Medical Image Analysis with NVIDIA Jetson GPU Modules

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F22%3A10249012" target="_blank" >RIV/61989100:27240/22:10249012 - isvavai.cz</a>

Výsledek na webu

<a href="https://link.springer.com/chapter/10.1007/978-3-030-84910-8_25" target="_blank" >https://link.springer.com/chapter/10.1007/978-3-030-84910-8_25</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-3-030-84910-8_25" target="_blank" >10.1007/978-3-030-84910-8_25</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Medical Image Analysis with NVIDIA Jetson GPU Modules

Popis výsledku v původním jazyce

Medical imaging and image analysis are important elements of modern diagnostic and treatment methods. Intelligent image processing, pattern recognition, and data analysis can be leveraged to introduce a new level of detection, segmentation, and, in general, understanding to medical image analysis. However, modern image analysis methods such as deep neural networks are often connected with significant computational complexity, slowing their adoption. Recent embedded systems such as the NVIDIA Jetson general-purpose GPUs became a viable platform for efficient execution of some computational models. This work analyzes the performance and time and energy costs of several neural models for medical image analysis on different kinds of NVIDIA Jetson modules. The experiments are performed with the lung X-ray medical images in connection with the COVID-19 disease. (C) 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Název v anglickém jazyce

Medical Image Analysis with NVIDIA Jetson GPU Modules

Popis výsledku anglicky

Medical imaging and image analysis are important elements of modern diagnostic and treatment methods. Intelligent image processing, pattern recognition, and data analysis can be leveraged to introduce a new level of detection, segmentation, and, in general, understanding to medical image analysis. However, modern image analysis methods such as deep neural networks are often connected with significant computational complexity, slowing their adoption. Recent embedded systems such as the NVIDIA Jetson general-purpose GPUs became a viable platform for efficient execution of some computational models. This work analyzes the performance and time and energy costs of several neural models for medical image analysis on different kinds of NVIDIA Jetson modules. The experiments are performed with the lung X-ray medical images in connection with the COVID-19 disease. (C) 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

<a href="/cs/project/EF17_049%2F0008425" target="_blank" >EF17_049/0008425: Platforma pro výzkum orientovaný na Průmysl 4.0 a robotiku v ostravské aglomeraci</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2022

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

Lecture Notes in Networks and Systems. Volume 312

ISBN

978-3-030-84909-2

ISSN

2367-3370

e-ISSN

2367-3389

Počet stran výsledku

10

Strana od-do

233-242

Název nakladatele

Springer

Místo vydání

Cham

Místo konání akce

Tchaj-čung

Datum konání akce

1. 9. 2021

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

000704003000025