FiCRoN, a deep learning-based algorithm for the automatic determination of intracellular parasite burden from fluorescence microscopy images-Reference-Cited by-同舟云学术

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FiCRoN, a deep learning-based algorithm for the automatic determination of intracellular parasite burden from fluorescence microscopy images

Published:2024-01 Issue: Volume:91 Page:103036
ISSN:1361-8415
Container-title:Medical Image Analysis
language:en
Short-container-title:Medical Image Analysis

Author:

Juez-Castillo Graciela^ORCID,Valencia-Vidal Brayan^ORCID,Orrego Lina M.,Cabello-Donayre María^ORCID,Montosa-Hidalgo Laura,Pérez-Victoria José M.^ORCID

Funder

Consejo Superior de Investigaciones Científicas

Universidad de Granada

European Commission

Junta de Andalucía

Kementerian Sains, Teknologi dan Inovasi

Publisher

Elsevier BV

Subject

Computer Graphics and Computer-Aided Design,Health Informatics,Computer Vision and Pattern Recognition,Radiology, Nuclear Medicine and imaging,Radiological and Ultrasound Technology

Reference49 articles.

1. TensorFlow: Large-scale machine learning on heterogeneous systems;Abadi,2015

2. Automated cell counts on tissue sections by deep learning and unbiased stereology;Alahmari;J. Chem. Neuroanat.,2019

3. Leishmania heme uptake involves lmflvcrb, a novel porphyrin transporter essential for the parasite;Cabello-Donayre;Cellular and Molecular Life Sciences,2020

4. LABCG2, a new ABC transporter implicated in phosphatidylserine exposure, is involved in the infectivity and pathogenicity of Leishmania;Campos-Salinas;PLoS Neglect. Trop. D.,2013

5. A review on multiscale-deep-learning applications;Elizar;Sensors,2022

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