Learning semantic image quality for fetal ultrasound from noisy ranking annotation
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arXiv
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| Main Authors: | , , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866929242086834176 |
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| author | Lin, Manxi Ambsdorf, Jakob Sejer, Emilie Pi Fogtmann Bashir, Zahra Wong, Chun Kit Pegios, Paraskevas Raheli, Alberto Svendsen, Morten Bo Søndergaard Nielsen, Mads Tolsgaard, Martin Grønnebæk Christensen, Anders Nymark Feragen, Aasa |
| author_facet | Lin, Manxi Ambsdorf, Jakob Sejer, Emilie Pi Fogtmann Bashir, Zahra Wong, Chun Kit Pegios, Paraskevas Raheli, Alberto Svendsen, Morten Bo Søndergaard Nielsen, Mads Tolsgaard, Martin Grønnebæk Christensen, Anders Nymark Feragen, Aasa |
| contents | We introduce the notion of semantic image quality for applications where image quality relies on semantic requirements. Working in fetal ultrasound, where ranking is challenging and annotations are noisy, we design a robust coarse-to-fine model that ranks images based on their semantic image quality and endow our predicted rankings with an uncertainty estimate. To annotate rankings on training data, we design an efficient ranking annotation scheme based on the merge sort algorithm. Finally, we compare our ranking algorithm to a number of state-of-the-art ranking algorithms on a challenging fetal ultrasound quality assessment task, showing the superior performance of our method on the majority of rank correlation metrics. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_08294 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Learning semantic image quality for fetal ultrasound from noisy ranking annotation Lin, Manxi Ambsdorf, Jakob Sejer, Emilie Pi Fogtmann Bashir, Zahra Wong, Chun Kit Pegios, Paraskevas Raheli, Alberto Svendsen, Morten Bo Søndergaard Nielsen, Mads Tolsgaard, Martin Grønnebæk Christensen, Anders Nymark Feragen, Aasa Computer Vision and Pattern Recognition We introduce the notion of semantic image quality for applications where image quality relies on semantic requirements. Working in fetal ultrasound, where ranking is challenging and annotations are noisy, we design a robust coarse-to-fine model that ranks images based on their semantic image quality and endow our predicted rankings with an uncertainty estimate. To annotate rankings on training data, we design an efficient ranking annotation scheme based on the merge sort algorithm. Finally, we compare our ranking algorithm to a number of state-of-the-art ranking algorithms on a challenging fetal ultrasound quality assessment task, showing the superior performance of our method on the majority of rank correlation metrics. |
| title | Learning semantic image quality for fetal ultrasound from noisy ranking annotation |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2402.08294 |