Does Medical Imaging learn different Convolution Filters?
Not on the map. Why?
Abstract (arXiv)
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.
Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 10 of its 17 references that are in the map. First posted on arXiv 25 Oct 2022.
Builds on
10 of 17 references.
References · 10
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Citing papers per year: 2023: 1; 2024: 1.
2 papers.