Translating Imaging to Genomics: Leveraging Transformers for Predictive Modeling

TL;DR

This paper introduces a transformer-based model that predicts genomic information directly from non-invasive CT/MRI images, enabling personalized healthcare insights without invasive procedures.

Contribution

It presents a novel transformer approach for linking medical imaging with genomic data using only CT/MRI images, bypassing invasive biopsy methods.

Findings

Successfully predicts genomic sequences from imaging data

Demonstrates non-invasive imaging can inform genomic profiles

Enhances personalized healthcare through imaging-genomics integration

Abstract

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.