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Publication detail
KOLAŘÍK, M.
Original Title
Denoise pre-training for segmentation neural networks
Type
conference paper
Language
English
Original Abstract
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.
Keywords
deep learning; denoising; neural network; pre-training; segmentation
Authors
Released
25. 4. 2019
Publisher
Vysoké učení technické v Brně, Fakulta elektrotechniky a komunikačních technologií
Location
Brno
ISBN
978-80-214-5735-5
Book
Proceedings of the 25th Conference STUDENT EEICT 2019
Pages from
739
Pages to
744
Pages count
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" }