Grand: A Hybrid Attention Network for Performant Anatomical Landmark Detection
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| Main Authors: | , , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866929632198000640 |
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| author | Zhou, Xiaoqian Huang, Zhen Zhu, Heqin Yao, Qingsong Zhou, S. Kevin |
| author_facet | Zhou, Xiaoqian Huang, Zhen Zhu, Heqin Yao, Qingsong Zhou, S. Kevin |
| contents | 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}. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2412_06499 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | HYATT-Net is Grand: A Hybrid Attention Network for Performant Anatomical Landmark Detection Zhou, Xiaoqian Huang, Zhen Zhu, Heqin Yao, Qingsong Zhou, S. Kevin Computer Vision and Pattern Recognition 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}. |
| title | HYATT-Net is Grand: A Hybrid Attention Network for Performant Anatomical Landmark Detection |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2412.06499 |