| Title: |
Hyperparameter selection for dataset‐constrained semantic segmentation: Practical machine learning optimization |
| Authors: |
Boyd, C; Brown, GC; Kleinig, TJ; Mayer, W; Dawson, J; Jenkinson, M; Bezak, E |
| Publisher Information: |
Wiley |
| Publication Year: |
2025 |
| Collection: |
Oxford University Research Archive (ORA) |
| Description: |
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 of results was analyzed using random forest regression, identifying relative hyperparameter impact. Results: Metric implied segmentation quality (accuracy 96.8%, precision 95.1%) and visual inspection were found to be mismatched. In this work batch normalization was most important, but performance varied with hyperparameters and metrics selected. Targeted grid‐search optimization and random forest analysis of relative hyperparameter importance, was an easily implementable sensitivity analysis approach. Conclusion: The proposed optimization method gives a systematic and quantitative approach to something intuitively understood, that hyperparameters change model performance. Even just grid search optimization with random forest analysis presented here can be informative within hardware and data quality/availability limitations, adding confidence to model validity and minimize decision‐making risks. By providing a guided methodology, this work helps medical physicists to improve their model optimization, irrespective of specific challenges posed by datasets and model design. |
| Document Type: |
article in journal/newspaper |
| Language: |
English |
| Relation: |
https://doi.org/10.1002/acm2.14542 |
| DOI: |
10.1002/acm2.14542 |
| Availability: |
https://doi.org/10.1002/acm2.14542; https://ora.ox.ac.uk/objects/uuid:9ef9621c-fbed-4644-b78d-bc772709dba0 |
| Rights: |
info:eu-repo/semantics/openAccess ; CC Attribution (CC BY) |
| Accession Number: |
edsbas.9A8B968D |
| Database: |
BASE |