A major challenge in developing data-driven algorithms for medical imaging is the limited size of available datasets. Furthermore, these datasets often suffer from inter-site heterogeneity caused by the use of different scanners and scanning protocols. These factors may contribute to overfitting, which undermines the generalization ability and robustness of deep learning classification models in the medical domain, leading to inadequate performance in real-world applications. To address these challenges and mitigate overfitting, we propose a framework which incorporates explanation supervision during training of Vision Transformer (ViT) models for image classification. Our approach leverages foreground masks of the class object during training to regularize attribution maps extracted from ViT, encouraging the model to focus on relevant image regions and make predictions based on pertinent features. We introduce a new method for generating explanatory attribution maps from ViT-based models and construct a dual-loss function that combines a conventional classification loss with a term that regularizes attribution maps. Our approach demonstrates superior performance over existing methods on two challenging medical imaging datasets, highlighting its effectiveness in the medical domain and its potential for application in other fields.