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Deep learning methods have shown impressive results for a variety of medicalproblems over the last few years. However, datasets tend to be small due totime-consuming annotation. As datasets with different patients are often veryheterogeneous generalization to new patients can be difficult. This iscomplicated further if large differences in image acquisition can occur, whichis common during intravascular optical coherence tomography for coronary plaqueimaging. We address this problem with an adversarial training strategy where weforce a part of a deep neural network to learn features that are independent ofpatient- or acquisitionspecific characteristics. We compare our regularizationmethod to typical data augmentation strategies and show that our approachimproves performance for a small medical dataset.

Adversarial Training for Patient-Independent Feature Learning with IVOCT Data for Plaque Classification