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A Deep Learning Approach for Semantic Segmentation of Unbalanced Data in Electron Tomography of Catalytic Materials. (arXiv:2201.07342v1 [cond-mat.mtrl-sci])
Jan. 20, 2022, 2:10 a.m. | Arda Genc, Libor Kovarik, Hamish L. Fraser
cs.LG updates on arXiv.org arxiv.org
Heterogeneous catalysts possess complex surface and bulk structures,
relatively poor intrinsic contrast, and often a sparse distribution of the
catalytic nanoparticles (NPs), posing a significant challenge for image
segmentation, including the current state-of-the-art deep learning methods. To
tackle this problem, we apply a deep learning-based approach for the
multi-class semantic segmentation of a $\gamma$-Alumina/Pt catalytic material
in a class imbalance situation. Specifically, we used the weighted focal loss
as a loss function and attached it to the U-Net's fully convolutional …
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