  Pulmonary diseases can cause severe respiratory problems, leading to sudden
death if not treated timely. Many researchers have utilized deep learning
systems to diagnose pulmonary disorders using chest X-rays (CXRs). However,
such systems require exhaustive training efforts on large-scale data to
effectively diagnose chest abnormalities. Furthermore, procuring such
large-scale data is often infeasible and impractical, especially for rare
diseases. With the recent advances in incremental learning, researchers have
periodically tuned deep neural networks to learn different classification tasks
with few training examples. Although, such systems can resist catastrophic
forgetting, they treat the knowledge representations independently of each
other, and this limits their classification performance. Also, to the best of
our knowledge, there is no incremental learning-driven image diagnostic
framework that is specifically designed to screen pulmonary disorders from the
CXRs. To address this, we present a novel framework that can learn to screen
different chest abnormalities incrementally. In addition to this, the proposed
framework is penalized through an incremental learning loss function that
infers Bayesian theory to recognize structural and semantic inter-dependencies
between incrementally learned knowledge representations to diagnose the
pulmonary diseases effectively, regardless of the scanner specifications. We
tested the proposed framework on five public CXR datasets containing different
chest abnormalities, where it outperformed various state-of-the-art system
through various metrics.
