  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.
