One potential limitation of the current approach is its reliance on the quality and consistency of the mammographic images. Variations in imaging protocols, patient positioning, and breast compression can introduce significant deformations that may not be adequately captured by the attention alignment module. To address this, future improvements could involve the development of more sophisticated image registration techniques that account for these variations, ensuring that the model can accurately align and compare features across time points.
Additionally, the current model may struggle with complex breast tissue changes that occur due to factors such as hormonal fluctuations, aging, or treatment effects. To enhance the model's robustness, incorporating advanced machine learning techniques, such as generative adversarial networks (GANs), could help simulate and model these complex changes. This would allow the model to learn from a broader range of scenarios, improving its predictive capabilities.
Furthermore, expanding the dataset to include a more diverse population with varying breast densities and pathologies could enhance the model's generalizability. By training on a wider array of cases, the model would be better equipped to handle the complexities of breast tissue changes and improve its accuracy in risk prediction and time-to-event estimation.