Digital histopathological image analysis encounters processing level challenges due to the extremely high resolution of whole slide images (WSI), which requires resource-intensive annotations and extensive computational resources. Due to these limitations, histological processing is heavily reliant on self-supervision and patch-level processing. While there are multiple modelling approaches which consider the aspect of limited supervision and patch-level processing, most of the models are not capable of capturing spatial context while focusing into the local cellular-level details. This simultaneous processing of global spatial context along with cellular details at the patch-level again becomes computationally inefficient to handle. In order to circumvent these issues, we devise a spatially aware masked attention based DINO network (SMatt-DINO) that processes spatial neighbourhood in an efficient manner by selectively masking the attention layers within the network. This capacitates our model to generate robust representations that are capable of efficiently classifying brain regions in histological images in a self-supervised manner. We further incorporate positional information into the network to enhance classification in anatomical boundary regions. Through experimentation, we validate that our model has better performance in classification of regions in fetal brain, specifically reducing misprediction in the anatomical boundary regions. We have also validated the generalization capability of our model by testing it on a fetal brain from a completely different acquisition setup. This demonstrates the robustness and effectiveness of our model in histological tasks.