@article{dittberner2018development,
    type = {article},
    key = {dittberner2018development},
    title = {Development of an Automatic Image Analysis Method by Deep Learning Methods for the Detection of Head and Neck Cancer Based on Standard Real-Time Near-Infrared ICG Fluorescence Endoscopy Images (NIR-ICG-FE)},
    author = {Andreas Dittberner and Sven Sickert and Joachim Denzler and Orlando Guntinas-Lichius and Thomas Bitter and Sven Koscielny},
    journal = {Laryngo-Rhino-Otologie},
    year = {2018},
    number = {S02},
    pages = {97},
    volume = {97},
    abstract = {Improving the gold standard in the diagnosis of head and neck cancer using white light and invasive biopsy with digital image recognition procedures, there is a need for a development of new technologies. In the sense of an "optical biopsy", they in vivo and online should provide additional objective information for decision making for the head and neck surgeon. Artificial neural networks in combination with machine learning might be a helpful and fast approach.},
    doi = {10.1055/s-0038-1640005},
    groups = {biomedical,semanticsegmentaton,cancer_research},
    url = {https://www.thieme-connect.de/products/ejournals/abstract/10.1055/s-0038-1640005},
}