Hernandez Nerea, Carrillo-Perez Francisco, Ortuño Francisco M, Rojas Ignacio, Valenzuela Olga
Department of Computer Engineering, Automation and Robotics, University of Granada, 18071 Granada, Spain.
Cancers (Basel). 2025 Apr 24;17(9):1425. doi: 10.3390/cancers17091425.
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.
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.
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.
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（AI）有潜力提升临床实践，尤其是在乳腺癌等疾病的早期准确诊断方面。然而，要使人工智能模型在医疗环境中有效，它们不仅必须准确，还必须具有可解释性和可靠性。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的性能。
在这项工作中，我们应用方差分析（ANOVA）来研究不同参数的变化如何影响深度学习模型的性能。该模型使用注意力机制构建，既能进行分类又能识别医学图像中最相关的区域，从而提高模型的可解释性。方差分析用于确定每个参数在影响模型结果方面的重要性，深入了解驱动其决策的具体因素。
我们的分析表明，某些参数对模型性能有显著影响，一些配置比其他配置表现出更高的敏感性和特异性。通过使用方差分析，我们确定了影响模型正确分类图像能力的关键因素。这种方法有助于更深入地理解模型的工作方式，并突出可以改进的领域，以提高其在临床实践中的可靠性。
该研究表明，将方差分析应用于医学应用中的深度学习模型，可为影响性能的参数提供有价值的见解。这种分析有助于使人工智能模型更具可解释性和可信度，这对于它们在乳腺癌检测等现实世界医疗环境中的应用至关重要。了解这些因素有助于开发更透明、高效的临床用人工智能工具。
