旋转-尺度等变可控滤波器
Rotation-Scale Equivariant Steerable Filters
AI总结：
针对CNN在组织病理学图像分析中泛化受限问题，提出旋转-尺度等变可控滤波器（RSESF），结合可控滤波器与尺度空间理论，在连续层约束尺度参数跨越不相交尺度，实验表明该方法以更少参数和GPU资源超越其他方法。
摘要：
将旋转等变性或尺度等变性融入CNN已被证明能有效提升模型的泛化性能。然而，将旋转和尺度等变性联合融入CNN尚未得到广泛探索。活检组织的数字组织学成像可以在任意方向和放大倍数下捕获，并以不同分辨率存储，导致细胞呈现不同的尺度。当将传统CNN应用于组织病理学图像分析时，模型的泛化性能受限，原因在于：1）滤波器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性特征的能力；2）在给定尺度图像上训练的固定尺寸滤波器无法泛化到不同尺度的图像。为解决这些问题，我们提出了旋转-尺度等变可控滤波器（RSESF），它结合了可控滤波器和尺度空间理论。RSESF包含高斯滤波器线性组合的滤波器副本，其方向由方向导数控制，其尺度参数是可训练的，但被约束在网络连续层中跨越不相交的尺度。在两个腺体分割数据集上的大量实验表明，我们的方法优于其他方法，且所需的可训练参数和GPU资源少得多。源代码可在：https://github.com/ynulonger/RSESF 获取。
英文摘要：
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.
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