Ensemble Learning with Residual Transformer for Brain Tumor Segmentation
Lanhong Yao Zheyuan Zhang Ulas Bagci
Brain tumor segmentation is an active research area due to the difficulty in
delineating highly complex shaped and textured tumors as well as the failure of
the commonly used U-Net architectures. The combination of different neural
architectures is among the mainstream research recently, particularly the
combination of U-Net with Transformers because of their innate attention
mechanism and pixel-wise labeling. Different from previous efforts, this paper
proposes a novel network architecture that integrates Transformers into a
self-adaptive U-Net to draw out 3D volumetric contexts with reasonable
computational costs. We further add a residual connection to prevent
degradation in information flow and explore ensemble methods, as the evaluated
models have edges on different cases and sub-regions. On the BraTS 2021 dataset
(3D), our model achieves 87.6% mean Dice score and outperforms the
state-of-the-art methods, demonstrating the potential for combining multiple
architectures to optimize brain tumor segmentation.