Digital histopathology slides are scanned and viewed under different
magnifications and stored as images at different resolutions. Convolutional
Neural Networks (CNNs) trained on such images at a given scale fail to
generalise to those at different scales. This inability is often addressed by
augmenting training data with re-scaled images, allowing a model with
sufficient capacity to learn the requisite patterns. Alternatively, designing
CNN filters to be scale-equivariant frees up model capacity to learn
discriminative features. In this paper, we propose the Scale-Equivariant UNet
(SEUNet) for image segmentation by building on scale-space theory. The SEUNet
contains groups of filters that are linear combinations of Gaussian basis
filters, whose scale parameters are trainable but constrained to span disjoint
scales through the layers of the network. Extensive experiments on a nuclei
segmentation dataset and a tissue type segmentation dataset demonstrate that
our method outperforms other approaches, with much fewer trainable parameters.

本论文提出了基于尺度空间理论的比例等变 UNet（SEUNet）进行数字组织病理学图像分割，实验结果表明该方法在训练参数更少的情况下优于其他方法。