Translating Imaging to Genomics: Leveraging Transformers for Predictive Modeling
Fuente:
arXiv
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| Autores principales: | , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| Acceso en línea: | |
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| _version_ | 1866914895531868160 |
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| author | Farooq, Aiman Mishra, Deepak Chaudhury, Santanu |
| author_facet | Farooq, Aiman Mishra, Deepak Chaudhury, Santanu |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_00311 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Translating Imaging to Genomics: Leveraging Transformers for Predictive Modeling Farooq, Aiman Mishra, Deepak Chaudhury, Santanu Computer Vision and Pattern Recognition 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. |
| title | Translating Imaging to Genomics: Leveraging Transformers for Predictive Modeling |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2408.00311 |