Diffusion-based Iterative Counterfactual Explanations for Fetal
  Ultrasound Image Quality Assessment
Paraskevas Pegios Manxi Lin Nina Weng Morten Bo Søndergaard Svendsen Zahra Bashir Siavash Bigdeli Anders Nymark Christensen Martin Tolsgaard Aasa Feragen
Obstetric ultrasound image quality is crucial for accurate diagnosis and
monitoring of fetal health. However, producing high-quality standard planes is
difficult, influenced by the sonographer's expertise and factors like the
maternal BMI or the fetus dynamics. In this work, we propose using
diffusion-based counterfactual explainable AI to generate realistic
high-quality standard planes from low-quality non-standard ones. Through
quantitative and qualitative evaluation, we demonstrate the effectiveness of
our method in producing plausible counterfactuals of increased quality. This
shows future promise both for enhancing training of clinicians by providing
visual feedback, as well as for improving image quality and, consequently,
downstream diagnosis and monitoring.