MIAP – Web-based platform for the computer analysis of microscopic images to support the pathological diagnosis-Reference-Cited by-同舟云学术

MIAP – Web-based platform for the computer analysis of microscopic images to support the pathological diagnosis

Published:2016 Issue:4 Volume:36 Page:597-609
ISSN:0208-5216
Container-title:Biocybernetics and Biomedical Engineering
language:en
Short-container-title:Biocybernetics and Biomedical Engineering

Author:

Markiewicz Tomasz,Korzynska Anna,Kowalski Andrzej,Swiderska-Chadaj Zaneta,Murawski Piotr,Grala Bartlomiej,Lorent Malgorzata,Wdowiak Marek,Zak Jakub,Roszkowiak Lukasz,Kozlowski Wojciech,Pijanowska Dorota

Funder

National Centre for Research and Development, Poland

Publisher

Elsevier BV

Subject

Biomedical Engineering

Reference53 articles.

1. New trends of emerging technologies in digital pathology;Bueno;Pathobiology,2016

2. Review of imaging solutions for integrated quantitative immunohistochemistry in the pathology daily practice;Gracia Rojo;Folia Histochem Cytobiol,2009

3. Robust nuclei/cell detection and segmentation for digital pathology and microscopic images: a comprehensive review;Xinga;IEEE Rev Biomed Eng,2016

4. A novel approach for detection and delineation of cell nuclei using feature similarity index measure;Jisha;Biocybern Biomed Eng,2015

Cited by 16 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. A balanced hybrid cuckoo search algorithm for microscopic image segmentation;Soft Computing;2023-10-03

2. A review of current systems for annotation of cell and tissue images in digital pathology;BIOCYBERN BIOMED ENG;2021

3. A review of current systems for annotation of cell and tissue images in digital pathology;Biocybernetics and Biomedical Engineering;2021-10

4. Clustered nuclei splitting based on recurrent distance transform in digital pathology images;EURASIP Journal on Image and Video Processing;2020-07-01

5. A Comprehensive Review for Breast Histopathology Image Analysis Using Classical and Deep Neural Networks;IEEE Access;2020