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One-Pass Multi-Task Networks With Cross-Task Guided Attention for Brain Tumor Segmentation

机译:单通多任务网络,具有脑肿瘤分割的交叉任务引导注意

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Class imbalance has emerged as one of the major challenges for medical image segmentation. The model cascade (MC) strategy, a popular scheme, significantly alleviates the class imbalance issue via running a set of individual deep models for coarse-to-fine segmentation. Despite its outstanding performance, however, this method leads to undesired system complexity and also ignores the correlation among the models. To handle these flaws in the MC approach, we propose in this paper a light-weight deep model, i.e., the One-pass Multi-task Network (OM-Net) to solve class imbalance better than MC does, while requiring only one-pass computation for brain tumor segmentation. First, OM-Net integrates the separate segmentation tasks into one deep model, which consists of shared parameters to learn joint features, as well as task-specific parameters to learn discriminative features. Second, to more effectively optimize OM-Net, we take advantage of the correlation among tasks to design both an online training data transfer strategy and a curriculum learning-based training strategy. Third, we further propose sharing prediction results between tasks, which enables us to design a cross-task guided attention (CGA) module. By following the guidance of the prediction results provided by the previous task, CGA can adaptively recalibrate channel-wise feature responses based on the category-specific statistics. Finally, a simple yet effective post-processing method is introduced to refine the segmentation results of the proposed attention network. Extensive experiments are conducted to demonstrate the effectiveness of the proposed techniques. Most impressively, we achieve state-of-the-art performance on the BraTS 2015 testing set and BraTS 2017 online validation set. Using these proposed approaches, we also won joint third place in the BraTS 2018 challenge among 64 participating teams. The code is publicly available at https://github.com/chenhong-zhou/OM-Net.
机译:类不平衡已成为医学图像细分的主要挑战之一。模型级联(MC)策略是一种流行的计划,通过运行一组个体深度模型来减轻级别的不平衡问题,以获得粗到细分的细分。然而,尽管其表现出色,但这种方法导致了不希望的系统复杂性,并且还忽略了模型之间的相关性。为了在MC方法中处理这些缺陷,我们提出了一个轻量级深层模型,即单通式多任务网络(OM-Net)来解决比MC更好的阶级不平衡,同时只需要一个 - 通过计算脑肿瘤分割。首先,OM-Net将单独的分段任务集成到一个深度模型中,该模型包括共享参数,以学习联合特征,以及特定于任务的参数以学习歧视特征。其次,为了更有效地优化OM-Net,我们利用任务之间的相关性来设计在线培训数据传输策略和基于课程的培训策略。第三,我们进一步提出了任务之间共享预测结果,这使我们能够设计跨任务引导注意力(CGA)模块。通过遵循先前任务提供的预测结果的指导,CGA可以根据特定于类别的统计数据自适应地重新校准通道 - 方向特征响应。最后,引入了简单但有效的后处理方法以优化所提出的注意网络的分割结果。进行广泛的实验以证明所提出的技术的有效性。最令人印象深刻,我们在Brats 2015测试集和Brats 2017在线验证集上实现了最先进的表现。使用这些拟议的方法,我们还在64个参与团队中的Brats 2018挑战中获得了联合第三名。该代码公开可在https://github.com/chenhong-zhou/om-net上获得。

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