  In this study, we present a novel approach for predicting genomic information
from medical imaging modalities using a transformer-based model. We aim to
bridge the gap between imaging and genomics data by leveraging transformer
networks, allowing for accurate genomic profile predictions from CT/MRI images.
Presently most studies rely on the use of whole slide images (WSI) for the
association, which are obtained via invasive methodologies. We propose using
only available CT/MRI images to predict genomic sequences. Our transformer
based approach is able to efficiently generate associations between multiple
sequences based on CT/MRI images alone. This work paves the way for the use of
non-invasive imaging modalities for precise and personalized healthcare,
allowing for a better understanding of diseases and treatment.
