Learning semantic image quality for fetal ultrasound from noisy ranking
  annotation
Manxi Lin Jakob Ambsdorf Emilie Pi Fogtmann Sejer Zahra Bashir Chun Kit Wong Paraskevas Pegios Alberto Raheli Morten Bo Søndergaard Svendsen Mads Nielsen Martin Grønnebæk Tolsgaard Anders Nymark Christensen Aasa Feragen
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.