Hidden Variables in Deep Learning Digital Pathology and Their Potential to Cause Batch Effects: Prediction Model Study

Hidden Variables in Deep Learning Digital Pathology and Their Potential to Cause Batch Effects: Prediction Model Study

BackgroundAn increasing number of studies within digital pathology show the potential of artificial intelligence (AI) to diagnose cancer using histological whole slide images, which requires large and diverse data sets. While diversification may result in more generalizable AI-based systems, it can...

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Main Authors: Schmitt, Max (Author), Maron, Roman Christoph (Author), Hekler, Achim (Author), Stenzinger, Albrecht (Author), Hauschild, Axel (Author), Weichenthal, Michael (Author), Tiemann, Markus (Author), Krahl, Dieter (Author), Kutzner, Heinz (Author), Utikal, Jochen Sven (Author), Haferkamp, Sebastian (Author), Kather, Jakob Nikolas (Author), Klauschen, Frederick (Author), Krieghoff-Henning, Eva (Author), Fröhling, Stefan (Author), von Kalle, Christof (Author), Brinker, Titus Josef (Author)
Format: Book
Published: JMIR Publications, 2021-02-01T00:00:00Z.
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