Ensemble learning via supervision augmentation for white matter hyperintensity segmentation - Guo, Xutao; Ye, Chenfei; Yang, Yanwu; Zhang, Li; Liang, Li; Lu, Shang; Lv, Haiyan; Guo, Chunjie; Ma, Ting (Frontiers in Neuroscience, 2022)
Since the ambiguous boundary of the lesion and inter-observer variability, white matter hyperintensity segmentation annotations are inherently noisy and uncertainties. On the other hand, the high capacity of deep neural networks (DNN) enables them to overfit labels with noise and uncertainty, which may lead to biased models with weak generalization ability. This challenge has been addressed by leveraging multiple annotations per image. However, multiple annotations are often not available in a real-world scenario. To mitigate the issue, this paper propose a supervision augmentation method (SA) and combine it with ensemble learning (SA-EN) to improve the generalization ability of the model. Supervision augmentation can obtain diverse supervision information by estimating the uncertainty of annotation in a real-world scenario that per image have only one ambiguous annotation. Then different base learners in ensemble learning are trained with diverse supervision information. The experimental results on two white matter hyperintensity segmentation datasets demonstrate that SA-EN get the optimal accuracy compared with other state-of-the-art ensemble methods. And SA-EN is more effective on small datasets, which is more suitable for medical image segmentation with few annotation. A quantitative study is presented to show the effect of ensemble size and the effectiveness of the ensemble model. Further, SA-EN can captures two types of uncertainty, aleatoric uncertainty modeled in supervision augmentation and epistemic uncertainty modeled in the ensemble learning.