MicrAnt: Towards Regression Task Oriented Annotation Tool for Microscopic Images

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11140%2F20%3A10418632" target="_blank" >RIV/00216208:11140/20:10418632 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/00669806:_____/20:10418632

Výsledek na webu

<a href="https://link.springer.com/chapter/10.1007%2F978-3-030-51002-2_15" target="_blank" >https://link.springer.com/chapter/10.1007%2F978-3-030-51002-2_15</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-3-030-51002-2_15" target="_blank" >10.1007/978-3-030-51002-2_15</a>

Jazyk výsledku

angličtina

Název v původním jazyce

MicrAnt: Towards Regression Task Oriented Annotation Tool for Microscopic Images

Popis výsledku v původním jazyce

Annotating a dataset for training a Supervised Machine Learning algorithm is time and annotator&apos;s attention intensive. Our goal was to create a tool that would enable us to create annotations of the dataset with minimal demands on expert&apos;s time. Inspired by applications such as Tinder, we have created an annotation tool for describing microscopic images. A graphical user interface is used to select from a couple of images the one with the higher value of the examined parameter. Two experiments were performed. The first compares the speed of annotation of our application with the commonly used tool for processing microscopic images. In the second experiment, the texture description was compared with the annotations from MicrAnt application and commonly used application. The results showed that the processing time using our application is 3 times lower and the Spearman coefficient increases by 0.05 than using a commonly used application. In an experiment, we have shown that the annotations processed using our application increase the correlation of the studied parameter and texture descriptors compared with manual annotations.

Název v anglickém jazyce

MicrAnt: Towards Regression Task Oriented Annotation Tool for Microscopic Images

Popis výsledku anglicky

Annotating a dataset for training a Supervised Machine Learning algorithm is time and annotator&apos;s attention intensive. Our goal was to create a tool that would enable us to create annotations of the dataset with minimal demands on expert&apos;s time. Inspired by applications such as Tinder, we have created an annotation tool for describing microscopic images. A graphical user interface is used to select from a couple of images the one with the higher value of the examined parameter. Two experiments were performed. The first compares the speed of annotation of our application with the commonly used tool for processing microscopic images. In the second experiment, the texture description was compared with the annotations from MicrAnt application and commonly used application. The results showed that the processing time using our application is 3 times lower and the Spearman coefficient increases by 0.05 than using a commonly used application. In an experiment, we have shown that the annotations processed using our application increase the correlation of the studied parameter and texture descriptors compared with manual annotations.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

30502 - Other medical science

Projekt

<a href="/cs/project/EF17_048%2F0007280" target="_blank" >EF17_048/0007280: Aplikace moderních technologií v medicíně a průmyslu</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2020

Kód důvěrnosti údajů

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

Název statě ve sborníku

Lecture Notes in Computer Science

ISBN

978-3-030-51001-5

ISSN

0302-9743

e-ISSN

—

Počet stran výsledku

10

Strana od-do

209-218

Název nakladatele

Springer

Místo vydání

Heidelberg

Místo konání akce

Novi Sad; Serbia

Datum konání akce

16. 7. 2020

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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