Literature DB >> 31044723

Digital pathology and artificial intelligence.

Muhammad Khalid Khan Niazi1, Anil V Parwani2, Metin N Gurcan3.   

Abstract

In modern clinical practice, digital pathology has a crucial role and is increasingly a technological requirement in the scientific laboratory environment. The advent of whole-slide imaging, availability of faster networks, and cheaper storage solutions has made it easier for pathologists to manage digital slide images and share them for clinical use. In parallel, unprecedented advances in machine learning have enabled the synergy of artificial intelligence and digital pathology, which offers image-based diagnosis possibilities that were once limited only to radiology and cardiology. Integration of digital slides into the pathology workflow, advanced algorithms, and computer-aided diagnostic techniques extend the frontiers of the pathologist's view beyond a microscopic slide and enable true utilisation and integration of knowledge that is beyond human limits and boundaries, and we believe there is clear potential for artificial intelligence breakthroughs in the pathology setting. In this Review, we discuss advancements in digital slide-based image diagnosis for cancer along with some challenges and opportunities for artificial intelligence in digital pathology.
Copyright © 2019 Elsevier Ltd. All rights reserved.

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Year:  2019        PMID: 31044723      PMCID: PMC8711251          DOI: 10.1016/S1470-2045(19)30154-8

Source DB:  PubMed          Journal:  Lancet Oncol        ISSN: 1470-2045            Impact factor:   41.316


  29 in total

1.  An Automatic Learning-Based Framework for Robust Nucleus Segmentation.

Authors:  Fuyong Xing; Yuanpu Xie; Lin Yang
Journal:  IEEE Trans Med Imaging       Date:  2015-09-23       Impact factor: 10.048

2.  Computer-aided detection of centroblasts for follicular lymphoma grading using adaptive likelihood-based cell segmentation.

Authors:  Olcay Sertel; Gerard Lozanski; Arwa Shana'ah; Metin N Gurcan
Journal:  IEEE Trans Biomed Eng       Date:  2010-06-28       Impact factor: 4.538

3.  Computational pathology: an emerging definition.

Authors:  David N Louis; Georg K Gerber; Jason M Baron; Lyn Bry; Anand S Dighe; Gad Getz; John M Higgins; Frank C Kuo; William J Lane; James S Michaelson; Long P Le; Craig H Mermel; John R Gilbertson; Jeffrey A Golden
Journal:  Arch Pathol Lab Med       Date:  2014-09       Impact factor: 5.534

4.  AggNet: Deep Learning From Crowds for Mitosis Detection in Breast Cancer Histology Images.

Authors:  Shadi Albarqouni; Christoph Baur; Felix Achilles; Vasileios Belagiannis; Stefanie Demirci; Nassir Navab
Journal:  IEEE Trans Med Imaging       Date:  2016-02-11       Impact factor: 10.048

5.  Context-aware stacked convolutional neural networks for classification of breast carcinomas in whole-slide histopathology images.

Authors:  Babak Ehteshami Bejnordi; Guido Zuidhof; Maschenka Balkenhol; Meyke Hermsen; Peter Bult; Bram van Ginneken; Nico Karssemeijer; Geert Litjens; Jeroen van der Laak
Journal:  J Med Imaging (Bellingham)       Date:  2017-12-14

Review 6.  Mapping spatial heterogeneity in the tumor microenvironment: a new era for digital pathology.

Authors:  Andreas Heindl; Sidra Nawaz; Yinyin Yuan
Journal:  Lab Invest       Date:  2015-01-19       Impact factor: 5.662

7.  An automated blur detection method for histological whole slide imaging.

Authors:  Xavier Moles Lopez; Etienne D'Andrea; Paul Barbot; Anne-Sophie Bridoux; Sandrine Rorive; Isabelle Salmon; Olivier Debeir; Christine Decaestecker
Journal:  PLoS One       Date:  2013-12-13       Impact factor: 3.240

8.  Deep learning as a tool for increased accuracy and efficiency of histopathological diagnosis.

Authors:  Geert Litjens; Clara I Sánchez; Nadya Timofeeva; Meyke Hermsen; Iris Nagtegaal; Iringo Kovacs; Christina Hulsbergen-van de Kaa; Peter Bult; Bram van Ginneken; Jeroen van der Laak
Journal:  Sci Rep       Date:  2016-05-23       Impact factor: 4.379

9.  Microenvironmental Heterogeneity Parallels Breast Cancer Progression: A Histology-Genomic Integration Analysis.

Authors:  Rachael Natrajan; Heba Sailem; Faraz K Mardakheh; Mar Arias Garcia; Christopher J Tape; Mitch Dowsett; Chris Bakal; Yinyin Yuan
Journal:  PLoS Med       Date:  2016-02-16       Impact factor: 11.069

10.  Nuclear IHC enumeration: A digital phantom to evaluate the performance of automated algorithms in digital pathology.

Authors:  Muhammad Khalid Khan Niazi; Fazly Salleh Abas; Caglar Senaras; Michael Pennell; Berkman Sahiner; Weijie Chen; John Opfer; Robert Hasserjian; Abner Louissaint; Arwa Shana'ah; Gerard Lozanski; Metin N Gurcan
Journal:  PLoS One       Date:  2018-05-10       Impact factor: 3.240

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  121 in total

1.  The Presence and Location of Podocytes in Glomeruli as Affected by Diabetes Mellitus.

Authors:  Kathryn E Maraszek; Briana A Santo; Rabi Yacoub; John E Tomaszewski; Imtiaz Mohammad; Amber M Worral; Pinaki Sarder
Journal:  Proc SPIE Int Soc Opt Eng       Date:  2020-03-16

Review 2.  AI applications in renal pathology.

Authors:  Yuankai Huo; Ruining Deng; Quan Liu; Agnes B Fogo; Haichun Yang
Journal:  Kidney Int       Date:  2021-02-10       Impact factor: 10.612

Review 3.  Artificial intelligence and algorithmic computational pathology: an introduction with renal allograft examples.

Authors:  Alton B Farris; Juan Vizcarra; Mohamed Amgad; Lee A D Cooper; David Gutman; Julien Hogan
Journal:  Histopathology       Date:  2021-03-08       Impact factor: 5.087

Review 4.  Harnessing non-destructive 3D pathology.

Authors:  Jonathan T C Liu; Adam K Glaser; Kaustav Bera; Lawrence D True; Nicholas P Reder; Kevin W Eliceiri; Anant Madabhushi
Journal:  Nat Biomed Eng       Date:  2021-02-15       Impact factor: 25.671

5.  Data-efficient and weakly supervised computational pathology on whole-slide images.

Authors:  Drew F K Williamson; Tiffany Y Chen; Ming Y Lu; Richard J Chen; Matteo Barbieri; Faisal Mahmood
Journal:  Nat Biomed Eng       Date:  2021-03-01       Impact factor: 25.671

Review 6.  Deep learning powers cancer diagnosis in digital pathology.

Authors:  Yunjie He; Hong Zhao; Stephen T C Wong
Journal:  Comput Med Imaging Graph       Date:  2020-12-11       Impact factor: 4.790

7.  Deep Learning Model for Real-Time Prediction of Intradialytic Hypotension.

Authors:  Hojun Lee; Donghwan Yun; Jayeon Yoo; Kiyoon Yoo; Yong Chul Kim; Dong Ki Kim; Kook-Hwan Oh; Kwon Wook Joo; Yon Su Kim; Nojun Kwak; Seung Seok Han
Journal:  Clin J Am Soc Nephrol       Date:  2021-02-11       Impact factor: 8.237

Review 8.  Artificial Intelligence in Pathology: From Prototype to Product.

Authors:  André Homeyer; Johannes Lotz; Lars Ole Schwen; Nick Weiss; Daniel Romberg; Henning Höfener; Norman Zerbe; Peter Hufnagl
Journal:  J Pathol Inform       Date:  2021-03-22

9.  What makes AI 'intelligent' and 'caring'? Exploring affect and relationality across three sites of intelligence and care.

Authors:  Giulia De Togni; Sonja Erikainen; Sarah Chan; Sarah Cunningham-Burley
Journal:  Soc Sci Med       Date:  2021-03-23       Impact factor: 4.634

Review 10.  Multiplex Immunofluorescence and Multispectral Imaging: Forming the Basis of a Clinical Test Platform for Immuno-Oncology.

Authors:  Clifford C Hoyt
Journal:  Front Mol Biosci       Date:  2021-06-02
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