V
Vitali Liauchuk
Researcher at National Academy of Sciences of Belarus
Publications - 25
Citations - 2760
Vitali Liauchuk is an academic researcher from National Academy of Sciences of Belarus. The author has contributed to research in topics: Tuberculosis & Question answering. The author has an hindex of 11, co-authored 24 publications receiving 1848 citations.
Papers
More filters
Book ChapterDOI
The 2021 ImageCLEF Benchmark: Multimedia Retrieval in Medical, Nature, Internet and Social Media Applications
Bogdan Ionescu,Henning Müller,Renaud Péteri,Asma Ben Abacha,Dina Demner-Fushman,Sadid A. Hasan,Mourad Sarrouti,Obioma Pelka,Christoph M. Friedrich,Alba García Seco de Herrera,Janadhip Jacutprakart,Vassili Kovalev,Serge Kozlovski,Vitali Liauchuk,Yashin Dicente Cid,Jon Chamberlain,Adrian F. Clark,Antonio Campello,Hassan Moustahfid,Thomas Oliver,Abigail Schulz,Paul Brie,Raul Berari,Dimitri Fichou,Andrei Tauteanu,Mihai Dogariu,Liviu-Daniel Stefan,Mihai Gabriel Constantin,Jérôme Deshayes,Adrian Popescu +29 more
TL;DR: The 2019 edition of ImageCLEF Labs as mentioned in this paper was the 19th edition of the evaluation initiative, which promoted the evaluation of technologies for annotation, indexing and retrieval of visual data with the aim of providing information access to large collections of images in various usage scenarios and domains.
Overview of ImageCLEF 2018
Bogdan Ionescu,Henning Müller,Mauricio Villegas,Alba García Seco de Herrera,Carsten Eickhoff,Vincent Andrearczyk,Yashin Dicente Cid,Vitali Liauchuk,Vassili Kovalev,Sadid A. Hasan,Yuan Ling,Oladimeji Farri,Joey Liu,Matthew P. Lungren,Duc-Tien Dang-Nguyen,Luca Piras,Michael Riegler,Liting Zhou,Mathias Lux,Cathal Gurrin +19 more
Journal ArticleDOI
Recognition of underlying surface using a convolutional neural network on a single-board computer
TL;DR: Development results for hardware and software system (micromodule), which detects and classifies underlying surface images of Earth and uses convolutional neural network based on MobileNetV2 architecture for image classification is presented.
Book ChapterDOI
Highlighting Tumor Borders Using Generalized Gradient
TL;DR: This paper presents a generalized approach for computing image gradient aimed at detecting unclear and in certain circumstances even completely invisible borders in large 2D and 3D texture images.