  Numerous Deep Learning (DL) models have been developed for a large spectrum
of medical image analysis applications, which promises to reshape various
facets of medical practice. Despite early advances in DL model validation and
implementation, which encourage healthcare institutions to adopt them, some
fundamental questions remain: are the DL models capable of generalizing? What
causes a drop in DL model performances? How to overcome the DL model
performance drop? Medical data are dynamic and prone to domain shift, due to
multiple factors such as updates to medical equipment, new imaging workflow,
and shifts in patient demographics or populations can induce this drift over
time. In this paper, we review recent developments in generalization methods
for DL-based classification models. We also discuss future challenges,
including the need for improved evaluation protocols and benchmarks, and
envisioned future developments to achieve robust, generalized models for
medical image classification.
