Diffusion-based Iterative Counterfactual Explanations for Fetal Ultrasound Image Quality Assessment
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arXiv
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| Main Authors: | , , , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866916820188921856 |
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| author | Pegios, Paraskevas Lin, Manxi Weng, Nina Svendsen, Morten Bo Søndergaard Bashir, Zahra Bigdeli, Siavash Christensen, Anders Nymark Tolsgaard, Martin Feragen, Aasa |
| author_facet | Pegios, Paraskevas Lin, Manxi Weng, Nina Svendsen, Morten Bo Søndergaard Bashir, Zahra Bigdeli, Siavash Christensen, Anders Nymark Tolsgaard, Martin Feragen, Aasa |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_08700 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Diffusion-based Iterative Counterfactual Explanations for Fetal Ultrasound Image Quality Assessment Pegios, Paraskevas Lin, Manxi Weng, Nina Svendsen, Morten Bo Søndergaard Bashir, Zahra Bigdeli, Siavash Christensen, Anders Nymark Tolsgaard, Martin Feragen, Aasa Image and Video Processing Computer Vision and Pattern Recognition Human-Computer Interaction Machine Learning 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. |
| title | Diffusion-based Iterative Counterfactual Explanations for Fetal Ultrasound Image Quality Assessment |
| topic | Image and Video Processing Computer Vision and Pattern Recognition Human-Computer Interaction Machine Learning |
| url | https://arxiv.org/abs/2403.08700 |