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Cited by 274AI in cancer detectionArtificial Intelligence in Healthcare and EducationRadiomics and Machine Learning in Medical Imaging

The DOI matches its Crossref record; 108 of the 108 references it cites were found in a registry or an index.

Translational AI and Deep Learning in Diagnostic Pathology

Open access
Serag, Ahmed, Ion-Margineanu, Adrian, Qureshi, Hammad, McMillan, Ryan, Saint Martin, Marie-Judith, Diamond, Jim, O'Reilly, Paul, Hamilton, Peter·2019·Frontiers in Medicine
DOI 10.3389/fmed.2019.00185PMC PMC6779702
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Abstract

There has been an exponential growth in the application of AI in health and in pathology. This is resulting in the innovation of deep learning technologies that are specifically aimed at cellular imaging and practical applications that could transform diagnostic pathology. This paper reviews the different approaches to deep learning in pathology, the public grand challenges that have driven this innovation and a range of emerging applications in pathology. The translation of AI into clinical practice will require applications to be embedded seamlessly within digital pathology workflows, driving an integrated approach to diagnostics and providing pathologists with new tools that accelerate workflow and improve diagnostic consistency and reduce errors. The clearance of digital pathology for primary diagnosis in the US by some manufacturers provides the platform on which to deliver practical AI. AI and computational pathology will continue to mature as researchers, clinicians, industry, regulatory organizations and patient advocacy groups work together to innovate and deliver new technologies to health care providers: technologies which are better, faster, cheaper, more precise, and safe.

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Crossref · 10.3389/fmed.2019.00185

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Figures

Figure 1 U-Net architecture for semantic segmentation, comprising encoder (downsampling), and decoder (upsampling) sections, and showing the skip connections between layers (in yellow).
Figure 1 U-Net architecture for semantic segmentation, comprising encoder (downsampling), and decoder (upsampling) sections, and showing the skip connections between layers (in yellow).CC BY · PubMed Central
Figure 2 GANs, Generative adversarial networks (GANs) are deep neural network architectures comprised of two networks (generator and discriminator), opposing one against the other (thus the “adversarial”). The generator takes in random numbers and returns an image. This generated image is fed into the discriminator alongside a stream of images taken from the actual, ground-truth dataset.
Figure 2 GANs, Generative adversarial networks (GANs) are deep neural network architectures comprised of two networks (generator and discriminator), opposing one against the other (thus the “adversarial”). The generator takes in random numbers and returns an image. This generated image is fed into the discriminator alongside a stream of images taken from the actual, ground-truth dataset.CC BY · PubMed Central
Figure 3 Unsupervised Learning, unsupervised anomaly detection framework. Generative adversarial training is performed on healthy data and testing is performed on unseen data.
Figure 3 Unsupervised Learning, unsupervised anomaly detection framework. Generative adversarial training is performed on healthy data and testing is performed on unseen data.CC BY · PubMed Central
Figure 4 PD-L1 imaging in lung cancer. Deep learning can be used to identify and distinguish positive | negative tumor cells and positive | negative inflammatory cells.
Figure 4 PD-L1 imaging in lung cancer. Deep learning can be used to identify and distinguish positive | negative tumor cells and positive | negative inflammatory cells.CC BY · PubMed Central
Figure 5 Illustrates the current workflow in molecular research and diagnostics. Solid tumor analysis is commonly derived from FFPE block and H&E tissue section as guide for tumor content (far left). The figure shows the need for annotation and macrodissection and the importance of tumor purity from FFPE samples for molecular profiling. Digital pathology can automate the annotation and measurement of tumor cells in H&E—providing a more objective, reliable platform for molecular pathology.
Figure 5 Illustrates the current workflow in molecular research and diagnostics. Solid tumor analysis is commonly derived from FFPE block and H&E tissue section as guide for tumor content (far left). The figure shows the need for annotation and macrodissection and the importance of tumor purity from FFPE samples for molecular profiling. Digital pathology can automate the annotation and measurement of tumor cells in H&E—providing a more objective, reliable platform for molecular pathology.CC BY · PubMed Central
Figure 6 Automated identification of colorectal tumor in H&E tissue samples using deep learning networks, showing heatmap of tumor regions (Left) and automatically generated macrodissection boundary (Right) with a product called TissueMark1.
Figure 6 Automated identification of colorectal tumor in H&E tissue samples using deep learning networks, showing heatmap of tumor regions (Left) and automatically generated macrodissection boundary (Right) with a product called TissueMark1.CC BY · PubMed Central
Figure 7 Automated analysis of cellular content in H&E using deep learning in TissueMark1. Here tumor (red) and non-tumor cells (green) can be distinguished, annotated for visual inspection and counted to reach more precise qualititive measures of % tumor across entire whole slide H&E scans in lung, colon, melanoma, breast, and prostate tissue sections.
Figure 7 Automated analysis of cellular content in H&E using deep learning in TissueMark1. Here tumor (red) and non-tumor cells (green) can be distinguished, annotated for visual inspection and counted to reach more precise qualititive measures of % tumor across entire whole slide H&E scans in lung, colon, melanoma, breast, and prostate tissue sections.CC BY · PubMed Central

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Citations

Cites

Show all 94 references

Research works printed but not linked: 6

  1. Press Announcements - FDA Allows Marketing of First Whole Slide Imaging System for Digital Pathology2017
  2. Detection of mitosis and evaluation of nuclear atypia score in breast cancer histological images RouxL 22nd International Conference on Pattern Recognition 2014. MITOS-ATYPIA Contest.2014
  3. Mitosis detection in breast cancer histology images via deep cascaded networks11606 ChenH DouQ WangX QinJ HengP-A AAAI PressProceedings of the Thirtieth AAAI Conference on Artificial Intelligence2016
  4. Automated segmentation of epithelial tissue in prostatectomy slides using deep learning BultenW LitjensGJS Hulsbergen-van de KaaCA van der LaakJ Proceedings Medical Imaging 2018: Digital Pathology.2018
  5. Towards grading gleason score using generically trained deep convolutional neural networks11637 KallenH MolinJ HeydenA LundstromC AstromK In: 2016 IEEE 13th Int Symp on Biomed Im (ISBI).2016
  6. Automatic lymphocyte detection on gastric cancer ihc images using deep learning2004 GarciaE HermozaR CastanonCB CanoL CastilloM CastannedaC IEEE 30th International Symposium on Computer Based Medical Systems (CBMS)2017

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Metadata

Authors
Serag, A., Ion-Margineanu, A., Qureshi, H., McMillan, R., Saint Martin, M.-J., Diamond, J., O'Reilly, P., & Hamilton, P.
Affiliations
Philips (United Kingdom) (GB)
Corresponding author
Hamilton, Peter
ORCID
Venue
Frontiers in Medicine
Publisher
Frontiers Media SA
Published
2019-10-01
Volume
6
Pages
185-185
ISSN
2296-858X (ISSN-L)
DOI
10.3389/fmed.2019.00185
PMC
PMC6779702
OpenAlex
W2965481926
Cited by
274
References
94
Citations by year
2026: 22 · 2025: 43 · 2024: 35 · 2023: 46 · 2022: 43 · 2021: 51 · 2020: 33
FWCI
2.06
Type
journal article
Language
en
Open access
Licence
Locations
  • Frontiers in Medicine (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Topics
  • Physical Sciences › Computer Science › Artificial Intelligence › AI in cancer detection
  • Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
  • Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Radiomics and Machine Learning in Medical Imaging
Keywords
artificial intelligence in pathology, deep learning, digital pathology, computational pathology, cell imaging, diagnostic consistency

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