  Background and objective: Employing deep learning models in critical domains
such as medical imaging poses challenges associated with the limited
availability of training data. We present a strategy for improving the
performance and generalization capabilities of models trained in low-data
regimes. Methods: The proposed method starts with a pre-training phase, where
features learned in a self-supervised learning setting are disentangled to
improve the robustness of the representations for downstream tasks. We then
introduce a meta-fine-tuning step, leveraging related classes between
meta-training and meta-testing phases but varying the granularity level. This
approach aims to enhance the model's generalization capabilities by exposing it
to more challenging classification tasks during meta-training and evaluating it
on easier tasks but holding greater clinical relevance during meta-testing. We
demonstrate the effectiveness of the proposed approach through a series of
experiments exploring several backbones, as well as diverse pre-training and
fine-tuning schemes, on two distinct medical tasks, i.e., classification of
prostate cancer aggressiveness from MRI data and classification of breast
cancer malignity from microscopic images. Results: Our results indicate that
the proposed approach consistently yields superior performance w.r.t. ablation
experiments, maintaining competitiveness even when a distribution shift between
training and evaluation data occurs. Conclusion: Extensive experiments
demonstrate the effectiveness and wide applicability of the proposed approach.
We hope that this work will add another solution to the arsenal of addressing
learning issues in data-scarce imaging domains.
