Sökning: WFRF:(Strand Robin 1978 )
> (2020-2024) >
Classification of r...
Classification of rotation-invariant biomedical images using equivariant neural networks
-
- Bengtsson Bernander, Karl (författare)
- Uppsala universitet,Bildanalys och människa-datorinteraktion,Avdelningen Vi3
-
- Sintorn, Ida-Maria, 1976- (författare)
- Uppsala universitet,Bildanalys och människa-datorinteraktion,Avdelningen Vi3,Vironova AB, Stockholm, Sweden.
-
- Strand, Robin, 1978- (författare)
- Uppsala universitet,Bildanalys och människa-datorinteraktion,Avdelningen Vi3
-
visa fler...
-
- Nyström, Ingela, 1967- (författare)
- Uppsala universitet,Bildanalys och människa-datorinteraktion,Avdelningen Vi3
-
visa färre...
-
(creator_code:org_t)
- Springer Nature, 2024
- 2024
- Engelska.
-
Ingår i: Scientific Reports. - : Springer Nature. - 2045-2322. ; 14:1
- Relaterad länk:
-
https://doi.org/10.1...
-
visa fler...
-
https://uu.diva-port... (primary) (Raw object)
-
https://urn.kb.se/re...
-
https://doi.org/10.1...
-
visa färre...
Abstract
Ämnesord
Stäng
- Transmission electron microscopy (TEM) is an imaging technique used to visualize and analyze nano-sized structures and objects such as virus particles. Light microscopy can be used to diagnose diseases or characterize e.g. blood cells. Since samples under microscopes exhibit certain symmetries, such as global rotation invariance, equivariant neural networks are presumed to be useful. In this study, a baseline convolutional neural network is constructed in the form of the commonly used VGG16 classifier. Thereafter, it is modified to be equivariant to the p4 symmetry group of rotations of multiples of 90 degrees using group convolutions. This yields a number of benefits on a TEM virus dataset, including higher top validation set accuracy by on average 7.6% and faster convergence during training by on average 23.1% of that of the baseline. Similarly, when training and testing on images of blood cells, the convergence time for the equivariant neural network is 7.9% of that of the baseline. From this it is concluded that augmentation strategies for rotation can be skipped. Furthermore, when modelling the accuracy versus amount of TEM virus training data with a power law, the equivariant network has a slope of - 0.43 compared to - 0.26 of the baseline. Thus the equivariant network learns faster than the baseline when more training data is added. This study extends previous research on equivariant neural networks applied to images which exhibit symmetries to isometric transformations.
Ämnesord
- NATURVETENSKAP -- Data- och informationsvetenskap -- Bioinformatik (hsv//swe)
- NATURAL SCIENCES -- Computer and Information Sciences -- Bioinformatics (hsv//eng)
- NATURVETENSKAP -- Data- och informationsvetenskap -- Datorteknik (hsv//swe)
- NATURAL SCIENCES -- Computer and Information Sciences -- Computer Engineering (hsv//eng)
Publikations- och innehållstyp
- ref (ämneskategori)
- art (ämneskategori)
Hitta via bibliotek
Till lärosätets databas