@inproceedings{Raj20_AOF,
    type = {inproceedings},
    key = {Raj20_AOF},
    title = {Automatic and Objective Facial Palsy Grading Index Prediction using Deep Feature Regression},
    author = {Anish Raj and Oliver Mothes and Sven Sickert and Gerd F. Volk and Orlando Guntinas-Lichius and Joachim Denzler},
    booktitle = {Annual Conference on Medical Image Understanding and Analysis (MIUA)},
    year = {2020},
    pages = {253-266},
    abstract = {One of the main reasons for a half-sided facial paralysis is caused by a dysfunction of the facial nerve. Physicians have to assess such a unilateral facial palsy with the help of standardized grading scales to evaluate the treatment. However, such assessments are usually very subjective and they are prone to variance and inconsistency between physicians regarding their experience. We propose an automatic non-biased method using deep features combined with a linear regression method for facial palsy grading index prediction. With an extension of the free software tool Auto-eFace we annotated images of facial palsy patients and healthy subjects according to a common facial palsy grading scale. In our experiments, we obtained an average grading error of 11%},
    doi = {10.1007/978-3-030-52791-4_20},
    groups = {biomedical,facialpalsy},
    url = {https://link.springer.com/chapter/10.1007/978-3-030-52791-4_20},
}