Paper ID: 2211.05884

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

Luis Carlos Rivera Monroy, Leonhard Rist, Martin Eberhardt, Christian Ostalecki, Andreas Baur, Julio Vera, Katharina Breininger, Andreas Maier

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%.

Submitted: Nov 10, 2022