Comparative Analysis of Radiomic Features and Gene Expression Profiles in Histopathology Data using Graph Neural Networks
Rivera Monroy L, Rist L, Eberhardt M, Ostalecki C, Bauer A, Vera J, Breininger K, Maier A (2024)
Publication Type: Conference contribution
Publication year: 2024
Journal
Publisher: Springer Link
Edited Volumes: Bildverarbeitung für die Medizin 2024
Pages Range: 160-165
Event location: Erlangen
ISBN: 9783658440367
DOI: 10.1007/978-3-658-44037-4_49
Abstract
This study leverages graph neural networks to integrate MELC data with Radiomic-extracted features for melanoma classification, focusing on cellwise analysis. It assesses the effectiveness of gene expression profiles and Radiomic features, revealing that Radiomic features, particularly when combined with UMAP for dimensionality reduction, significantly enhance classification performance. Notably, using Radiomics contributes to increased diagnostic accuracy and computational efficiency, as it allows for the extraction of critical data from fewer stains, thereby reducing operational costs. This methodology marks an advancement in computational dermatology for melanoma cell classification, setting the stage for future research and potential developments.
Authors with CRIS profile
Luis Rivera Monroy
Lehrstuhl für Informatik 5 (Mustererkennung)
Leonhard Rist
Lehrstuhl für Informatik 5 (Mustererkennung)
Martin Eberhardt
Department of Dermatology
Christian Ostalecki
Department of Dermatology
Julio Vera González
Professur für Tumorimmunologie (Schwerpunkt Systembiologie)
Katharina Breininger
Juniorprofessur für Artificial Intelligence in Medical Imaging
Andreas Maier
Lehrstuhl für Informatik 5 (Mustererkennung)
How to cite
APA:
Rivera Monroy, L., Rist, L., Eberhardt, M., Ostalecki, C., Bauer, A., Vera, J.,... Maier, A. (2024). Comparative Analysis of Radiomic Features and Gene Expression Profiles in Histopathology Data using Graph Neural Networks. In Andreas Maier, Thomas M. Deserno, Heinz Handels, Klaus Maier-Hein, Christoph Palm, Thomas Tolxdorff (Eds.), Proceedings of the Bildverarbeitung für die Medizin 2024 (pp. 160-165). Erlangen, DE: Springer Link.
MLA:
Rivera Monroy, Luis, et al. "Comparative Analysis of Radiomic Features and Gene Expression Profiles in Histopathology Data using Graph Neural Networks." Proceedings of the Bildverarbeitung für die Medizin 2024, Erlangen Ed. Andreas Maier, Thomas M. Deserno, Heinz Handels, Klaus Maier-Hein, Christoph Palm, Thomas Tolxdorff, Springer Link, 2024. 160-165.
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