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
Proceedings Article
Overview of ImageCLEFtuberculosis 2021 - CT-based Tuberculosis Type Classification
Book ChapterDOI
ImageCLEF 2019: Multimedia Retrieval in Lifelogging, Medical, Nature, and Security Applications
Bogdan Ionescu,Henning Müller,Henning Müller,Renaud Péteri,Duc-Tien Dang-Nguyen,Luca Piras,Michael Riegler,Minh-Triet Tran,Mathias Lux,Cathal Gurrin,Yashin Dicente Cid,Vitali Liauchuk,Vassili Kovalev,Asma Ben Abacha,Sadid A. Hasan,Vivek V. Datla,Joey Liu,Dina Demner-Fushman,Obioma Pelka,Christoph M. Friedrich,Jon Chamberlain,Adrian F. Clark,Alba García Seco de Herrera,Narciso Garcia,Ergina Kavallieratou,Carlos R. del Blanco,Carlos Cuevas Rodríguez,Nikos Vasillopoulos,Konstantinos Karampidis +28 more
TL;DR: This paper presents an overview of the foreseen ImageCLEF 2019 lab that will be organized as part of the Conference and Labs of the Evaluation Forum - CLEF Labs 2019, and expects the new tasks to attract at least as many researchers for 2019.
ImageCLEF 2018: Lesion-based TB-descriptor for CT Image Analysis.
Vitali Liauchuk,Aleh Tarasau,Eduard Snezhko,Vassili Kovalev,Andrei Gabrielian,Alex Rosenthal +5 more
TL;DR: It was shown that combination of lesion-based TB-descriptor and Random Forests classifier allows achieving the best performance in TB type classification and TB severity scoring subtasks.
ImageCLEF 2019: Projection-based CT Image Analysis for TB Severity Scoring and CT Report Generation.
TL;DR: This paper presents an approach for automated analysis of 3D Computed Tomography images based on representing the 3D CT data as a set of 2D projection images along all three axes that reduces the dimensionality of the input data and therefore allows using less complicated models for image classification tasks.
Journal ArticleDOI
Biomedical Image Recognition in Pulmonology and Oncology with the Use of Deep Learning
TL;DR: In this article, the authors demonstrate the efficiency of the deep learning methods and state-of-the-art architectures of deep convolutional neural networks applied to solve different types of problems in pulmonology and oncology.