Hyperparameter selection for dataset-constrained semantic segmentation: Practical machine learning optimization.

ABSTRACT:

Purpose/aim

This paper provides a pedagogical example for systematic machine learning optimization in small dataset image segmentation, emphasizing hyperparameter selections. A simple process is presented for medical physicists to examine hyperparameter optimization. This is also applied to a case-study, demonstrating the benefit of the method.

Materials and methods

An unrestricted public Computed Tomography (CT) dataset, with binary organ segmentation, was used to develop a multiclass segmentation model. To start the optimization process, a preliminary manual search of hyperparameters was conducted and from there a grid search identified the most influential result metrics. A total of 658 different models were trained in 2100 h, using 13 160 effective patients. The quantity

SUBMITTER: Boyd C 

PROVIDER: S-EPMC11633816 | biostudies-literature | 2024 Dec

REPOSITORIES: biostudies-literature