Digital profiling of cancer transcriptomes from histology images with grouped vision attention.

ABSTRACT: Cancer is a heterogeneous disease that demands precise molecular profiling for better understanding and management. Recently, deep learning has demonstrated potentials for cost-efficient prediction of molecular alterations from histology images. While transformer-based deep learning architectures have enabled significant progress in non-medical domains, their application to histology images remains limited due to small dataset sizes coupled with the explosion of trainable parameters. Here, we develop SEQUOIA, a transformer model to predict cancer transcriptomes from whole-slide histology images. To enable the full potential of transformers, we first pre-train the model using data from 1,802 normal tissues. Then, we fine-tune and evaluate the model in 4,331 tumor samples acros

SUBMITTER: Zheng Y 

PROVIDER: S-EPMC10557714 | biostudies-literature | 2024 Jan

REPOSITORIES: biostudies-literature