  Machine 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.
