Automatic quality control framework for more reliable integration of machine learning-based image segmentation into medical workflows

Machine learning algorithms underpin modern diagnostic-aiding software, which has proved valuable in clinical practice, particularly in radiology. However, inaccuracies, mainly due to the limited availability of clinical samples for training these algorithms, hamper their wider applicability, accept...

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Veröffentlicht in:	arXiv.org 2022-12
Hauptverfasser:	Williams, Elena, Niehaus, Sebastian, Reinelt, Janis, Merola, Alberto, Paul Glad Mihai, Villringer, Kersten, Thierbach, Konstantin, Medawar, Evelyn, Lichterfeld, Daniel, Roeder, Ingo, Scherf, Nico, Maria del C Valdés Hernández
Format:	Artikel
Sprache:	eng
Schlagworte:	Algorithms Automatic control Clinical medicine Deep learning Failure detection Image segmentation Machine learning Magnetic resonance imaging Medical imaging Quality control
Online-Zugang:	Volltext
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