Optimising for the Unknown: Domain Alignment for Cephalometric Landmark Detection
<|reference_start|>Optimising for the Unknown: Domain Alignment for Cephalometric Landmark Detection: Cephalometric Landmark Detection is the process of identifying key areas for cephalometry. Each landmark is a single GT point labelled by a clinician. A machine learning model predicts the probability locus of a landmark represented by a heatmap. This work, for the 2024 CL-Detection MICCAI Challenge, proposes a domain alignment strategy with a regional facial extraction module and an X-ray artefact augmentation procedure. The challenge ranks our method's results as the best in MRE of 1.186mm and third in the 2mm SDR of 82.04% on the online validation leaderboard. The code is available at https://github.com/Julian-Wyatt/OptimisingfortheUnknown.<|reference_end|>
@article{wyatt2024optimising,
    title={Optimising for the Unknown: Domain Alignment for Cephalometric Landmark
  Detection},
    author={Julian Wyatt and Irina Voiculescu},
    journal={arXiv preprint arXiv:2410.04445},
    year={2024},
    archivePrefix={arXiv},
    eprint={2410.04445},
    primaryClass={cs.CV}
}