  Acquiring properly annotated data is expensive in the medical field as it
requires experts, time-consuming protocols, and rigorous validation. Active
learning attempts to minimize the need for large annotated samples by actively
sampling the most informative examples for annotation. These examples
contribute significantly to improving the performance of supervised machine
learning models, and thus, active learning can play an essential role in
selecting the most appropriate information in deep learning-based diagnosis,
clinical assessments, and treatment planning. Although some existing works have
proposed methods for sampling the best examples for annotation in medical image
analysis, they are not task-agnostic and do not use multimodal auxiliary
information in the sampler, which has the potential to increase robustness.
Therefore, in this work, we propose a Multimodal Variational Adversarial Active
Learning (M-VAAL) method that uses auxiliary information from additional
modalities to enhance the active sampling. We applied our method to two
datasets: i) brain tumor segmentation and multi-label classification using the
BraTS2018 dataset, and ii) chest X-ray image classification using the
COVID-QU-Ex dataset. Our results show a promising direction toward
data-efficient learning under limited annotations.
