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Recent work has investigated the distributions of learned convolution filtersthrough a large-scale study containing hundreds of heterogeneous image models.Surprisingly, on average, the distributions only show minor drifts incomparisons of various studied dimensions including the learned task, imagedomain, or dataset. However, among the studied image domains, medical imagingmodels appeared to show significant outliers through "spikey" distributions,and, therefore, learn clusters of highly specific filters different from otherdomains. Following this observation, we study the collected medical imagingmodels in more detail. We show that instead of fundamental differences, theoutliers are due to specific processing in some architectures. Quite thecontrary, for standardized architectures, we find that models trained onmedical data do not significantly differ in their filter distributions fromsimilar architectures trained on data from other domains. Our conclusionsreinforce previous hypotheses stating that pre-training of imaging models canbe done with any kind of diverse image data.

Does Medical Imaging learn different Convolution Filters?