The intriguing effect of frequency disentangled learning on medical image segmentation
Guanghui Fu,
Gabriel Jimenez,
Sophie Loizillon
et al.
Abstract:Deep models have been shown to tend to fit the target function from low to high frequencies (a phenomenon called the frequency principle of deep learning). One may hypothesize that such property can be leveraged for better training of deep learning models, in particular for segmentation tasks where annotated datasets are often small. In this paper, we exploit this property to propose a new training method based on frequencydomain disentanglement. It consists of three main stages. First, it disentangles the ima… Show more
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