  Transfer learning is an important step to extract meaningful features and
overcome the data limitation in the medical Visual Question Answering (VQA)
task. However, most of the existing medical VQA methods rely on external data
for transfer learning, while the meta-data within the dataset is not fully
utilized. In this paper, we present a new multiple meta-model quantifying
method that effectively learns meta-annotation and leverages meaningful
features to the medical VQA task. Our proposed method is designed to increase
meta-data by auto-annotation, deal with noisy labels, and output meta-models
which provide robust features for medical VQA tasks. Extensively experimental
results on two public medical VQA datasets show that our approach achieves
superior accuracy in comparison with other state-of-the-art methods, while does
not require external data to train meta-models.
