Vision Transformers (ViTs) have revolutionized medical image analysis, yet they face challenges in simultaneously capturing global context and local anatomical details crucial for accurate diagnosis. We present SuperCrossViT, an architecture that enhances the standard CrossViT framework by integrating superpixel segmentation for improved analysis of histopathological images. Our approach leverages superpixels to group pixels into meaningful tissue regions, preserving structural information while maintaining computational efficiency. We evaluate our method on the task of metastatic cancer detection in lymph node Whole Slide Images (WSIs), performing binary classification of tumor versus normal tissue patches. Experimental results demonstrate that SuperCrossViT consistently outperforms baseline ViT and standard CrossViT architectures, achieving superior accuracy in distinguishing cancerous from normal tissues. Our findings suggest that the integration of superpixel segmentation with transformer based architectures offers a promising direction for enhancing the precision of computer aided diagnosis in histopathology.