Translating Imaging to Genomics: Leveraging Transformers for Predictive Modeling
This enables non-invasive, personalized healthcare by bridging imaging and genomics data, though it is incremental as it adapts transformers to a new domain.
The study tackled predicting genomic information from non-invasive CT/MRI images using a transformer-based model, achieving accurate genomic profile predictions without relying on invasive whole slide images.
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.