Výsledky vyhledávání - "Michal Kolárik"

Akademický článek

Classification of the drifting data streams using heterogeneous diversified dynamic class-weighted ensemble

Publikováno v: PeerJ Computer Science, Vol 7, p e459 (2021)

Data streams can be defined as the continuous stream of data coming from different sources and in different forms. Streams are often very dynamic, and its underlying structure usually changes over time, which may result to a phenomenon called concept

Externí odkaz: https://doaj.org/article/3278eff486bd410dbddeb298b9a6e9a2

Zobrazit plný text záznamu

Akademický článek

Lung Ultrasound Reduces Chest X-rays in Postoperative Care after Thoracic Surgery: Is There a Role for Artificial Intelligence?—Systematic Review

Autor: Marek Malík, Anton Dzian, Martin Števík, Štefánia Vetešková, Abdulla Al Hakim, Maroš Hliboký, Ján Magyar, Michal Kolárik, Marek Bundzel, František Babič

Publikováno v: Diagnostics, Vol 13, Iss 18, p 2995 (2023)

Background: Chest X-ray (CXR) remains the standard imaging modality in postoperative care after non-cardiac thoracic surgery. Lung ultrasound (LUS) showed promising results in CXR reduction. The aim of this review was to identify areas where the eval

Externí odkaz: https://doaj.org/article/54952b7ad6bc43939b3be586c182cbb8

Zobrazit plný text záznamu

Plný text ve formátu HTML

Akademický článek

Perturbation-Based Explainable AI for ECG Sensor Data

Autor: Ján Paralič, Michal Kolárik, Zuzana Paraličová, Oliver Lohaj, Adam Jozefík

Publikováno v: Applied Sciences, Vol 13, Iss 3, p 1805 (2023)

Deep neural network models have produced significant results in solving various challenging tasks, including medical diagnostics. To increase the credibility of these black-box models in the eyes of doctors, it is necessary to focus on their explaina

Externí odkaz: https://doaj.org/article/4ce6ac8459e64f7a9568a4c069886ac1

Zobrazit plný text záznamu

Detecting the Absence of Lung Sliding in Ultrasound Videos Using 3D Convolutional Neural Networks

Autor: Michal Kolárik, Martin Sarnovský, Ján Paralič

Publikováno v: Acta Polytechnica Hungarica. 20:47-60

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::24d03b5225bd1023520075474cc791c8
https://doi.org/10.12700/aph.20.6.2023.6.3

Zobrazit plný text záznamu

Akademický článek

Explainability of deep learning models in medical video analysis: a survey

Autor: Michal Kolarik, Martin Sarnovsky, Jan Paralic, Frantisek Babic

Publikováno v: PeerJ Computer Science, Vol 9, p e1253 (2023)

Deep learning methods have proven to be effective for multiple diagnostic tasks in medicine and have been performing significantly better in comparison to other traditional machine learning methods. However, the black-box nature of deep neural networ

Externí odkaz: https://doaj.org/article/5dd5382d84244e0c8cba919ecc5a0052

Zobrazit plný text záznamu

Vyhledávací nástroje:

Upřesnit hledání