Cooperative metric learning-based hybrid transformer for automatic recognition of standard echocardiographic multi-views

The successful recognition of the standard echocardiographic ten-views remains elusive, primarily due to the complexity of cardiac anatomy, confusion caused by low-quality data, and subtle variations among closely related multi-views. To cope with the limitations of existing algorithms, which include a lack of objectivity, accuracy, and robustness, we propose a Hybrid Cooperative Metric Network (HCMN). We enhance the objectivity, accuracy and robustness of the quality assessment by integrating knowledge of cycle-consistency with metric consistency, which helps mitigate inaccurate fitting in hybrid distributions. Therefore, it provides a clear feature similarity distribution to prevent feature confusion. The experiments demonstrate that the HCMN model significantly outperforms the state-of-the-art in quality assessment, achieving an impressive accuracy of 96.74%. We believe this novel framework will establish a reliable benchmark for recognizing standard echocardiographic multi-views and provide a new interpretable perspective on standardized the automatic cardiac disease diagnosis. By adapting and applying advanced assessment methodologies, we can enhance the clarity and interpretability of medical imaging, thereby aiding in the precise identification of lesions and improving decision-making accuracy in drug discovery.