RTU Zinātniskās darbības rezultāti un resursi: Personāls

Laser Speckle Imaging Reveals Bacterial Activity within Colony
(2021)

Ilya Balmages, Janis Liepins, Dmitrijs Bļizņuks, Stivens Zolins, Ilze Lihacova, Alexey Lihachov
Zinātniskais raksts, kas indeksēts Web of science un/vai Scopus datu bāzē

Deep Learning Model Deploying on Embedded Skin Cancer Diagnostic Device
(2020)

Dmitrijs Bļizņuks, Evija Cibuļska, Andrejs Bondarenko, Jurijs Čižovs, Ilze Lihacova
Publikācijas konferenču materiālos, kas ir indeksēti Web of Science un/vai SCOPUS

Use of Machine Learning Approaches to Improve Non-invasive Skin Melanoma Diagnostic Method in Spectral Range 450 - 950nm
(2020)

Andrejs Bondarenko, Jurijs Čižovs, Dilshat Uteshev, Dmitrijs Bļizņuks, Alexey Lihachev, Ilze Lihacova
Publikācijas konferenču materiālos, kas ir indeksēti Web of Science un/vai SCOPUS

Identification of the Most Informative Wavelengths for Non-invasive Melanoma Diagnostics in Spectral Region from 450 to 950 nm
(2020)

Dmitrijs Bļizņuks, Jurijs Čižovs, Andrejs Bondarenko, Dilshat Uteshev, Alexey Lihachev, Ilze Lihacova
Publikācijas konferenču materiālos, kas ir indeksēti Web of Science un/vai SCOPUS

Challenges of Automatic Processing of Large Amount of Skin Lesion Multispectral Data
(2020)

Ilze Lihacova, Evija Cibuļska, Alexey Lihachev, Marta Lanģe, Emilija-Vija Ploriņa, Dmitrijs Bļizņuks, Aleksandrs Derjabo, Norbert Kiss
Publikācijas konferenču materiālos, kas ir indeksēti Web of Science un/vai SCOPUS

Embedded Neural Network System for Microorganisms Growth Analysis
(2020)

Dmitrijs Bļizņuks, Jurijs Čižovs, Andrejs Bondarenko, Dilshat Uteshev, Jānis Liepiņš, Stivens Zoļins, Alexey Lihachev, Ilze Lihacova
Publikācijas konferenču materiālos, kas ir indeksēti Web of Science un/vai SCOPUS

Multispectral and Autofluorescence RGB Imaging for Skin Cancer Diagnostics
(2019)

Vanesa Lukinsone, Ilona Kuzmina, Rita Veilande, Dmitrijs Bļizņuks, Katrīna Šmite, Emilija Vija Plorina, Alexander Derjabo, Ilze Lihacova, Janis Spiguls
Publikācijas konferenču materiālos, kas ir indeksēti Web of Science un/vai SCOPUS

Towards to Deep Neural Network Application with Limited Training Data: Synthesis of Melanoma's Diffuse Reflectance Spectral Images
(2019)

Katrīna Šmite, Dmitrijs Bļizņuks, Dilshat Uteshev, Ilze Lihacova, Aleksey Lihachev, Jurijs Čižovs, Andrey Bondarenko
Publikācijas konferenču materiālos, kas ir indeksēti Web of Science un/vai SCOPUS

Imaging of LED-Excited Autofluorescence Photobleaching Rates for Skin Lesion Diagnostics
(2019)

Aleksey Lihachev, Emilija Vija Plorina, Marta Lange, Ilze Lihacova, Alexander Derjabo, Dmitrijs Bļizņuks
Publikācijas konferenču materiālos, kas ir indeksēti Web of Science un/vai SCOPUS

Quality Enhancement of Multispectral Images for Skin Cancer Optical Diagnostics
(2018)

Katrīna Šmite, Dmitrijs Bļizņuks, Ilze Lihacova, Aleksejs Lihachov
Publikācijas konferenču materiālos, kas ir indeksēti Web of Science un/vai SCOPUS

Informācija par autoru

Publikācijas

Laser Speckle Imaging Reveals Bacterial Activity within Colony (2021)

Deep Learning Model Deploying on Embedded Skin Cancer Diagnostic Device (2020)

Use of Machine Learning Approaches to Improve Non-invasive Skin Melanoma Diagnostic Method in Spectral Range 450 - 950nm (2020)

Identification of the Most Informative Wavelengths for Non-invasive Melanoma Diagnostics in Spectral Region from 450 to 950 nm (2020)

Challenges of Automatic Processing of Large Amount of Skin Lesion Multispectral Data (2020)

Embedded Neural Network System for Microorganisms Growth Analysis (2020)

Multispectral and Autofluorescence RGB Imaging for Skin Cancer Diagnostics (2019)

Towards to Deep Neural Network Application with Limited Training Data: Synthesis of Melanoma's Diffuse Reflectance Spectral Images (2019)

Imaging of LED-Excited Autofluorescence Photobleaching Rates for Skin Lesion Diagnostics (2019)

Quality Enhancement of Multispectral Images for Skin Cancer Optical Diagnostics (2018)

Laser Speckle Imaging Reveals Bacterial Activity within Colony
(2021)

Deep Learning Model Deploying on Embedded Skin Cancer Diagnostic Device
(2020)

Use of Machine Learning Approaches to Improve Non-invasive Skin Melanoma Diagnostic Method in Spectral Range 450 - 950nm
(2020)

Identification of the Most Informative Wavelengths for Non-invasive Melanoma Diagnostics in Spectral Region from 450 to 950 nm
(2020)

Challenges of Automatic Processing of Large Amount of Skin Lesion Multispectral Data
(2020)

Embedded Neural Network System for Microorganisms Growth Analysis
(2020)

Multispectral and Autofluorescence RGB Imaging for Skin Cancer Diagnostics
(2019)

Towards to Deep Neural Network Application with Limited Training Data: Synthesis of Melanoma's Diffuse Reflectance Spectral Images
(2019)

Imaging of LED-Excited Autofluorescence Photobleaching Rates for Skin Lesion Diagnostics
(2019)

Quality Enhancement of Multispectral Images for Skin Cancer Optical Diagnostics
(2018)