  A critical challenge for tumour segmentation models is the ability to adapt
to diverse clinical settings, particularly when applied to poor-quality
neuroimaging data. The uncertainty surrounding this adaptation stems from the
lack of representative datasets, leaving top-performing models without exposure
to common artifacts found in MRI data throughout Sub-Saharan Africa (SSA). We
replicated a framework that secured the 2nd position in the 2022 BraTS
competition to investigate the impact of dataset composition on model
performance and pursued four distinct approaches through training a model with:
1) BraTS-Africa data only (train_SSA, N=60), 2) BraTS-Adult Glioma data only
(train_GLI, N=1251), 3) both datasets together (train_ALL, N=1311), and 4)
through further training the train_GLI model with BraTS-Africa data
(train_ftSSA). Notably, training on a smaller low-quality dataset alone
(train_SSA) yielded subpar results, and training on a larger high-quality
dataset alone (train_GLI) struggled to delineate oedematous tissue in the
low-quality validation set. The most promising approach (train_ftSSA) involved
pre-training a model on high-quality neuroimages and then fine-tuning it on the
smaller, low-quality dataset. This approach outperformed the others, ranking
second in the MICCAI BraTS Africa global challenge external testing phase.
These findings underscore the significance of larger sample sizes and broad
exposure to data in improving segmentation performance. Furthermore, we
demonstrated that there is potential for improving such models by fine-tuning
them with a wider range of data locally.
