Learning A Multi-Task Transformer Via Unified And Customized Instruction Tuning For Chest Radiograph Interpretation. (arXiv:2311.01092v1 [cs.CV])

The emergence of multi-modal deep learning models has made significant
impacts on clinical applications in the last decade. However, the majority of
models are limited to single-tasking, without considering disease diagnosis is
indeed a multi-task procedure. Here, we demonstrate a unified transformer model
specifically designed for multi-modal clinical tasks by incorporating
customized instruction tuning. We first compose a multi-task training dataset
comprising 13.4 million instruction and ground-truth pairs (with approximately
one million radiographs) for the customized tuning, involving both image- …

applications arxiv clinical deep learning diagnosis disease disease diagnosis emergence impacts indeed interpretation multi-modal transformer transformer model