Medical imaging is a very useful tool in healthcare, various technologies being employed to non-invasively peek inside the human body. Deep learning with neural networks in radiology was welcome - albeit cautiously - by the radiologist community. Most of the currently deployed or researched deep learning solutions are applied on already generated images of medical scans, use the neural networks to aid in the generation of such images, or use them for identifying specific substance markers in spectrographs. This paper's author posits that if the neural networks were trained directly on the raw signals from the scanning machines, they would gain access to more nuanced information than from the already processed images, hence the training - and later, the inferences - would become more accurate. The paper presents the main current applications of deep learning in radiography, ultrasonography, and electrophysiology, and discusses whether the proposed neural network training directly on raw signals is feasible.

翻译：医学成像是医疗保健领域非常有用的工具，各种技术被用于非侵入性地观察人体内部。放射学领域对基于神经网络的深度学习持谨慎欢迎态度。当前大多数已部署或正在研究的深度学习解决方案应用于已生成的医学扫描图像、借助神经网络辅助生成此类图像，或用于识别光谱仪中的特定物质标志物。本文作者提出，如果神经网络直接使用扫描设备采集的原始信号进行训练，相较于处理后的图像，网络能够获取更细微的信息，从而提升训练及后续推理的准确性。本文介绍了深度学习在放射摄影、超声学和电生理学中的主要应用现状，并探讨了直接对原始信号进行神经网络训练的可行性。