1 research outputs found
Model-Based and Data-Driven Strategies in Medical Image Computing
Model-based approaches for image reconstruction, analysis and interpretation
have made significant progress over the last decades. Many of these approaches
are based on either mathematical, physical or biological models. A challenge
for these approaches is the modelling of the underlying processes (e.g. the
physics of image acquisition or the patho-physiology of a disease) with
appropriate levels of detail and realism. With the availability of large
amounts of imaging data and machine learning (in particular deep learning)
techniques, data-driven approaches have become more widespread for use in
different tasks in reconstruction, analysis and interpretation. These
approaches learn statistical models directly from labelled or unlabeled image
data and have been shown to be very powerful for extracting clinically useful
information from medical imaging. While these data-driven approaches often
outperform traditional model-based approaches, their clinical deployment often
poses challenges in terms of robustness, generalization ability and
interpretability. In this article, we discuss what developments have motivated
the shift from model-based approaches towards data-driven strategies and what
potential problems are associated with the move towards purely data-driven
approaches, in particular deep learning. We also discuss some of the open
challenges for data-driven approaches, e.g. generalization to new unseen data
(e.g. transfer learning), robustness to adversarial attacks and
interpretability. Finally, we conclude with a discussion on how these
approaches may lead to the development of more closely coupled imaging
pipelines that are optimized in an end-to-end fashion.Comment: Accepted in IEEE Proceeding