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The emergence of multi-modal deep learning models has made significantimpacts on clinical applications in the last decade. However, the majority ofmodels are limited to single-tasking, without considering disease diagnosis isindeed a multi-task procedure. Here, we demonstrate a unified transformer modelspecifically designed for multi-modal clinical tasks by incorporatingcustomized instruction tuning. We first compose a multi-task training datasetcomprising 13.4 million instruction and ground-truth pairs (with approximatelyone million radiographs) for the customized tuning, involving both image- andpixel-level tasks. Thus, we can unify the various vision-intensive tasks in asingle training framework with homogeneous model inputs and outputs to increaseclinical interpretability in one reading. Finally, we demonstrate the overallsuperior performance of our model compared to prior arts on various chest X-raybenchmarks across multi-tasks in both direct inference and finetuning settings.Three radiologists further evaluate the generated reports against the recordedones, which also exhibit the enhanced explainability of our multi-task model.

Learning A Multi-Task Transformer Via Unified And Customized Instruction Tuning For Chest Radiograph Interpretation