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Effective and efficient multitask learning for brain tumor segmentation
Journal of Real-Time Image Processing ( IF 3 ) Pub Date : 2020-04-06 , DOI: 10.1007/s11554-020-00961-4
Guohua Cheng , Jingliang Cheng , Mengyan Luo , Linyang He , Yan Tian , Ruili Wang

Recently, brain tumor segmentation has achieved great success, partially because of deep learning-based relation exploration and multiscale analysis. However, the computational complexity hinders the real-time application. In this paper, we propose a revised multitask learning approach in which a lightweight network with only two scales is adopted to segment different kinds of tumor regions. Moreover, we design a hybrid hard sampling method that considers both sample sparsity and effectiveness. Extensive experiments on the BraTS19 segmentation challenge dataset have shown that our proposed method improves the Dice coefficient by a margin of 0.4–1.0 for different kinds of brain tumor regions and obtains results that are competitive with state-of-the-art brain tumor segmentation approaches.



中文翻译:

有效且高效的多任务学习,用于脑肿瘤分割

最近,脑肿瘤分割取得了巨大的成功,部分原因是基于深度学习的关系探索和多尺度分析。但是,计算复杂度阻碍了实时应用。在本文中,我们提出了一种修订的多任务学习方法,其中仅采用具有两个尺度的轻量级网络来分割不同种类的肿瘤区域。此外,我们设计了一种兼顾样本稀疏性和有效性的混合硬采样方法。在BraTS19分割挑战数据集上进行的大量实验表明,我们提出的方法将Dice系数针对不同类型的脑肿瘤区域提高了0.4–1.0的余量,并获得了与最新的脑肿瘤分割方法相竞争的结果。

更新日期:2020-04-21
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