Radim Burget

Assoc. Prof.

Brno University of Technology

Czech Republic

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About

Radim Burget is an Associate Professor at Brno University of Technology in the Czech Republic. His research areas encompass anomaly detection in artworks, automated medical diagnosis using imaging techniques, and the usability of semantic segmentation in traffic analysis. He has contributed significantly to the field with recent publications focusing on artificial intelligence applications in healthcare and art analysis.

Articles (20)

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2025

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2021

Collaborators (8)

Hsiao-Chun Lin

University of Tampere

FINLAND

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Juwairiya Siraj Khan

Aalborg University

DENMARK

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Daria Alekseeva

University of Tampere

FINLAND

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Aleksandr Ometov

University of Tampere

FINLAND

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Elena Simona Lohan

University of Tampere

FINLAND

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Jan Mizera

CZECH REPUBLIC

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Jiri Hosek

Brno University of Technology

CZECH REPUBLIC

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Jiri Haze

Head/ Ass. Prof.

Brno University of Technology

CZECH REPUBLIC

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Radim Burget

Research Interests

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About

Articles (20)

ForgAnoNet: A Neural Network for Anomaly Detection in Artworks Using X-ray and Visible Spectrum Imaging

Self-attention-guided residual deep neural network with multi-scale dilated feature extraction for automated gallbladder disease diagnosis in ultrasound imaging

Stress and Emotion Open Access Data: A Review on Datasets, Modalities, Methods, Challenges, and Future Research Perspectives

Enhancing Extended Reality Assisted Surgery through a Field-of-View Video Delivery Optimization

EnsArtNet: Ensemble neural network architecture for identifying art styles from paintings

Detection of post-COVID-19-related pulmonary diseases in X-ray images using Vision Transformer-based neural network

Corticosteroid treatment prediction using chest X-ray and clinical data

The effectiveness of glucocorticoid treatment in post-COVID-19 pulmonary involvement

IoT Mechatronic Access Control System ePRO 1.4

Reinterpreting Usability of Semantic Segmentation Approach for Darknet Traffic Analysis

DeepRespNet: A deep neural network for classification of respiratory sounds

Artificial-Intelligence-Driven Algorithms for Predicting Response to Corticosteroid Treatment in Patients with Post-Acute COVID-19

LoRaWAN: Lost for Localization?

Cardi-Net: A deep neural network for classification of cardiac disease using phonocardiogram signal

1D-FHRNet: Automatic Diagnosis of Fetal Acidosis from Fetal Heart Rate Signals

Automated detection of bioimages using novel deep feature fusion algorithm and effective high-dimensional feature selection approach

Ensemble based machine learning approach for prediction of glioma and multi-grade classification

The Rise of Wearable Devices during the COVID-19 Pandemic: A Systematic Review

SLINet: Dysphasia detection in children using deep neural network

Measurements of LoRaWAN Technology in Urban Scenarios: A Data Descriptor

Collaborators (8)

Hsiao-Chun Lin

Juwairiya Siraj Khan

Daria Alekseeva

Aleksandr Ometov

Elena Simona Lohan

Jan Mizera

Jiri Hosek

Jiri Haze

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