Employing Graph Representations for Cell-level Characterization of Melanoma MELC Samples

• URL: http://arxiv.org/abs/2211.05884v1
• Date: Thu, 10 Nov 2022 21:28:53 GMT
• Title: Employing Graph Representations for Cell-level Characterization of Melanoma MELC Samples
• Authors: Luis Carlos Rivera Monroy, Leonhard Rist, Martin Eberhardt, Christian Ostalecki, Andreas Baur, Julio Vera, Katharina Breininger, and Andreas Maier
• Abstract summary: This work describes a pipeline that uses suspected melanoma samples that have been characterized using Multi-Epitope-Ligand Cartography (MELC) This cellular-level tissue characterisation is then represented as a graph and 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%.
• Score: 6.007415817901118
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• 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 tissue characterisation is then represented as a graph and 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%.

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