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- W4386362605 abstract "Histopathology imaging is crucial for the diagnosis and treatment of skin diseases. For this reason, computer-assisted approaches have gained popularity and shown promising results in tasks such as segmentation and classification of skin disorders. However, collecting essential data and sufficiently high-quality annotations is a challenge. This work describes a pipeline that uses suspected melanoma samples that have been characterized using Multi-Epitope-Ligand Cartography (MELC). This cellular level characterization of the tissue is then represented as a graph and finally used to train a graph neural network. This imaging technology, combined with the methodology proposed in this work, achieves a classification accuracy of 87 %, outperforming existing approaches by 10 %." @default.
- W4386362605 created "2023-09-02" @default.
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- W4386362605 date "2023-04-18" @default.
- W4386362605 modified "2023-09-27" @default.
- W4386362605 title "Employing Graph Representations for Cell-Level Characterization of Melanoma MELC Samples" @default.
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- W4386362605 doi "https://doi.org/10.1109/isbi53787.2023.10230519" @default.
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