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AuthorAl-Kababji, Ayman
AuthorBensaali, Faycal
AuthorDakua, Sarada Prasad
Available date2022-12-29T07:34:43Z
Publication Date2022
Publication NameCommunications in Computer and Information Science
ResourceScopus
URIhttp://dx.doi.org/10.1007/978-3-031-08277-1_17
URIhttp://hdl.handle.net/10576/37819
AbstractMachine learning and computer vision techniques have influenced many fields including the biomedical one. The aim of this paper is to investigate the important concept of schedulers in manipulating the learning rate (LR), for the liver segmentation task, throughout the training process, focusing on the newly devised OneCycleLR against the ReduceLRonPlateau. A dataset, published in 2018 and produced by the Medical Segmentation Decathlon Challenge organizers, called Task 8 Hepatic Vessel (MSDC-T8) has been used for testing and validation. The reported results that have the same number of maximum epochs (75), and are the average of 5-fold cross-validation, indicate that ReduceLRonPlateau converges faster while maintaining a similar or even better loss score on the validation set when compared to OneCycleLR. The epoch at which the peak LR occurs perhaps should be made early for the OneCycleLR such that the super-convergence feature can be observed. Moreover, the overall results outperform the state-of-the-art results from the researchers who published the liver masks for this dataset. To conclude, both schedulers are suitable for medical segmentation challenges, especially the MSDC-T8 dataset, and can be used confidently in rapidly converging the validation loss with a minimal number of epochs. 2022, Springer Nature Switzerland AG.
SponsorThis publication was made possible by an Award [GSRA6-2-0521-19034] from Qatar National Research Fund (a member of Qatar Foundation). The contents herein are solely the responsibility of the authors. Moreover, the HPC resources and services used in this work were provided by the Research Computing group in Texas A&M University at Qatar. Research Computing is funded by the Qatar Foundation for Education, Science and Community Development (http://www.qf.org.qa).
Languageen
PublisherSpringer Science and Business Media Deutschland GmbH
SubjectConvolutional neural network
Dice score
Learning rate
Liver delineation
Schedulers
Semantic segmentation
TitleScheduling Techniques for Liver Segmentation: ReduceLRonPlateau vs OneCycleLR
TypeConference Paper
Pagination204-212
Volume Number1589 CCIS
dc.accessType Abstract Only


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