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| Format: | Preprint |
| Published: |
2024
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| Online Access: | https://arxiv.org/abs/2402.16165 |
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| _version_ | 1866916288204374016 |
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| author | Enyedi, Szilard |
| author_facet | Enyedi, Szilard |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_16165 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | On the Feasibility of Deep Learning Classification from Raw Signal Data in Radiology, Ultrasonography and Electrophysiology Enyedi, Szilard Systems and Control Neural and Evolutionary Computing Quantitative Methods 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. |
| title | On the Feasibility of Deep Learning Classification from Raw Signal Data in Radiology, Ultrasonography and Electrophysiology |
| topic | Systems and Control Neural and Evolutionary Computing Quantitative Methods |
| url | https://arxiv.org/abs/2402.16165 |