论文标题
使用图表表示黑色素瘤样品的细胞水平表征
Employing Graph Representations for Cell-level Characterization of Melanoma MELC Samples
论文作者
论文摘要
组织病理学成像对于诊断和治疗皮肤疾病至关重要。因此，计算机辅助的方法在皮肤疾病的细分和分类等任务中表现出了有希望的结果。但是，收集基本数据和足够高质量的注释是一个挑战。这项工作描述了一种使用可疑的黑色素瘤样品的管道，该管道使用多型配体制图（MELC）进行了表征。然后将这种细胞级的组织表征表示为图，并用于训练图神经网络。这种成像技术与这项工作中提出的方法相结合，达到了87％的分类准确性，表现优于现有方法10％。
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%.
