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

Rivera Monroy L, Rist L, Eberhardt M, Ostalecki C, Baur A, Vera J, Breininger K, Maier A (2023)

Publication Type: Conference contribution

Publication year: 2023

Conference Proceedings Title: 2023 IEEE 20th International Symposium on Biomedical Imaging (ISBI)

Event location: Cartagena

DOI: 10.1109/ISBI53787.2023.10230519

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 characterization of the tissue is then represented as a graph and finally 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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How to cite

APA:

Rivera Monroy, L., Rist, L., Eberhardt, M., Ostalecki, C., Baur, A., Vera, J.,... Maier, A. (2023). Employing Graph Representations for Cell-Level Characterization of Melanoma MELC Samples. In IEEE (Eds.), 2023 IEEE 20th International Symposium on Biomedical Imaging (ISBI). Cartagena, CO.

MLA:

Rivera Monroy, Luis, et al. "Employing Graph Representations for Cell-Level Characterization of Melanoma MELC Samples." Proceedings of the IEEE International Symposium on Biomedical Imaging, Cartagena Ed. IEEE, 2023.

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