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%.

该研究使用多表位配体地图（Multi-Epitope-Ligand Cartography，MELC）对疑似黑色素瘤样本进行细胞水平的组织表征并构建了表示该表征的图表，进而采用图神经网络进行训练。该技术与方法组合的皮肤疾病图像分类准确率高达 87％，比现有方法提高 10％。