AutoRG-Brain: Grounded Report Generation for Brain MRI
<|reference_start|>AutoRG-Brain: Grounded Report Generation for Brain MRI: Radiologists are tasked with interpreting a large number of images in a daily base, with the responsibility of generating corresponding reports. This demanding workload elevates the risk of human error, potentially leading to treatment delays, increased healthcare costs, revenue loss, and operational inefficiencies. To address these challenges, we initiate a series of work on grounded Automatic Report Generation (AutoRG), starting from the brain MRI interpretation system, which supports the delineation of brain structures, the localization of anomalies, and the generation of well-organized findings. We make contributions from the following aspects, first, on dataset construction, we release a comprehensive dataset encompassing segmentation masks of anomaly regions and manually authored reports, termed as RadGenome-Brain MRI. This data resource is intended to catalyze ongoing research and development in the field of AI-assisted report generation systems. Second, on system design, we propose AutoRG-Brain, the first brain MRI report generation system with pixel-level grounded visual clues. Third, for evaluation, we conduct quantitative assessments and human evaluations of brain structure segmentation, anomaly localization, and report generation tasks to provide evidence of its reliability and accuracy. This system has been integrated into real clinical scenarios, where radiologists were instructed to write reports based on our generated findings and anomaly segmentation masks. The results demonstrate that our system enhances the report-writing skills of junior doctors, aligning their performance more closely with senior doctors, thereby boosting overall productivity.<|reference_end|>
@article{lei2024autorg-brain:,
    title={AutoRG-Brain: Grounded Report Generation for Brain MRI},
    author={Jiayu Lei, Xiaoman Zhang, Chaoyi Wu, Lisong Dai, Ya Zhang, Yanyong
  Zhang, Yanfeng Wang, Weidi Xie and Yuehua Li},
    journal={arXiv preprint arXiv:2407.16684},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.16684},
    primaryClass={eess.IV cs.CV q-bio.NC}
}