Automated Labelling using an Attention model for Radiology reports of MRI scans (ALARM)
School of Biomedical Engineering, King’s College London（伦敦国王学院生物医学工程学院）；King’s College Hospital（国王学院医院）；Institute of Psychiatry, Psychology & Neuroscience, King’s College London（伦敦国王学院精神病学、心理学与神经科学研究所）；Centre for Medical Image Computing, Dementia Research, University College London（伦敦大学学院医学影像计算中心（痴呆研究））
英文摘要：
Labelling large datasets for training high-capacity neural networks is a major obstacle to the development of deep learning-based medical imaging applications. Here we present a transformer-based network for magnetic resonance imaging (MRI) radiology report classification which automates this task by assigning image labels on the basis of free-text expert radiology reports. Our model's performance is comparable to that of an expert radiologist, and better than that of an expert physician, demonstrating the feasibility of this approach. We make code available online for researchers to label their own MRI datasets for medical imaging applications.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
