  Medical image datasets are essential for training models used in
computer-aided diagnosis, treatment planning, and medical research. However,
some challenges are associated with these datasets, including variability in
data distribution, data scarcity, and transfer learning issues when using
models pre-trained from generic images. This work studies the effect of these
challenges at the intra- and inter-domain level in few-shot learning scenarios
with severe data imbalance. For this, we propose a methodology based on Siamese
neural networks in which a series of techniques are integrated to mitigate the
effects of data scarcity and distribution imbalance. Specifically, different
initialization and data augmentation methods are analyzed, and four adaptations
to Siamese networks of solutions to deal with imbalanced data are introduced,
including data balancing and weighted loss, both separately and combined, and
with a different balance of pairing ratios. Moreover, we also assess the
inference process considering four classifiers, namely Histogram, $k$NN, SVM,
and Random Forest. Evaluation is performed on three chest X-ray datasets with
annotated cases of both positive and negative COVID-19 diagnoses. The accuracy
of each technique proposed for the Siamese architecture is analyzed separately
and their results are compared to those obtained using equivalent methods on a
state-of-the-art CNN. We conclude that the introduced techniques offer
promising improvements over the baseline in almost all cases, and that the
selection of the technique may vary depending on the amount of data available
and the level of imbalance.
