COMPYDA: An online tool for verifying the similarity of image datasets

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216224%3A14330%2F24%3A00135497" target="_blank" >RIV/00216224:14330/24:00135497 - isvavai.cz</a>

Výsledek na webu

<a href="https://ieeexplore.ieee.org/document/10635415" target="_blank" >https://ieeexplore.ieee.org/document/10635415</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/ISBI56570.2024.10635415" target="_blank" >10.1109/ISBI56570.2024.10635415</a>

Jazyk výsledku

angličtina

Název v původním jazyce

COMPYDA: An online tool for verifying the similarity of image datasets

Popis výsledku v původním jazyce

Nowadays, when the vast majority of biomedical research relies on machine learning methods, paying attention to the meaningfulness of the data we work with is crucial. This is especially true if the dataset is scarce and we are required to use various augmentation techniques to enlarge training sets. The additional data are, however, not guaranteed to have the same characteristics as the original data, and therefore, the augmented set may be inconsistent. This can subsequently lead to incorrect training of biomedical image analysis methods, which may result in biased classification, detection, segmentation, or tracking results. In this paper, we present an online tool called COMPYDA, that allows users to easily assess the similarity of a pair of datasets using well-founded, commonly used statistic methods. COMPYDA guides users through univariate and multivariate analyses and helps them understand and explain dataset differences to ascertain a compatible dataset for further training. The tool is available at: https://cbia.fi.muni.cz/compyda/

Název v anglickém jazyce

COMPYDA: An online tool for verifying the similarity of image datasets

Popis výsledku anglicky

Nowadays, when the vast majority of biomedical research relies on machine learning methods, paying attention to the meaningfulness of the data we work with is crucial. This is especially true if the dataset is scarce and we are required to use various augmentation techniques to enlarge training sets. The additional data are, however, not guaranteed to have the same characteristics as the original data, and therefore, the augmented set may be inconsistent. This can subsequently lead to incorrect training of biomedical image analysis methods, which may result in biased classification, detection, segmentation, or tracking results. In this paper, we present an online tool called COMPYDA, that allows users to easily assess the similarity of a pair of datasets using well-founded, commonly used statistic methods. COMPYDA guides users through univariate and multivariate analyses and helps them understand and explain dataset differences to ascertain a compatible dataset for further training. The tool is available at: https://cbia.fi.muni.cz/compyda/

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

30200 - Clinical medicine

Projekt

<a href="/cs/project/EF18_046%2F0016045" target="_blank" >EF18_046/0016045: Modernizace národní infrastruktury pro biologické a medicínské zobrazování Czech-BioImaging</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

2024

Kód důvěrnosti údajů

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

Název statě ve sborníku

2024 IEEE International Symposium on Biomedical Imaging (ISBI)

ISBN

9798350313345

ISSN

1945-7928

e-ISSN

1945-8452

Počet stran výsledku

5

Strana od-do

1-5

Název nakladatele

IEEE

Místo vydání

Athens, Greece

Místo konání akce

Athens

Datum konání akce

1. 1. 2024

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

001305705101140