DeepPlayer: An open-source SignalPlant plugin for deep learning inference

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26220%2F22%3APU146235" target="_blank" >RIV/00216305:26220/22:PU146235 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/68081731:_____/23:00565476 RIV/00216208:11120/23:43924304 RIV/00064173:_____/23:43924304 RIV/00159816:_____/23:00079792

Výsledek na webu

<a href="https://onlinelibrary.wiley.com/doi/epdf/10.1002/spe.3159" target="_blank" >https://onlinelibrary.wiley.com/doi/epdf/10.1002/spe.3159</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1002/spe.3159" target="_blank" >10.1002/spe.3159</a>

Jazyk výsledku

angličtina

Název v původním jazyce

DeepPlayer: An open-source SignalPlant plugin for deep learning inference

Popis výsledku v původním jazyce

Background and Objective Machine learning has become a powerful tool in several computation domains. The most progressive way of machine learning, deep learning, has already surpassed several algorithms designed by human experts. It also applies to the field of biomedical signal processing. However, while many experts produce deep learning models, there is no software platform for signal processing, allowing the convenient use of pre-trained deep learning models and evaluating them using any inspected signal. This may also hinder understanding, interpretation, and explanation of results. For these reasons, we designed DeepPlayer. It is a plugin for the free signal processing software SignalPlant. The plugin allows loading deep learning models saved in the Open Neural Network Exchange (ONNX) file format and evaluating them on any given signal. Methods The DeepPlayer plugin and its graphical user interface were designed in C# programming language and the .NET framework. We used the inference library OnnxRuntime, which supports graphics card acceleration. The inference is executed in asynchronous tasks for a live preview and evaluation of the signals. Model outputs can be exported back to SignalPlant for further processing, such as peak detection or thresholding. Results We developed the DeepPlayer plugin to evaluate deep learning models in SignalPlant. The plugin keeps with SignalPlant's interactive work with signals, such as live preview or easy selection of associated signals. The plugin can load classification or regression models and allows standard pre-processing and post-processing methods. We prepared several deep learning models to test the plugin. Additionally, we provide a tutorial training script that outputs an ONNX format model with correctly set metadata information. These, and the source code of the DeepPlayer plugin, are publicly accessible via GitHub and Google Colab service. Conclusion The DeepPlayer plugin allows running deep learning models easily

Název v anglickém jazyce

DeepPlayer: An open-source SignalPlant plugin for deep learning inference

Popis výsledku anglicky

Background and Objective Machine learning has become a powerful tool in several computation domains. The most progressive way of machine learning, deep learning, has already surpassed several algorithms designed by human experts. It also applies to the field of biomedical signal processing. However, while many experts produce deep learning models, there is no software platform for signal processing, allowing the convenient use of pre-trained deep learning models and evaluating them using any inspected signal. This may also hinder understanding, interpretation, and explanation of results. For these reasons, we designed DeepPlayer. It is a plugin for the free signal processing software SignalPlant. The plugin allows loading deep learning models saved in the Open Neural Network Exchange (ONNX) file format and evaluating them on any given signal. Methods The DeepPlayer plugin and its graphical user interface were designed in C# programming language and the .NET framework. We used the inference library OnnxRuntime, which supports graphics card acceleration. The inference is executed in asynchronous tasks for a live preview and evaluation of the signals. Model outputs can be exported back to SignalPlant for further processing, such as peak detection or thresholding. Results We developed the DeepPlayer plugin to evaluate deep learning models in SignalPlant. The plugin keeps with SignalPlant's interactive work with signals, such as live preview or easy selection of associated signals. The plugin can load classification or regression models and allows standard pre-processing and post-processing methods. We prepared several deep learning models to test the plugin. Additionally, we provide a tutorial training script that outputs an ONNX format model with correctly set metadata information. These, and the source code of the DeepPlayer plugin, are publicly accessible via GitHub and Google Colab service. Conclusion The DeepPlayer plugin allows running deep learning models easily

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

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

Projekt

—

Návaznosti

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 periodika

SOFTWARE-PRACTICE & EXPERIENCE

ISSN

0038-0644

e-ISSN

1097-024X

Svazek periodika

neuveden

Číslo periodika v rámci svazku

October

Stát vydavatele periodika

GB - Spojené království Velké Británie a Severního Irska

Počet stran výsledku

10

Strana od-do

1-10

Kód UT WoS článku

000877357800001

EID výsledku v databázi Scopus

2-s2.0-85141344500