Explicit Physics-Informed Deep Learning for Computer-Aided Diagnostic Tasks in Medical Imaging

Research output: Contribution to journalArticlepeer-review


DNN-based systems have demonstrated unprecedented performance in terms of accuracy and speed over the past decade. However, recent work has shown that such models may not be sufficiently robust during the inference process. Furthermore, due to the data-driven learning nature of DNNs, designing interpretable and generalizable networks is a major challenge, especially when considering critical applications such as medical computer-aided diagnostics (CAD) and other medical imaging tasks. Within this context, a line of approaches incorporating prior knowledge domain information into deep learning methods has recently emerged. In particular, many of these approaches utilize known physics-based forward imaging models, aimed at improving the stability and generalization ability of DNNs for medical imaging applications. In this paper, we review recent work focused on such physics-based or physics-prior-based learning for a variety of imaging modalities and medical applications. We discuss how the inclusion of such physics priors to the training process and/or network architecture supports their stability and generalization ability. Moreover, we propose a new physics-based approach, in which an explicit physics prior, which describes the relation between the input and output of the forward imaging model, is included as an additional input into the network architecture. Furthermore, we propose a tailored training process for this extended architecture, for which training data are generated with perturbed physical priors that are also integrated into the network. Within the scope of this approach, we offer a problem formulation for a regression task with a highly nonlinear forward model and highlight possible useful applications for this task. Finally, we briefly discuss future challenges for physics-informed deep learning in the context of medical imaging.

Original languageEnglish
Pages (from-to)385-401
Number of pages17
JournalMachine Learning and Knowledge Extraction
Issue number1
StatePublished - Mar 2024

Bibliographical note

Publisher Copyright:
© 2024 by the authors.


  • computer-aided diagnostics
  • deep learning
  • deep neural networks
  • generalization
  • medical imaging
  • physics-informed deep learning
  • robustness
  • stability

ASJC Scopus subject areas

  • Engineering (miscellaneous)
  • Artificial Intelligence


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