Abstract
In Sub-Saharan Africa (SSA), the utilization of lower-quality Magnetic Resonance Imaging (MRI) technology raises questions about the applicability of machine learning methods for clinical tasks. This study aims to provide a robust deep learning-based brain tumor segmentation (BraTS) method tailored for the SSA population using a threefold approach. Firstly, the impact of domain shift from the SSA training data on model efficacy was examined, revealing no significant effect. Secondly, a comparative analysis of 3D and 2D full-resolution models using the nnU-Net framework indicates similar performance of both the models trained for 300 epochs achieving a five-fold cross-validation score of 0.93. Lastly, addressing the performance gap observed in SSA validation as opposed to the relatively larger BraTS glioma (GLI) validation set, two strategies are proposed: fine-tuning SSA cases using the GLI+SSA best-pretrained 2D fullres model at 300 epochs, and introducing a novel neural style transfer-based data augmentation technique for the SSA cases. This investigation underscores the potential of enhancing brain tumor prediction within SSA's unique healthcare landscape.
Abstract (translated)
在撒哈拉以南非洲（SSA），使用较低质量的磁共振成像（MRI）技术引发了关于机器学习方法在临床任务中适用性的疑问。本研究旨在通过三管齐下的方法为撒哈拉以南非洲人群提供一种稳健的基于深度学习的大脑肿瘤分割（BraTS）方法。首先，该研究考察了从SSA训练数据中域偏移对模型效果的影响，结果显示没有显著影响。其次，使用nnU-Net框架对比分析3D和2D全分辨率模型，发现两种模型在经过300轮训练后达到五倍交叉验证分数为0.93时性能相似。最后，为了弥补SSA验证集相对于较大的BraTS胶质瘤（GLI）验证集中观察到的性能差距，提出了两项策略：一是使用GLI+SSA的最佳预训练2D全分辨率模型对SSA病例进行微调；二是为SSA病例引入一种基于神经风格转换的数据增强技术。这项研究强调了在撒哈拉以南非洲独特的医疗环境中提升脑肿瘤预测潜力的可能性。
URL
https://arxiv.org/abs/2501.04734
