Our paper entitled “Interpretable CNN Pruning for Preserving Scale-Covariant Features in Medical Imaging” by M. Graziani, T. Lompech, H. Müller, A. Depeursinge and V. Andrearczyk has been accepted for presentation at the Workshop on Interpretability of Machine Intelligence in Medical Image Computing (iMIMIC) at MICCAI 2020.
In this paper, we analyze the invariance to scale in standard pre-trained CNNs and the effects of this invariance in a medical imaging task, where scale often carries crucial information.