1 research outputs found
Adversarial Training for Patient-Independent Feature Learning with IVOCT Data for Plaque Classification
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 acquisitionspecific characteristics. We compare our regularization
method to typical data augmentation strategies and show that our approach
improves performance for a small medical dataset.Comment: Presented at MIDL 2018 Conference
https://openreview.net/forum?id=SJWY1Ujs