Vassili Kovalev
Overview
Explore the profile of Vassili Kovalev including associated specialties, affiliations and a list of published articles.
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10
Citations
1006
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Recent Articles
1.
Hu B, Ye Z, Wei Z, Snezhko E, Kovalev V, Ye M
IEEE J Biomed Health Inform
. 2025 Mar;
PP.
PMID: 40031026
Segmentation of cell nuclei from three-dimensional (3D) volumetric fluorescence microscopy images is crucial for biological and clinical analyses. In recent years, convolutional neural networks have become the reliable 3D medical...
2.
Roth H, Xu Z, Tor-Diez C, Sanchez Jacob R, Zember J, Molto J, et al.
Med Image Anal
. 2022 Sep;
82:102605.
PMID: 36156419
Artificial intelligence (AI) methods for the automatic detection and quantification of COVID-19 lesions in chest computed tomography (CT) might play an important role in the monitoring and management of the...
3.
Roth H, Xu Z, Diez C, Sanchez Jacob R, Zember J, Molto J, et al.
Res Sq
. 2021 Jun;
PMID: 34100010
Artificial intelligence (AI) methods for the automatic detection and quantification of COVID-19 lesions in chest computed tomography (CT) might play an important role in the monitoring and management of the...
4.
Trukhan S, Tafintseva V, Tondel K, Grosserueschkamp F, Mosig A, Kovalev V, et al.
J Biophotonics
. 2020 May;
13(8):e201960223.
PMID: 32352634
Fourier-transform infrared (FTIR) microspectroscopy is rounding the corner to become a label-free routine method for cancer diagnosis. In order to build infrared-spectral based classifiers, infrared images need to be registered...
5.
Veta M, Heng Y, Stathonikos N, Bejnordi B, Beca F, Wollmann T, et al.
Med Image Anal
. 2019 Mar;
54:111-121.
PMID: 30861443
Tumor proliferation is an important biomarker indicative of the prognosis of breast cancer patients. Assessment of tumor proliferation in a clinical setting is a highly subjective and labor-intensive task. Previous...
6.
Bejnordi B, Veta M, van Diest P, van Ginneken B, Karssemeijer N, Litjens G, et al.
JAMA
. 2017 Dec;
318(22):2199-2210.
PMID: 29234806
Importance: Application of deep learning algorithms to whole-slide pathology images can potentially improve diagnostic accuracy and efficiency. Objective: Assess the performance of automated deep learning algorithms at detecting metastases in...
7.
Rosenthal A, Gabrielian A, Engle E, Hurt D, Alexandru S, Crudu V, et al.
J Clin Microbiol
. 2017 Sep;
55(11):3267-3282.
PMID: 28904183
The TB Portals program is an international consortium of physicians, radiologists, and microbiologists from countries with a heavy burden of drug-resistant tuberculosis working with data scientists and information technology professionals....
8.
Alilou M, Kovalev V, Taimouri V
Comput Med Imaging Graph
. 2013 Sep;
37(7-8):488-99.
PMID: 24008033
Histological tissue images typically exhibit very sophisticated spatial color patterns. It is of great clinical importance to extract qualitative and quantitative information from these images. As an ad hoc solution,...
9.
Sprindzuk M, Dmitruk A, Kovalev V, Bogush A, Tuzikov A, Liakhovski V, et al.
J Clin Med Res
. 2012 Apr;
1(5):249-61.
PMID: 22481986
Unlabelled: This article reviews the questions regarding the image evaluation of angiogeneic histological samples, particularly the ovarian epithelial cancer. Review is focused on the principles of image analysis in the...
10.
Olsen O, Thuen M, Berry M, Kovalev V, Petrou M, Goa P, et al.
J Magn Reson Imaging
. 2007 Dec;
27(1):34-42.
PMID: 18157895
Purpose: To develop and validate an objective technique for 3D segmentation of manganese-enhanced MR images of the optic nerve/tract (ON) in adult rats to improve contrast-to-noise (CNR) calculations and use...