Ensemble Learning with Residual Transformer for Brain Tumor Segmentation

• URL: http://arxiv.org/abs/2308.00128v1
• Date: Mon, 31 Jul 2023 19:47:33 GMT
• Title: Ensemble Learning with Residual Transformer for Brain Tumor Segmentation
• Authors: Lanhong Yao, Zheyuan Zhang, Ulas Bagci
• Abstract summary: This paper proposes a novel network architecture that integrates Transformers into a self-adaptive U-Net. On the BraTS 2021 dataset (3D), our model achieves 87.6% mean Dice score and outperforms the state-of-the-art methods.
• Score: 2.0654955576087084
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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