论文标题
评估显着性图在医学成像中异常定位的有效性
Assessing the validity of saliency maps for abnormality localization in medical imaging
论文作者
论文摘要
显着图已成为一种广泛使用的方法，用于评估输入图像的哪些区域与训练有素的神经网络的预测最相关。但是，在医学成像的背景下，据我们所知，尚无研究检查这些技术的功效，并使用与地面真相界框重叠对其进行了量化。在这项工作中，我们探讨了RSNA肺炎数据集上各种现有的显着图方法的可信度。我们发现GradCAM对模型参数和标签随机化最敏感，并且对模型体系结构高度不可知。
Saliency maps have become a widely used method to assess which areas of the input image are most pertinent to the prediction of a trained neural network. However, in the context of medical imaging, there is no study to our knowledge that has examined the efficacy of these techniques and quantified them using overlap with ground truth bounding boxes. In this work, we explored the credibility of the various existing saliency map methods on the RSNA Pneumonia dataset. We found that GradCAM was the most sensitive to model parameter and label randomization, and was highly agnostic to model architecture.
