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Artificial Intelligence and Digital Pathology: Challenges and Opportunities.

作者信息

Tizhoosh Hamid Reza, Pantanowitz Liron

机构信息

Kimia Lab, University of Waterloo, Canada.

Huron Digital Pathology, Engineering Department, St. Jacobs, ON, Canada.

出版信息

J Pathol Inform. 2018 Nov 14;9:38. doi: 10.4103/jpi.jpi_53_18. eCollection 2018.


DOI:10.4103/jpi.jpi_53_18
PMID:30607305
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC6289004/
Abstract

In light of the recent success of artificial intelligence (AI) in computer vision applications, many researchers and physicians expect that AI would be able to assist in many tasks in digital pathology. Although opportunities are both manifest and tangible, there are clearly many challenges that need to be overcome in order to exploit the AI potentials in computational pathology. In this paper, we strive to provide a realistic account of all challenges and opportunities of adopting AI algorithms in digital pathology from both engineering and pathology perspectives.

摘要

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本文引用的文献

[1]
Representing Medical Images With Encoded Local Projections.

IEEE Trans Biomed Eng. 2018-1-10

[2]
Histopathological Whole Slide Image Analysis Using Context-Based CBIR.

IEEE Trans Med Imaging. 2018-7

[3]
Challenges in Pathologic Staging of Renal Cell Carcinoma: A Study of Interobserver Variability Among Urologic Pathologists.

Am J Surg Pathol. 2018-9

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Imaging, Health Record, and Artificial Intelligence: Hype or Hope?

Curr Cardiol Rep. 2018-5-10

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[6]
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Cell Rep. 2018-4-3

[7]
Predicting cancer outcomes from histology and genomics using convolutional networks.

Proc Natl Acad Sci U S A. 2018-3-12

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JAMA. 2017-12-12

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Digital image analysis in breast pathology-from image processing techniques to artificial intelligence.

Transl Res. 2017-11-7

[10]
Data augmentation-assisted deep learning of hand-drawn partially colored sketches for visual search.

PLoS One. 2017-8-31

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