  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%.
