论文标题
医学成像学习不同的卷积过滤器吗？
Does Medical Imaging learn different Convolution Filters?
论文作者
论文摘要
最近的工作通过一项大规模研究调查了学习的卷积过滤器的分布，该研究包含数百个异质图像模型。令人惊讶的是，平均而言，分布仅显示在比较各种研究维度（包括学习任务，图像域或数据集）中的较小漂移。但是，在研究的图像域中，医学成像模型似乎通过“尖峰”分布显示出重要的异常值，因此，学习了与其他域不同特定过滤器的簇。在此观察之后，我们更详细地研究了收集的医学成像模型。我们表明，离群值不是基本差异，而是由于某些架构中的特定处理。相反，对于标准化的体系结构，我们发现在医疗数据上训练的模型与对来自其他域数据训练的类似体系结构的过滤分布没有显着差异。我们的结论加强了以前的假设，表明成像模型的预训练可以使用任何种类的图像数据来完成。
Recent work has investigated the distributions of learned convolution filters through a large-scale study containing hundreds of heterogeneous image models. Surprisingly, on average, the distributions only show minor drifts in comparisons of various studied dimensions including the learned task, image domain, or dataset. However, among the studied image domains, medical imaging models appeared to show significant outliers through "spikey" distributions, and, therefore, learn clusters of highly specific filters different from other domains. Following this observation, we study the collected medical imaging models in more detail. We show that instead of fundamental differences, the outliers are due to specific processing in some architectures. Quite the contrary, for standardized architectures, we find that models trained on medical data do not significantly differ in their filter distributions from similar architectures trained on data from other domains. Our conclusions reinforce previous hypotheses stating that pre-training of imaging models can be done with any kind of diverse image data.
