Potential use of deep learning techniques for postmortem imaging

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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.

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Most cited references 57

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Dermatologist-level classification of skin cancer with deep neural networks

Andre Esteva, Brett Kuprel, Roberto Chacón-Novoa … (2017)

Skin cancer, the most common human malignancy, is primarily diagnosed visually, beginning with an initial clinical screening and followed potentially by dermoscopic analysis, a biopsy and histopathological examination. Automated classification of skin lesions using images is a challenging task owing to the fine-grained variability in the appearance of skin lesions. Deep convolutional neural networks (CNNs) show potential for general and highly variable tasks across many fine-grained object categories. Here we demonstrate classification of skin lesions using a single CNN, trained end-to-end from images directly, using only pixels and disease labels as inputs. We train a CNN using a dataset of 129,450 clinical images—two orders of magnitude larger than previous datasets—consisting of 2,032 different diseases. We test its performance against 21 board-certified dermatologists on biopsy-proven clinical images with two critical binary classification use cases: keratinocyte carcinomas versus benign seborrheic keratoses; and malignant melanomas versus benign nevi. The first case represents the identification of the most common cancers, the second represents the identification of the deadliest skin cancer. The CNN achieves performance on par with all tested experts across both tasks, demonstrating an artificial intelligence capable of classifying skin cancer with a level of competence comparable to dermatologists. Outfitted with deep neural networks, mobile devices can potentially extend the reach of dermatologists outside of the clinic. It is projected that 6.3 billion smartphone subscriptions will exist by the year 2021 (ref. 13) and can therefore potentially provide low-cost universal access to vital diagnostic care.

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A survey on deep learning in medical image analysis

Geert Litjens, Arnaud Arindra Adiyoso Setio, Thijs Kooi … (2017)

Deep learning algorithms, in particular convolutional networks, have rapidly become a methodology of choice for analyzing medical images. This paper reviews the major deep learning concepts pertinent to medical image analysis and summarizes over 300 contributions to the field, most of which appeared in the last year. We survey the use of deep learning for image classification, object detection, segmentation, registration, and other tasks. Concise overviews are provided of studies per application area: neuro, retinal, pulmonary, digital pathology, breast, cardiac, abdominal, musculoskeletal. We end with a summary of the current state-of-the-art, a critical discussion of open challenges and directions for future research.

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Author and article information

Contributors

Akos Dobay:

ORCID: https://orcid.org/0000-0001-6492-9298

akos.dobay@uzh.ch

Journal

Journal ID (nlm-ta): Forensic Sci Med Pathol

Journal ID (iso-abbrev): Forensic Sci Med Pathol

Title: Forensic Science, Medicine, and Pathology

Publisher: Springer US (New York )

ISSN (Print): 1547-769X

ISSN (Electronic): 1556-2891

Publication date (Electronic): 29 September 2020

Publication date PMC-release: 29 September 2020

Publication date (Print): 2020

Volume: 16

Issue: 4

Pages: 671-679

Affiliations

[1 ]GRID grid.7400.3, ISNI 0000 0004 1937 0650, Zurich Institute of Forensic Medicine, , University of Zurich, ; Winterthurerstrasse 190/52, CH-8057 Zurich, Switzerland

[2 ]GRID grid.7400.3, ISNI 0000 0004 1937 0650, Department of Evolutionary Biology and Environmental Studies, , University of Zurich, ; Winterthurerstrasse 190, CH-8057 Zurich, Switzerland

[3 ]GRID grid.170693.a, ISNI 0000 0001 2353 285X, Department of Radiology, , University of South Florida Morsani College of Medicine, ; 2 Tampa General Circle STC 6097, Tampa, FL 33606 USA

[4 ]GRID grid.412004.3, ISNI 0000 0004 0478 9977, Institute of Diagnostic and Interventional Radiology, , University Hospital Zurich, ; Raemistrasse 100, 8091 Zurich, Switzerland

Author information

Akos Dobay https://orcid.org/0000-0001-6492-9298

Article

Publisher ID: 307

DOI: 10.1007/s12024-020-00307-3

PMC ID: 7669812

PubMed ID: 32990926

SO-VID: b73bba1c-b388-4449-be08-4c281dcc4f05

License:

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/.

History

Date accepted : 30 August 2020

Funding

Funded by: Emma Louise Kessler Foundation (CH)

Award ID: not applicable

Award Recipient : Akos Dobay

Custom metadata

ScienceOpen disciplines: Forensic science

Keywords: deep learning,convolutional neural networks,computed tomography,forensic sciences,pmct

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ScienceOpen disciplines: Forensic science

Keywords: deep learning, convolutional neural networks, computed tomography, forensic sciences, pmct

Potential use of deep learning techniques for postmortem imaging

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Arab Journal of Forensic Sciences & Forensic Medicine (AJFSFM)

Most cited references 57

ImageNet classification with deep convolutional neural networks

Dermatologist-level classification of skin cancer with deep neural networks

A survey on deep learning in medical image analysis

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