Towards computational fluorescence microscopy: Machine learning-based integrated prediction of morphological and molecular tumor profiles

@article{Binder2018TowardsCF,
  title={Towards computational fluorescence microscopy: Machine learning-based integrated prediction of morphological and molecular tumor profiles},
  author={Alexander Binder and Michael Bockmayr and Miriam H{\"a}gele and Stephan Wienert and Daniel Heim and Katharina Hellweg and Albrecht Stenzinger and Laura Parlow and Jan Budczies and Benjamin Goeppert and Denise Treue and Manato Kotani and Masaru Ishii and Manfred Dietel and Andreas Hocke and Carsten Denkert and Klaus-Robert M{\"u}ller and Frederick Klauschen},
  journal={ArXiv},
  year={2018},
  volume={abs/1805.11178}
}
Recent advances in cancer research largely rely on new developments in microscopic or molecular profiling techniques offering high level of detail with respect to either spatial or molecular features, but usually not both. Here, we present a novel machine learning-based computational approach that allows for the identification of morphological tissue features and the prediction of molecular properties from breast cancer imaging data. This integration of microanatomic information of tumors with… CONTINUE READING
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