Limitations of CEM and Mammographic Image Analysis Methods: Exploring Multi-view Information with a Novel Approach

The above-mentioned CEM image analysis method and mammographic analysis method have made significant contributions. However, they still have certain limitations. The connectivity-based methods lose a lot of local information, thus cannot effectively utilize multi-view information. The attention-based methods can better learn cross-view information, but they have not explored the potential of integrating local and global features from multiple views. To overcome these limitations, we propose a novel approach that combines the strengths of both connectivity-based and attention-based methods. Our approach utilizes a multi-scale feature extraction network to capture both local and global information from multiple views. This network is then integrated with an attention module to selectively focus on the most informative features from each view. We evaluate our approach on a benchmark dataset and demonstrate its superior performance compared to existing methods. Our findings suggest that our proposed approach is a promising solution for effectively utilizing multi-view information in CEM and mammographic image analysis.