Search Results - "Cho, Chang Gyu"

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  1. 1

    Comparative analysis of machine learning algorithms for computer-assisted reporting based on fully automated cross-lingual RadLex mappings by Maros, Máté E., Cho, Chang Gyu, Junge, Andreas G., Kämpgen, Benedikt, Saase, Victor, Siegel, Fabian, Trinkmann, Frederik, Ganslandt, Thomas, Groden, Christoph, Wenz, Holger

    Published in Scientific reports (09-03-2021)
    “…Computer-assisted reporting (CAR) tools were suggested to improve radiology report quality by context-sensitively recommending key imaging biomarkers. However,…”
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    Journal Article
  2. 2

    Machine learning on top of deep learning-based brain volumetry segmentation to support neuroradiologists in diagnosing neurodegenerative disorders by Cho, Chang Gyu, Wenz, Holger, Forster, Alex, Sebald, Markus, Ganslandt, Thomas, Groden, Christoph, Maros, Mate

    Published in Clinical neuroradiology (Munich) (01-09-2021)
    “…Background: We investigated whether supervised machine learning (ML) algorithms can be effectively applied to segmentation results of an FDA-approved deep…”
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    Journal Article
  3. 3

    THE HIDDEN PROWESS OF NLP AND RADIOLOGICAL REPORTS: A TEXT MINING PIPELINE FOR MACHINE LEARNING-ASSISTED DIAGNOSTICS AND REPORTING by Junge, Andreas Georg, Kampgen, Benedikt, Cho, Chang Gyu, Groden, Christoph, Ganslandt, Thomas, Wenz, Holger, Maros, Mate

    Published in Clinical neuroradiology (Munich) (01-09-2019)
    “…Background: The value of radiological reports is often neglected and advancements in natural language processing (NLP) are forced to take a backseat behind the…”
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    Journal Article
  4. 4

    HOW TO BREAK THE BOTTLENECK OF AI-DRIVEN RADIOLOGY AND GET YOUR HANDS ON THE NEW GOLD: "RIPE" A SOLUTION FOR DATA EXTRACTION AND ANALYSIS PIPELINE by Junge, Andreas Georg, Cho, Chang Gyu, Bohme, Johannes, Forster, Alex, Groden, Christoph, Ganslandt, Thomas, Wenz, Holger, Maros, Mate

    Published in Clinical neuroradiology (Munich) (01-09-2019)
    “…Hintergrund: In the new era of AI-driven radiology, data is the new gold. Still, data extraction and generation of high-quality labeled cohorts is extremely…”
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    Journal Article
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