Abstract
Artificial intelligence (AI) has the potential to enhance clinical practice, particularly in the early and accurate diagnosis of diseases like breast cancer. However, for AI models to be effective in medical settings, they must not only be accurate but also interpretable and reliable. This study aims to analyze how variations in different model parameters affect the performance of a weakly supervised deep learning model used for breast cancer detection. METHODS: In this work, we apply Analysis of Variance (ANOVA) to investigate how changes in different parameters impact the performance of the deep learning model. The model is built using attention mechanisms, which both perform classification and identify the most relevant regions in medical images, improving the interpretability of the model. ANOVA is used to determine the significance of each parameter in influencing the model's outcome, offering insights into the specific factors that drive its decision-making. RESULTS: Our analysis reveals that certain parameters significantly affect the model's performance, with some configurations showing higher sensitivity and specificity than others. By using ANOVA, we identify the key factors that influence the model's ability to classify images correctly. This approach allows for a deeper understanding of how the model works and highlights areas where improvements can be made to enhance its reliability in clinical practice. CONCLUSIONS: The study demonstrates that applying ANOVA to deep learning models in medical applications provides valuable insights into the parameters that influence performance. This analysis helps make AI models more interpretable and trustworthy, which is crucial for their adoption in real-world medical environments like breast cancer detection. Understanding these factors enables the development of more transparent and efficient AI tools for clinical use.