/ May 14th, 2021 /


🏆 Publication @ #1 Top Conference under Biomedical & Medical Informatics


MICCAI 2021, the 24th International Conference on Medical Image Computing and Computer Assisted Intervention, will be held from September 27th to October 1st 2021.

🎉 The conference has reviewed 1630 papers this year and AIOZ paper is among the top 13% that received a provisional accept recommendation. 🎉

Visit Conference Website at: https://www.miccai2021.org/en/

AIOZ research @ MICCAI 2021

✨ Multiple Meta-model Quantifying for Medical Visual Question Answering

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.

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