Diffusion-based Iterative Counterfactual Explanations for Fetal Ultrasound Image Quality Assessment

TL;DR

This paper introduces a diffusion-based counterfactual explainable AI method to generate high-quality fetal ultrasound images from low-quality ones, aiming to improve diagnostic accuracy and clinician training.

Contribution

The work presents a novel diffusion-based approach for generating realistic high-quality ultrasound images as counterfactuals, enhancing interpretability and training.

Findings

Effective generation of plausible high-quality ultrasound images

Quantitative and qualitative evaluation confirms approach's success

Potential to improve clinical training and diagnosis

Abstract

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.