We propose to apply model-agnostic meta-learning (MAML) and MAML++ for pathology classification from optical coherence tomography (OCT) images. These meta-learning methods train a set of initialization parameters using training tasks, by which the model achieves fast convergence in new tasks with only a small amount of data. Our model is pretrained on an OCT dataset with seven types of retinal pathologies, and then refined and tested on another dataset with three types of pathologies. The classification accuracies of MAML and MAML++ reached 90.60% and 95.60% respectively, which are higher than the traditional deep learning method with pretraining.
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