Deep neural networks (DNNs) have immense potential for precise clinical decision-making in the field of biomedical imaging. However, accessing high-quality data is crucial for ensuring the high-performance of DNNs. Obtaining medical imaging data is often challenging in terms of both quantity and quality. To address these issues, we propose a score-based counterfactual generation (SCG) framework to create counterfactual images from latent space, to compensate for scarcity and imbalance of data. In addition, some uncertainties in external physical factors may introduce unnatural features and further affect the estimation of the true data distribution. Therefore, we integrated a learnable FuzzyBlock into the classifier of the proposed framework to manage these uncertainties. The proposed SCG framework can be applied to both classification and lesion localization tasks. The experimental results revealed a remarkable performance boost in classification tasks, achieving an average performance enhancement of 3-5% compared to previous state-of-the-art (SOTA) methods in interpretable lesion localization.

中文翻译：

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基于分数的反事实生成，用于可解释的医学图像分类和病变定位


深度神经网络 （DNN） 在生物医学成像领域具有精确的临床决策的巨大潜力。然而，获取高质量的数据对于确保 DNN 的高性能至关重要。获取医学成像数据在数量和质量方面通常都具有挑战性。为了解决这些问题，我们提出了一个基于分数的反事实生成 （SCG） 框架，从潜在空间创建反事实图像，以补偿数据的稀缺性和不平衡性。此外，外部物理因素的一些不确定性可能会引入非自然特征，并进一步影响对真实数据分布的估计。因此，我们将一个可学习的 FuzzyBlock 集成到拟议框架的分类器中，以管理这些不确定性。所提出的 SCG 框架可应用于分类和病变定位任务。实验结果显示，分类任务的性能得到了显著提升，与以前的最先进的 （SOTA） 方法相比，在可解释病变定位方面实现了 3-5% 的平均性能提升。