1. Potential use of deep learning techniques for postmortem imaging
- Author
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Raffael Affolter, Garyfalia Ampanozi, Summer J. Decker, Till Sieberth, Lars C. Ebert, Sabine Franckenberg, Akos Dobay, Jonathan Ford, University of Zurich, and Dobay, Akos
- Subjects
medicine.medical_specialty ,Computer science ,ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION ,Computed tomography ,030218 nuclear medicine & medical imaging ,Pathology and Forensic Medicine ,Standard procedure ,03 medical and health sciences ,Deep Learning ,0302 clinical medicine ,medicine ,Humans ,Whole Body Imaging ,Medical physics ,030216 legal & forensic medicine ,medicine.diagnostic_test ,business.industry ,Deep learning ,Conventional autopsy ,General Medicine ,Forensic Medicine ,10218 Institute of Legal Medicine ,2734 Pathology and Forensic Medicine ,Postmortem radiology ,Postmortem Changes ,570 Life sciences ,biology ,590 Animals (Zoology) ,Autopsy ,Neural Networks, Computer ,Artificial intelligence ,Tomography, X-Ray Computed ,business - Abstract
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.
- Published
- 2020
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