Employing Graph Representations for Cell-level Characterization of Melanoma MELC Samples
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arXiv
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| Main Authors: | , , , , , , , |
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| Format: | Preprint |
| Published: |
2022
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| _version_ | 1866912086830874624 |
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| author | Monroy, Luis Carlos Rivera Rist, Leonhard Eberhardt, Martin Ostalecki, Christian Baur, Andreas Vera, Julio Breininger, Katharina Maier, Andreas |
| author_facet | Monroy, Luis Carlos Rivera Rist, Leonhard Eberhardt, Martin Ostalecki, Christian Baur, Andreas Vera, Julio Breininger, Katharina Maier, Andreas |
| contents | 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%. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2211_05884 |
| institution | arXiv |
| publishDate | 2022 |
| record_format | arxiv |
| spellingShingle | Employing Graph Representations for Cell-level Characterization of Melanoma MELC Samples Monroy, Luis Carlos Rivera Rist, Leonhard Eberhardt, Martin Ostalecki, Christian Baur, Andreas Vera, Julio Breininger, Katharina Maier, Andreas Computer Vision and Pattern Recognition Artificial Intelligence Computational Engineering, Finance, and Science 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%. |
| title | Employing Graph Representations for Cell-level Characterization of Melanoma MELC Samples |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence Computational Engineering, Finance, and Science |
| url | https://arxiv.org/abs/2211.05884 |