Foundational Models in Medical Imaging: A Comprehensive Survey and
  Future Vision
Bobby Azad Reza Azad Sania Eskandari Afshin Bozorgpour Amirhossein Kazerouni Islem Rekik Dorit Merhof
Foundation models, large-scale, pre-trained deep-learning models adapted to a
wide range of downstream tasks have gained significant interest lately in
various deep-learning problems undergoing a paradigm shift with the rise of
these models. Trained on large-scale dataset to bridge the gap between
different modalities, foundation models facilitate contextual reasoning,
generalization, and prompt capabilities at test time. The predictions of these
models can be adjusted for new tasks by augmenting the model input with
task-specific hints called prompts without requiring extensive labeled data and
retraining. Capitalizing on the advances in computer vision, medical imaging
has also marked a growing interest in these models. To assist researchers in
navigating this direction, this survey intends to provide a comprehensive
overview of foundation models in the domain of medical imaging. Specifically,
we initiate our exploration by providing an exposition of the fundamental
concepts forming the basis of foundation models. Subsequently, we offer a
methodical taxonomy of foundation models within the medical domain, proposing a
classification system primarily structured around training strategies, while
also incorporating additional facets such as application domains, imaging
modalities, specific organs of interest, and the algorithms integral to these
models. Furthermore, we emphasize the practical use case of some selected
approaches and then discuss the opportunities, applications, and future
directions of these large-scale pre-trained models, for analyzing medical
images. In the same vein, we address the prevailing challenges and research
pathways associated with foundational models in medical imaging. These
encompass the areas of interpretability, data management, computational
requirements, and the nuanced issue of contextual comprehension.