  Building generalizable AI models is one of the primary challenges in the
healthcare domain. While radiologists rely on generalizable descriptive rules
of abnormality, Neural Network (NN) models suffer even with a slight shift in
input distribution (e.g., scanner type). Fine-tuning a model to transfer
knowledge from one domain to another requires a significant amount of labeled
data in the target domain. In this paper, we develop an interpretable model
that can be efficiently fine-tuned to an unseen target domain with minimal
computational cost. We assume the interpretable component of NN to be
approximately domain-invariant. However, interpretable models typically
underperform compared to their Blackbox (BB) variants. We start with a BB in
the source domain and distill it into a \emph{mixture} of shallow interpretable
models using human-understandable concepts. As each interpretable model covers
a subset of data, a mixture of interpretable models achieves comparable
performance as BB. Further, we use the pseudo-labeling technique from
semi-supervised learning (SSL) to learn the concept classifier in the target
domain, followed by fine-tuning the interpretable models in the target domain.
We evaluate our model using a real-life large-scale chest-X-ray (CXR)
classification dataset. The code is available at:
\url{https://github.com/batmanlab/MICCAI-2023-Route-interpret-repeat-CXRs}.
