The performance of deep learning models is highly dataset-dependent. Pretrained models on large-scale datasets have significant advantages in understanding general patterns by leveraging large volumes of data. Some of these models, which are adaptable to a wide range of downstream tasks, are referred to as foundation models. Recently, several foundation models have been published in the field of computational pathology, recognized for their potential to advance deep learning applications in several downstream tasks. In order to effectively utilize multiple foundation models, each with its own advantages, it is crucial to effectively summarize or ensemble their advantages. In this paper, we propose a feature transformation method for the effective utilization of features from multiple foundation models using an autoencoder-based architecture. This method facilitates the extraction of integrated features from multiple foundation models, enabling more generalized training. We demonstrated that the proposed approach resulted in more robust representations for out-of-distribution datasets in our patch-level classification tasks.