On the Feasibility of Deep Learning Classification from Raw Signal Data in Radiology, Ultrasonography and Electrophysiology
arXiv:2402.16165 · doi:10.1109/AQTR61889.2024.10554095
Abstract
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.
Updated 2024.04.14 17:49+3: accepted to the conference, so added IEEE copyright notice to the first page, as per "IEEE Publication Services and Products Board Operations Manual 2024"'s subsection 8.1.9. point D. 6 pages, 5 figures, 1 table, 56 references, submitted to AQTR 2024