A deep learning approach for semantic segmentation of unbalanced data in electron tomography of catalytic materials

HIGHLIGHTS

• who: Arda Genc from the Center for theState University have published the research: A deep learning approach for semantic segmentation of unbalanced data in electron tomography of catalytic materials, in the Journal: Scientific Reports Scientific Reports of 28/02/2022
• what: As described previously, the authors investigate the complex bulk and surface structure of the u03b3-Alumina/Pt catalysts. In this work, predictions were assessed using four commonly used semantic segmentation metrics: Dice similarity coefficient (DSC), recall, precision, and Hausdorff distance (HD)39. The authors aim to down-weight the impact of outliers and noise . . .
  

   

  

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