Detail publikace

Denoise pre-training for segmentation neural networks

KOLAŘÍK, M.

Originální název

Denoise pre-training for segmentation neural networks

Typ

článek ve sborníku ve WoS nebo Scopus

Jazyk

angličtina

Originální abstrakt

This paper proposes a method for pre-training segmentation neural networks on small datasets using unlabelled training data with added noise. The pre-training process helps the network with initial better weights settings for the training itself and also augments the training dataset when dealing with small labelled datasets especially in medical imaging. The experiment comparing results of pre-trained and not pre-trained networks on MRI brain segmentation task has shown that the denoise pre-training helps the network with faster training convergence without overfitting and achieving better results in all compared metrics even on very small datasets.

Klíčová slova

deep learning; denoising; neural network; pre-training; segmentation

Autoři

KOLAŘÍK, M.

Vydáno

25. 4. 2019

Nakladatel

Vysoké učení technické v Brně, Fakulta elektrotechniky a komunikačních technologií

Místo

Brno

ISBN

978-80-214-5735-5

Kniha

Proceedings of the 25th Conference STUDENT EEICT 2019

Strany od

739

Strany do

744

Strany počet

5

BibTex

@inproceedings{BUT157996,
  author="Martin {Kolařík}",
  title="Denoise pre-training for segmentation neural networks",
  booktitle="Proceedings of the 25th Conference STUDENT EEICT 2019",
  year="2019",
  pages="739--744",
  publisher="Vysoké učení technické v Brně, Fakulta elektrotechniky a komunikačních technologií",
  address="Brno",
  isbn="978-80-214-5735-5"
}