Diffusion-based Iterative Counterfactual Explanations for Fetal Ultrasound Image Quality Assessment
This addresses the challenge of acquiring high-quality ultrasound images for fetal health diagnosis, which is hindered by factors like sonographer expertise and maternal BMI, offering potential for clinician training and improved diagnostic accuracy.
The paper tackled the problem of low-quality fetal ultrasound images by using diffusion-based counterfactual explanations to generate realistic, high-quality standard planes from low-quality ones, demonstrating effectiveness through quantitative and qualitative evaluation.
Obstetric ultrasound image quality is crucial for accurate diagnosis and monitoring of fetal health. However, acquiring high-quality standard planes is difficult, influenced by the sonographer's expertise and factors like the maternal BMI or fetus dynamics. In this work, we explore 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 approach in generating plausible counterfactuals of increased quality. This shows future promise for enhancing training of clinicians by providing visual feedback and potentially improving standard plane quality and acquisition for downstream diagnosis and monitoring.