Learning Interpretable Anatomical Features Through Deep Generative Models: Application to Cardiac Remodeling

@article{Biffi2018LearningIA,
  title={Learning Interpretable Anatomical Features Through Deep Generative Models: Application to Cardiac Remodeling},
  author={Carlo Biffi and Ozan Oktay and Giacomo Tarroni and Wenjia Bai and Antonio M. Simoes Monteiro de Marvao and Georgia Doumou and Martin Rajchl and Reem Bedair and Sanjay Prasad and Stuart A. Cook and Declan P. O'Regan and Daniel Rueckert},
  journal={CoRR},
  year={2018},
  volume={abs/1807.06843}
}
Abstract. Alterations in the geometry and function of the heart define well-established causes of cardiovascular disease. However, current approaches to the diagnosis of cardiovascular diseases often rely on subjective human assessment as well as manual analysis of medical images. Both factors limit the sensitivity in quantifying complex structural and… CONTINUE READING