Comparative Analysis of Radiomic Features and Gene Expression Profiles in Histopathology Data Using Graph Neural Networks
This work addresses melanoma diagnosis in computational dermatology, offering incremental improvements in classification methods.
The study tackled melanoma classification by integrating radiomic features with gene expression profiles using graph neural networks, resulting in enhanced diagnostic accuracy and computational efficiency, with radiomics allowing data extraction from fewer stains to reduce costs.
This study leverages graph neural networks to integrate MELC data with Radiomic-extracted features for melanoma classification, focusing on cell-wise 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.