人工智能在医疗保健中的民主化：两个机构之间的模型开发研究迁移学习
Democratizing Artificial Intelligence in Healthcare: A Study of Model Development Across Two Institutions Incorporating Transfer Learning
摘要：
深度学习模型的训练通常需要大量的数据，这些数据不容易作为大型精心策划的医学图像数据集用于开发应用于放射学的人工智能（AI）模型。认识到迁移学习（TL）允许来自一个机构的完全训练模型由另一个机构使用小得多的本地数据集进行微调的潜力，本报告描述了TL在开发用于基本用例的AI模型的背景下的挑战，方法和好处，左心室心肌（LVM）的分割来自4维冠状动脉计算机断层扫描血管造影术的图像。最终，我们通过比较使用随机初始化本地训练的模型预测的LVM分割与TL增强的训练的结果表明，TL发起的用例模型可以使用稀疏标签开发，并且具有可接受的性能。该过程减少了在不同机构的临床环境中构建新模型所需的时间。
英文摘要：
The training of deep learning models typically requires extensive data, which are not readily available as large well-curated medical-image datasets for development of artificial intelligence (AI) models applied in Radiology. Recognizing the potential for transfer learning (TL) to allow a fully trained model from one institution to be fine-tuned by another institution using a much small local dataset, this report describes the challenges, methodology, and benefits of TL within the context of developing an AI model for a basic use-case, segmentation of Left Ventricular Myocardium (LVM) on images from 4-dimensional coronary computed tomography angiography. Ultimately, our results from comparisons of LVM segmentation predicted by a model locally trained using random initialization, versus one training-enhanced by TL, showed that a use-case model initiated by TL can be developed with sparse labels with acceptable performance. This process reduces the time required to build a new model in the clinical environment at a different institution.
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。
