Convolutional neural networks (CNNs) are known to fail if a difference exists in the data they are trained and tested on, known as domain shifts. This sensitivity is particularly problematic in computational pathology, where various factors, such as different staining protocols and stain providers, introduce domain shifts. Many solutions have been proposed in the literature to address this issue, with data augmentation being one of the most popular approaches. While data augmentation can significantly enhance the performance of a CNN in the presence of domain shifts, it does not guarantee robustness. Therefore, it would be advantageous to integrate generalization to specific sources of domain shift directly into the network's capabilities when known to be present in the real world. In this study, we draw inspiration from roto-translation equivariant CNNs and propose a customized layer to enhance domain generalization and the CNN's ability to handle variations in staining. To evaluate our approach, we conduct experiments on two publicly available, multi-institutional datasets: CAMELYON17 and MIDOG.