论文标题
在医疗保健中民主化人工智能：一项对纳入转移学习的机构的模型开发的研究
Democratizing Artificial Intelligence in Healthcare: A Study of Model Development Across Two Institutions Incorporating Transfer Learning
论文作者
论文摘要
深度学习模型的培训通常需要广泛的数据，这些数据不容易作为用于开发人工智能（AI）模型的大型医疗图像数据集（AI）模型。认识到转移学习的潜力（TL），可以使用一个小的本地数据集对一个机构进行全面训练的模型进行微调，该报告描述了TL在为基本用例的AI模型开发AI模型中的挑战，方法和益处，该模型是基本用例，对左心室心肌（LVM）的分割（LVM），对4Dimenary coronary coronary coronary cornary cormonary compy tomathy tomathiphate sementation。最终，我们从使用随机初始化的局部训练的模型与TL训练增强的模型预测的LVM分割的比较结果表明，TL启动的用例模型可以具有可接受性能的稀疏标签。这个过程减少了在不同机构的临床环境中建立新模型所需的时间。
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.
