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Pregled bibliografske jedinice broj: 1243636

Using Autoencoders to Reduce Dimensionality of DICOM Metadata

(Medical University of Graz, Graz, Austria) Mateja Napravnik; Robert Baždarić; Damir Miletić; Franko Hržić; Sebastian Tschauner; Mihaela Mamula; Ivan Štajduhar
Using Autoencoders to Reduce Dimensionality of DICOM Metadata // Proc. of the International Conference on Electrical, Computer, Communications and Mechatronics Engineering (ICECCME)
Malé, Maldivi, 2022. str. 1-6 doi:10.1109/ICECCME55909.2022.9988310 (predavanje, međunarodna recenzija, cjeloviti rad (in extenso), znanstveni)

CROSBI ID: 1243636 Za ispravke kontaktirajte CROSBI podršku putem web obrasca

Naslov
Using Autoencoders to Reduce Dimensionality of DICOM Metadata

Autori
Mateja Napravnik ; Robert Baždarić ; Damir Miletić ; Franko Hržić ; Sebastian Tschauner ; Mihaela Mamula ; Ivan Štajduhar

Kolaboracija
Medical University of Graz, Graz, Austria

Vrsta, podvrsta i kategorija rada
Radovi u zbornicima skupova, cjeloviti rad (in extenso), znanstveni

Izvornik
Proc. of the International Conference on Electrical, Computer, Communications and Mechatronics Engineering (ICECCME) / - , 2022, 1-6

Skup
International Conference on Electrical, Computer, Communications and Mechatronics Engineering (ICECCME 2022)

Mjesto i datum
Malé, Maldivi, 16.11.2022. - 18.11.2022

Vrsta sudjelovanja
Predavanje

Vrsta recenzije
Međunarodna recenzija

Ključne riječi
DICOM ; Autoencoders ; Medical Imaging ; Clustering

Sažetak
Digital Imaging and Communication in Medicine (DICOM) is a standardized format for storing medical images enriched with descriptive data. It consists of a number of informative header tags. E.g., these tags contain information concerning imaging technique, patient weight, patient age and so on. In machine learning, these tags can, among other things, be used for categorization, classification, and general manipulation of DICOM data. However, because of their number and variety, using them for automation undoubtedly impacts computational performance. To tackle this problem, the possibility of using auto encoders is explored in this manuscript. It was hypothesized that clustering data compressed with autoencoders can achieve the same results as when working with raw - uncompressed data. To support this claim, clustering of compressed and uncom- pressed data was performed on a dataset of 25, 000 DICOM files from the Clinical Hospital Centre Rijeka PACS. The results show no significant difference between compressed and uncompressed data (p > 0.05), thus confirming that equally good clustering results can be accomplished using a smaller representation.

Izvorni jezik
Engleski

Znanstvena područja
Računarstvo, Temeljne tehničke znanosti, Interdisciplinarne tehničke znanosti, Temeljne medicinske znanosti



POVEZANOST RADA

Projekti:
HRZZ-IP-2020-02-3770 - Strojno učenje za prijenos znanja u medicinskoj radiologiji (RadiologyNET) (Štajduhar, Ivan, HRZZ - 2020-02) ( CroRIS)

Ustanove:
Tehnički fakultet, Rijeka,
Klinički bolnički centar Rijeka

Profili:

Avatar Url Mateja Napravnik (autor)

Avatar Url Damir Miletić (autor)

Avatar Url Ivan Štajduhar (autor)

Avatar Url Franko Hržić (autor)

Avatar Url Robert Baždarić (autor)

Poveznice na cjeloviti tekst rada:

Pristup cjelovitom tekstu rada doi doi.org doi.org

Poveznice na istraživačke podatke:

doi.org

Citiraj ovu publikaciju:

(Medical University of Graz, Graz, Austria) Mateja Napravnik; Robert Baždarić; Damir Miletić; Franko Hržić; Sebastian Tschauner; Mihaela Mamula; Ivan Štajduhar
Using Autoencoders to Reduce Dimensionality of DICOM Metadata // Proc. of the International Conference on Electrical, Computer, Communications and Mechatronics Engineering (ICECCME)
Malé, Maldivi, 2022. str. 1-6 doi:10.1109/ICECCME55909.2022.9988310 (predavanje, međunarodna recenzija, cjeloviti rad (in extenso), znanstveni)
(Medical University of Graz, Graz, Austria) (Medical University of Graz, Graz, Austria) Mateja Napravnik, Robert Baždarić, Damir Miletić, Franko Hržić, Sebastian Tschauner, Mihaela Mamula & Ivan Štajduhar (2022) Using Autoencoders to Reduce Dimensionality of DICOM Metadata. U: Proc. of the International Conference on Electrical, Computer, Communications and Mechatronics Engineering (ICECCME) doi:10.1109/ICECCME55909.2022.9988310.
@article{article, year = {2022}, pages = {1-6}, DOI = {10.1109/ICECCME55909.2022.9988310}, keywords = {DICOM, Autoencoders, Medical Imaging, Clustering}, doi = {10.1109/ICECCME55909.2022.9988310}, title = {Using Autoencoders to Reduce Dimensionality of DICOM Metadata}, keyword = {DICOM, Autoencoders, Medical Imaging, Clustering}, publisherplace = {Mal\'{e}, Maldivi} }
@article{article, year = {2022}, pages = {1-6}, DOI = {10.1109/ICECCME55909.2022.9988310}, keywords = {DICOM, Autoencoders, Medical Imaging, Clustering}, doi = {10.1109/ICECCME55909.2022.9988310}, title = {Using Autoencoders to Reduce Dimensionality of DICOM Metadata}, keyword = {DICOM, Autoencoders, Medical Imaging, Clustering}, publisherplace = {Mal\'{e}, Maldivi} }

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