The study presents a novel approach, based on deep learning, for diagnosis of Parkinson's disease through medical imaging. The approach includes analysis and use of the knowledge extracted by deep convolutional and recurrent neural networks when trained with medical images, such as magnetic resonance images and dopamine transporters scans. Internal representations of the trained DNNs constitute the extracted knowledge which is used in a transfer learning and domain adaptation manner, so as to create a unified framework for prediction of Parkinson's across different medical environments. A large experimental study is presented illustrating the ability of the proposed approach to effectively predict Parkinson's, using different medical image sets from real environments.

中文翻译：

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统一的深度学习方法可预测帕金森氏病

这项研究提出了一种基于深度学习的新方法，可通过医学成像诊断帕金森氏病。该方法包括分析和使用深层卷积神经网络和递归神经网络在接受医学图像（如磁共振图像和多巴胺转运蛋白扫描）训练后提取的知识。训练后的DNN的内部表示构成了提取的知识，该知识以转移学习和领域适应的方式使用，从而为在不同医学环境中预测帕金森氏症创建了一个统一的框架。提出了一项大型实验研究，阐明了所提出方法使用实际环境中不同的医学图像集有效预测帕金森氏症的能力。