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.
人工智能在临床实践中具有巨大潜力，尤其在乳腺癌等疾病的早期精准诊断方面。然而，要使人工智能模型在医疗场景中真正发挥作用，不仅需要高准确度，还必须具备可解释性与可靠性。本研究旨在分析不同模型参数的变化如何影响用于乳腺癌检测的弱监督深度学习模型性能。方法：本研究采用方差分析法探究不同参数调整对深度学习模型性能的影响。该模型基于注意力机制构建，既能执行分类任务，又能识别医学图像中最具诊断意义的区域，从而提升模型的可解释性。通过方差分析确定各参数对模型结果影响的显著性，揭示驱动模型决策的关键因素。结果：分析表明特定参数显著影响模型性能，某些参数配置展现出更高的敏感性与特异性。借助方差分析，我们识别出影响图像正确分类能力的关键因素。这种方法深化了对模型工作机制的理解，并指明了通过参数优化提升临床实践可靠性的改进方向。结论：本研究证明，在医学应用中对深度学习模型进行方差分析，能够为理解性能影响因素提供重要依据。这种分析有助于增强人工智能模型的可解释性与可信度，这对乳腺癌检测等实际医疗场景中的应用至关重要。通过理解这些关键因素，能够开发出更透明、更高效的临床人工智能工具。
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