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Potential use of deep learning techniques for postmortem imaging
The use of postmortem computed tomography in forensic medicine, in addition to conventional autopsy, is now a standard procedure in several countries. However, the large number of cases, the large amount of data, and the lack of postmortem radiology experts have pushed researchers to develop solutio...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer US
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7669812/ https://www.ncbi.nlm.nih.gov/pubmed/32990926 http://dx.doi.org/10.1007/s12024-020-00307-3 |
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author | Dobay, Akos Ford, Jonathan Decker, Summer Ampanozi, Garyfalia Franckenberg, Sabine Affolter, Raffael Sieberth, Till Ebert, Lars C. |
author_facet | Dobay, Akos Ford, Jonathan Decker, Summer Ampanozi, Garyfalia Franckenberg, Sabine Affolter, Raffael Sieberth, Till Ebert, Lars C. |
author_sort | Dobay, Akos |
collection | PubMed |
description | The use of postmortem computed tomography in forensic medicine, in addition to conventional autopsy, is now a standard procedure in several countries. However, the large number of cases, the large amount of data, and the lack of postmortem radiology experts have pushed researchers to develop solutions that are able to automate diagnosis by applying deep learning techniques to postmortem computed tomography images. While deep learning techniques require a good understanding of image analysis and mathematical optimization, the goal of this review was to provide to the community of postmortem radiology experts the key concepts needed to assess the potential of such techniques and how they could impact their work. |
format | Online Article Text |
id | pubmed-7669812 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Springer US |
record_format | MEDLINE/PubMed |
spelling | pubmed-76698122020-11-17 Potential use of deep learning techniques for postmortem imaging Dobay, Akos Ford, Jonathan Decker, Summer Ampanozi, Garyfalia Franckenberg, Sabine Affolter, Raffael Sieberth, Till Ebert, Lars C. Forensic Sci Med Pathol Review The use of postmortem computed tomography in forensic medicine, in addition to conventional autopsy, is now a standard procedure in several countries. However, the large number of cases, the large amount of data, and the lack of postmortem radiology experts have pushed researchers to develop solutions that are able to automate diagnosis by applying deep learning techniques to postmortem computed tomography images. While deep learning techniques require a good understanding of image analysis and mathematical optimization, the goal of this review was to provide to the community of postmortem radiology experts the key concepts needed to assess the potential of such techniques and how they could impact their work. Springer US 2020-09-29 2020 /pmc/articles/PMC7669812/ /pubmed/32990926 http://dx.doi.org/10.1007/s12024-020-00307-3 Text en © The Author(s) 2020 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. |
spellingShingle | Review Dobay, Akos Ford, Jonathan Decker, Summer Ampanozi, Garyfalia Franckenberg, Sabine Affolter, Raffael Sieberth, Till Ebert, Lars C. Potential use of deep learning techniques for postmortem imaging |
title | Potential use of deep learning techniques for postmortem imaging |
title_full | Potential use of deep learning techniques for postmortem imaging |
title_fullStr | Potential use of deep learning techniques for postmortem imaging |
title_full_unstemmed | Potential use of deep learning techniques for postmortem imaging |
title_short | Potential use of deep learning techniques for postmortem imaging |
title_sort | potential use of deep learning techniques for postmortem imaging |
topic | Review |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7669812/ https://www.ncbi.nlm.nih.gov/pubmed/32990926 http://dx.doi.org/10.1007/s12024-020-00307-3 |
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