MicrAnt: Towards Regression Task Oriented Annotation Tool for Microscopic Images
The result's identifiers
Result code in 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>
Alternative codes found
RIV/00669806:_____/20:10418632
Result on the web
<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>
Alternative languages
Result language
angličtina
Original language name
MicrAnt: Towards Regression Task Oriented Annotation Tool for Microscopic Images
Original language description
Annotating a dataset for training a Supervised Machine Learning algorithm is time and annotator'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'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.
Czech name
—
Czech description
—
Classification
Type
D - Article in proceedings
CEP classification
—
OECD FORD branch
30502 - Other medical science
Result continuities
Project
<a href="/en/project/EF17_048%2F0007280" target="_blank" >EF17_048/0007280: Application of Modern Technologies in Medicine and Industry</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Others
Publication year
2020
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
Lecture Notes in Computer Science
ISBN
978-3-030-51001-5
ISSN
0302-9743
e-ISSN
—
Number of pages
10
Pages from-to
209-218
Publisher name
Springer
Place of publication
Heidelberg
Event location
Novi Sad; Serbia
Event date
Jul 16, 2020
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
—