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Abstract: Deep learning methods have shown impressive results for a variety of medical problems over the last few years. However, datasets tend to be small due to time\-consuming annotation. As datasets with different patients are often very heterogeneous generalization to new patients can be difficult. This is complicated further if large differences in image acquisition can occur, which is common during intravascular optical coherence tomography for coronary plaque imaging. We address this problem with an adversarial training strategy where we force a part of a deep neural network to learn features that are independent of patient\- or acquisition\-specific characteristics. We compare our regularization method to typical data augmentation strategies and show that our approach improves performance for a small medical dataset.