Correlation via synthesis: end-to-end nodule image generation and radiogenomic map learning based on generative adversarial network

Understand

Radiogenomic map linking image features and gene expression profiles is useful for noninvasively identifying molecular properties of a particular type of disease.

• Conventionally, such map is produced in three separate steps: 1) gene-clustering to "metagenes", 2) image feature extraction, and 3) statistical correlation between metagenes and image features.
• Each step is independently performed and relies on arbitrary measurements.
• In this work, we investigate the potential of an end-to-end method fusing gene data with image features to generate synthetic image and learn radiogenomic map simultaneously.