Incorporating either rotation equivariance or scale equivariance into CNNs has proved to be effective in improving models' generalization performance. However, jointly integrating rotation and scale equivariance into CNNs has not been widely explored. Digital histology imaging of biopsy tissue can be captured at arbitrary orientation and magnification and stored at different resolutions, resulting in cells appearing in different scales. When conventional CNNs are applied to histopathology image analysis, the generalization performance of models is limited because 1) a part of the parameters of filters are trained to fit rotation transformation, thus decreasing the capability of learning other discriminative features; 2) fixed-size filters trained on images at a given scale fail to generalize to those at different scales. To deal with these issues, we propose the Rotation-Scale Equivariant Steerable Filter (RSESF), which incorporates steerable filters and scale-space theory. The RSESF contains copies of filters that are linear combinations of Gaussian filters, whose direction is controlled by directional derivatives and whose scale parameters are trainable but constrained to span disjoint scales in successive layers of the network. Extensive experiments on two gland segmentation datasets demonstrate that our method outperforms other approaches, with much fewer trainable parameters and fewer GPU resources required. The source code is available at: https://github.com/ynulonger/RSESF.
翻译:将旋转等变性或缩放等变性纳入CNN在改善模型的泛化性能方面已被证明是有效的。然而,将旋转和缩放等变性共同整合到CNN中尚未被广泛探讨。活检组织的数字组织学成像可以以任意方向和放大倍率捕获并存储在不同分辨率下,这导致细胞以不同的尺度出现。当将传统的CNN应用于组织病理学图像分析时,模型的泛化性能受到限制,因为卷积核的一部分参数被训练以适应旋转变换,从而降低了学习其他判别特征的能力;以及在给定比例的图像上训练的固定大小卷积核无法推广到在不同比例下的图像。为了解决这些问题,我们提出了旋转缩放等变的可操纵滤波器(RSESF),该滤波器包含由方向导数控制其方向、尺度参数可训练但受限于网络中连续层中不相交尺度的高斯滤波器的线性组合卷积核。在两个腺体分割数据集上的广泛实验表明,我们的方法优于其他方法,需要较少的可训练参数和GPU资源。源代码可在 https://github.com/ynulonger/RSESF 获取。