Introduction to digital pathology and computer-aided pathology

There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

Abstract

Digital pathology (DP) is no longer an unfamiliar term for pathologists, but it is still difficult for many pathologists to understand the engineering and mathematics concepts involved in DP. Computer-aided pathology (CAP) aids pathologists in diagnosis. However, some consider CAP a threat to the existence of pathologists and are skeptical of its clinical utility. Implementation of DP is very burdensome for pathologists because technical factors, impact on workflow, and information technology infrastructure must be considered. In this paper, various terms related to DP and computer-aided pathologic diagnosis are defined, current applications of DP are discussed, and various issues related to implementation of DP are outlined. The development of computer-aided pathologic diagnostic tools and their limitations are also discussed.

Related collections

Most cited references 45

Record: found
Abstract: found
Article: not found

Cancer immunotherapy: harnessing the immune system to battle cancer.

Yiping Yang (2015)

The recent clinical successes of immune checkpoint blockade and chimeric antigen receptor T cell therapies represent a turning point in cancer immunotherapy. These successes also underscore the importance of understanding basic tumor immunology for successful clinical translation in treating patients with cancer. The Reviews in this Review Series focus on current developments in cancer immunotherapy, highlight recent advances in our understanding of basic aspects of tumor immunology, and suggest how these insights can lead to the development of new immunotherapeutic strategies.

0 comments Cited 594 times – based on 0 reviews      Review now

Bookmark

Record: found
Abstract: found
Article: not found

Artificial intelligence in medicine.

Johanne Tremblay, Pavel Hamet (2017)

Artificial Intelligence (AI) is a general term that implies the use of a computer to model intelligent behavior with minimal human intervention. AI is generally accepted as having started with the invention of robots. The term derives from the Czech word robota, meaning biosynthetic machines used as forced labor. In this field, Leonardo Da Vinci's lasting heritage is today's burgeoning use of robotic-assisted surgery, named after him, for complex urologic and gynecologic procedures. Da Vinci's sketchbooks of robots helped set the stage for this innovation. AI, described as the science and engineering of making intelligent machines, was officially born in 1956. The term is applicable to a broad range of items in medicine such as robotics, medical diagnosis, medical statistics, and human biology-up to and including today's "omics". AI in medicine, which is the focus of this review, has two main branches: virtual and physical. The virtual branch includes informatics approaches from deep learning information management to control of health management systems, including electronic health records, and active guidance of physicians in their treatment decisions. The physical branch is best represented by robots used to assist the elderly patient or the attending surgeon. Also embodied in this branch are targeted nanorobots, a unique new drug delivery system. The societal and ethical complexities of these applications require further reflection, proof of their medical utility, economic value, and development of interdisciplinary strategies for their wider application.

0 comments Cited 405 times – based on 0 reviews      Review now

Bookmark

Record: found
Abstract: found
Article: not found

Digital pathology and artificial intelligence

Muhammad Khalid Khan Niazi, Anil V Parwani, Metin N. Gurcan (2019)

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.

0 comments Cited 292 times – based on 0 reviews      Review now

Bookmark

All references

Author and article information

Journal

Journal ID (nlm-ta): J Pathol Transl Med

Journal ID (iso-abbrev): J Pathol Transl Med

Journal ID (publisher-id): JPTM

Title: Journal of Pathology and Translational Medicine

Publisher: The Korean Society of Pathologists and the Korean Society for Cytopathology

ISSN (Print): 2383-7837

ISSN (Electronic): 2383-7845

Publication date (Print): March 2020

Publication date (Electronic): 13 February 2020

Volume: 54

Issue: 2

Pages: 125-134

Affiliations

[1 ]Department of Pathology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea

[2 ]Department of Hospital Pathology, College of Medicine, The Catholic University of Korea, Seoul, Korea

[3 ]Deep Bio Inc., Seoul, Korea

[4 ]Department of Urology, College of Medicine, The Catholic University of Korea, Seoul, Korea

[5 ]Catholic Cancer Research Institute, College of Medicine, The Catholic University of Korea, Seoul, Korea

[6 ]Department of Biomedicine & Health Sciences, College of Medicine, The Catholic University of Korea, Seoul, Korea

Author notes

Corresponding Author: Heounjeong Go, MD, PhD, Department of Pathology, Asan Medical Center, University of Ulsan College of Medicine, 88 Olympic-ro 43-gil, Seoul 05505, Korea Tel: +82-2-3010-5888, Fax: +82-2-472-7898, E-mail: damul37@ 123456amc.seoul.kr

[*]

Soojeong Nam, Yosep Chong, Chan Kwon Jung, and Tae-Yeong Kwak contributed equally to this work.

Author information

Soojeong Nam http://orcid.org/0000-0001-9376-359X

Yosep Chong http://orcid.org/0000-0001-8615-3064

Chan Kwon Jung http://orcid.org/0000-0001-6843-3708

Tae-Yeong Kwak http://orcid.org/0000-0001-9171-7258

Ji Youl Lee http://orcid.org/0000-0001-6775-1157

Jihwan Park http://orcid.org/0000-0002-4027-1038

Mi Jung Rho http://orcid.org/0000-0002-9862-0122

Heounjeong Go http://orcid.org/0000-0003-0412-8709

Article

Publisher ID: jptm-2019-12-31

DOI: 10.4132/jptm.2019.12.31

PMC ID: 7093286

PubMed ID: 32045965

SO-VID: f5364c38-8fa0-40d4-846b-91c86201b30c

License:

This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License ( http://creativecommons.org/licenses/by-nc/4.0) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

Introduction to digital pathology and computer-aided pathology

Read this article at

Abstract

Related collections

Annual Reviews AI, Machine Learning, and Society

Most cited references 45

Cancer immunotherapy: harnessing the immune system to battle cancer.

Artificial intelligence in medicine.

Digital pathology and artificial intelligence

Author and article information

Journal

Affiliations

Author notes

Author information

Article

History

Categories

Comments

Comment on this article

Similar content 75

Cited by 25

Most referenced authors 1,179