Literature DB >> 30773224

[Neural network: A future in pathology?]

Ryad Zemouri1, Christine Devalland2, Séverine Valmary-Degano3, Noureddine Zerhouni4.   

Abstract

Artificial Intelligence, in particular deep neural networks are the most used machine learning technics in the biomedical field. Artificial neural networks are inspired by the biological neurons; they are interconnected and follow mathematical models. Two phases are required: a learning and a using phase. The two main applications are classification and regression Computer tools such as GPU computational accelerators or some development tools such as MATLAB libraries are used. Their application field is vast and allows the management of big data in genomics and molecular biology as well as the automated analysis of histological slides. The Whole Slide Image scanner can acquire and store slides in the form of digital images. This scanning associated with deep learning algorithms allows automatic recognition of lesions through the automatic recognition of regions of interest previously validated by the pathologist. These computer aided diagnosis techniques are tested in particular in mammary pathology and dermatopathology. They will allow an efficient and a more comprehensive vision, and will provide diagnosis assistance in pathology by correlating several biomedical data such as clinical, radiological and molecular biology data.
Copyright © 2019 Elsevier Masson SAS. All rights reserved.

Entities:  

Keywords:  Artificial network; Artificial neural networks; Computer-assisted diagnosis; Diagnostic assisté par ordinateur; Digital pathology; Intelligence artificielle; Pathologie numérique; Réseaux de neurones artificiels

Mesh:

Year:  2019        PMID: 30773224     DOI: 10.1016/j.annpat.2019.01.004

Source DB:  PubMed          Journal:  Ann Pathol        ISSN: 0242-6498            Impact factor:   0.407


  6 in total

1.  Integrating Artificial Intelligence for Clinical and Laboratory Diagnosis - a Review.

Authors:  Taran Rishit Undru; Utkarsha Uday; Jyothi Tadi Lakshmi; Ariyanachi Kaliappan; Saranya Mallamgunta; Shalam Sheerin Nikhat; V Sakthivadivel; Archana Gaur
Journal:  Maedica (Bucur)       Date:  2022-06

2.  Forecast of Dengue Cases in 20 Chinese Cities Based on the Deep Learning Method.

Authors:  Jiucheng Xu; Keqiang Xu; Zhichao Li; Fengxia Meng; Taotian Tu; Lei Xu; Qiyong Liu
Journal:  Int J Environ Res Public Health       Date:  2020-01-10       Impact factor: 3.390

Review 3.  Artificial intelligence (AI) in medicine, current applications and future role with special emphasis on its potential and promise in pathology: present and future impact, obstacles including costs and acceptance among pathologists, practical and philosophical considerations. A comprehensive review.

Authors:  Zubair Ahmad; Shabina Rahim; Maha Zubair; Jamshid Abdul-Ghafar
Journal:  Diagn Pathol       Date:  2021-03-17       Impact factor: 2.644

4.  Cloud Computing-Based Framework for Breast Cancer Diagnosis Using Extreme Learning Machine.

Authors:  Vivek Lahoura; Harpreet Singh; Ashutosh Aggarwal; Bhisham Sharma; Mazin Abed Mohammed; Robertas Damaševičius; Seifedine Kadry; Korhan Cengiz
Journal:  Diagnostics (Basel)       Date:  2021-02-04

5.  Conventional Machine Learning versus Deep Learning for Magnification Dependent Histopathological Breast Cancer Image Classification: A Comparative Study with Visual Explanation.

Authors:  Said Boumaraf; Xiabi Liu; Yuchai Wan; Zhongshu Zheng; Chokri Ferkous; Xiaohong Ma; Zhuo Li; Dalal Bardou
Journal:  Diagnostics (Basel)       Date:  2021-03-16

Review 6.  Application of Artificial Intelligence in Medicine: An Overview.

Authors:  Peng-Ran Liu; Lin Lu; Jia-Yao Zhang; Tong-Tong Huo; Song-Xiang Liu; Zhe-Wei Ye
Journal:  Curr Med Sci       Date:  2021-12-06
  6 in total

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