Diffusion-based Iterative Counterfactual Explanations for Fetal Ultrasound Image Quality Assessment
<|reference_start|>Diffusion-based Iterative Counterfactual Explanations for Fetal Ultrasound Image Quality Assessment: 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.<|reference_end|>
@article{pegios2024diffusion-based,
    title={Diffusion-based Iterative Counterfactual Explanations for Fetal
  Ultrasound Image Quality Assessment},
    author={Paraskevas Pegios, Manxi Lin, Nina Weng, Morten Bo S{o}ndergaard
  Svendsen, Zahra Bashir, Siavash Bigdeli, Anders Nymark Christensen, Martin
  Tolsgaard and Aasa Feragen},
    journal={arXiv preprint arXiv:2403.08700},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.08700},
    primaryClass={eess.IV cs.CV cs.HC cs.LG}
}