  Real-world application of chest X-ray abnormality classification requires
dealing with several challenges: (i) limited training data; (ii) training and
evaluation sets that are derived from different domains; and (iii) classes that
appear during training may have partial overlap with classes of interest during
evaluation. To address these challenges, we present an integrated framework
called Generalized Cross-Domain Multi-Label Few-Shot Learning (GenCDML-FSL).
The framework supports overlap in classes during training and evaluation,
cross-domain transfer, adopts meta-learning to learn using few training
samples, and assumes each chest X-ray image is either normal or associated with
one or more abnormalities. Furthermore, we propose Generalized Episodic
Training (GenET), a training strategy that equips models to operate with
multiple challenges observed in the GenCDML-FSL scenario. Comparisons with
well-established methods such as transfer learning, hybrid transfer learning,
and multi-label meta-learning on multiple datasets show the superiority of our
approach.
