Literature DB >> 32045965

Introduction to digital pathology and computer-aided pathology.

Soojeong Nam1, Yosep Chong2, Chan Kwon Jung2, Tae-Yeong Kwak3, Ji Youl Lee4, Jihwan Park5,6, Mi Jung Rho5, Heounjeong Go1.   

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.

Entities:  

Keywords:  Artificial intelligence; Computer-aided pathology; Deep learning; Digital pathology

Year:  2020        PMID: 32045965     DOI: 10.4132/jptm.2019.12.31

Source DB:  PubMed          Journal:  J Pathol Transl Med        ISSN: 2383-7837


  16 in total

1.  A novel evaluation method for Ki-67 immunostaining in paraffin-embedded tissues.

Authors:  Eliane Pedra Dias; Nathália Silva Carlos Oliveira; Amanda Oliveira Serra-Campos; Anna Karoline Fausto da Silva; Licínio Esmeraldo da Silva; Karin Soares Cunha
Journal:  Virchows Arch       Date:  2021-01-19       Impact factor: 4.064

2.  Mass spectrometry imaging to explore molecular heterogeneity in cell culture.

Authors:  Tanja Bien; Krischan Koerfer; Jan Schwenzfeier; Klaus Dreisewerd; Jens Soltwisch
Journal:  Proc Natl Acad Sci U S A       Date:  2022-07-11       Impact factor: 12.779

3.  Hyperspectral Microscopic Imaging for the Detection of Head and Neck Squamous Cell Carcinoma on Histologic Slides.

Authors:  Ling Ma; Ximing Zhou; James V Little; Amy Y Chen; Larry L Myers; Baran D Sumer; Baowei Fei
Journal:  Proc SPIE Int Soc Opt Eng       Date:  2021-02-15

4.  A Matched-Pair Analysis of Nuclear Morphologic Features Between Core Needle Biopsy and Surgical Specimen in Thyroid Tumors Using a Deep Learning Model.

Authors:  Faridul Haq; Andrey Bychkov; Chan Kwon Jung
Journal:  Endocr Pathol       Date:  2022-10-14       Impact factor: 4.056

5.  The challenges of colposcopy for cervical cancer screening in LMICs and solutions by artificial intelligence.

Authors:  Peng Xue; Man Tat Alexander Ng; Youlin Qiao
Journal:  BMC Med       Date:  2020-06-03       Impact factor: 8.775

6.  Diagnosis prediction of tumours of unknown origin using ImmunoGenius, a machine learning-based expert system for immunohistochemistry profile interpretation.

Authors:  Yosep Chong; Nishant Thakur; Ji Young Lee; Gyoyeon Hwang; Myungjin Choi; Yejin Kim; Hwanjo Yu; Mee Yon Cho
Journal:  Diagn Pathol       Date:  2021-03-11       Impact factor: 2.644

Review 7.  Digital pathology and artificial intelligence in translational medicine and clinical practice.

Authors:  Vipul Baxi; Robin Edwards; Michael Montalto; Saurabh Saha
Journal:  Mod Pathol       Date:  2021-10-05       Impact factor: 7.842

8.  Prediction of genetic alterations from gastric cancer histopathology images using a fully automated deep learning approach.

Authors:  Hyun-Jong Jang; Ahwon Lee; Jun Kang; In Hye Song; Sung Hak Lee
Journal:  World J Gastroenterol       Date:  2021-11-28       Impact factor: 5.742

9.  Digital Dermatopathology and Its Application to Mohs Micrographic Surgery.

Authors:  Yeongjoo Oh; Hye Min Kim; Soon Won Hong; Eunah Shin; Jihee Kim; Yoon Jung Choi
Journal:  Yonsei Med J       Date:  2022-01       Impact factor: 2.759

10.  Classification of Mouse Lung Metastatic Tumor with Deep Learning.

Authors:  Ha Neul Lee; Hong-Deok Seo; Eui-Myoung Kim; Beom Seok Han; Jin Seok Kang
Journal:  Biomol Ther (Seoul)       Date:  2022-03-01       Impact factor: 4.634

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