Paper ID: 2403.08700

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.

Submitted: Mar 13, 2024