Upsampling Algorithms for Autoencoder Segmentation Neural Networks: A Comparison Study

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26220%2F19%3APU134013" target="_blank" >RIV/00216305:26220/19:PU134013 - isvavai.cz</a>

Výsledek na webu

<a href="https://ieeexplore.ieee.org/abstract/document/8970918" target="_blank" >https://ieeexplore.ieee.org/abstract/document/8970918</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/ICUMT48472.2019.8970918" target="_blank" >10.1109/ICUMT48472.2019.8970918</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Upsampling Algorithms for Autoencoder Segmentation Neural Networks: A Comparison Study

Popis výsledku v původním jazyce

This paper compares nine different upsampling methods used in convolutional neural networks in terms of accuracy and processing speed. The process of image segmentation using autoencoder neural networks consists of the image downsampling in the encoder and correspondingly of image upsampling in the decoder part of the network to achieve original image resolution. This paper focuses on the upsampling process in the decoder part of the standard U-Net neural network. Three different interpolations are compared with and without subsequent 1x1 convolution layers and three transpose convolution layers for image upsampling using different size convolutional cores. The experiment has shown that the best practical results were achieved using simple nearest neighbor interpolation upsampling taking into consideration the computational time needed. The network using nearest neighbor interpolation upsampling achieved pixel accuracy of 99.47% and has shown fast training time and convergence in comparison with other networks using different upsampling methods. The data used in this work consist of a lumbar CT spine segmentation dataset.

Název v anglickém jazyce

Upsampling Algorithms for Autoencoder Segmentation Neural Networks: A Comparison Study

Popis výsledku anglicky

This paper compares nine different upsampling methods used in convolutional neural networks in terms of accuracy and processing speed. The process of image segmentation using autoencoder neural networks consists of the image downsampling in the encoder and correspondingly of image upsampling in the decoder part of the network to achieve original image resolution. This paper focuses on the upsampling process in the decoder part of the standard U-Net neural network. Three different interpolations are compared with and without subsequent 1x1 convolution layers and three transpose convolution layers for image upsampling using different size convolutional cores. The experiment has shown that the best practical results were achieved using simple nearest neighbor interpolation upsampling taking into consideration the computational time needed. The network using nearest neighbor interpolation upsampling achieved pixel accuracy of 99.47% and has shown fast training time and convergence in comparison with other networks using different upsampling methods. The data used in this work consist of a lumbar CT spine segmentation dataset.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

Výsledek vznikl pri realizaci vícero projektů. Více informací v záložce Projekty.

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2019

Kód důvěrnosti údajů

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

Název statě ve sborníku

2019 11th International Congress on Ultra Modern Telecommunications and Control Systems and Workshops (ICUMT)

ISBN

978-1-7281-5764-1

ISSN

—

e-ISSN

—

Počet stran výsledku

5

Strana od-do

1-5

Název nakladatele

Neuveden

Místo vydání

Dublin

Místo konání akce

Dublin

Datum konání akce

28. 10. 2019

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

000540651700049