论文标题
脑肿瘤分割的互惠对抗学习：解决方案的解决方案挑战2021分段任务
Reciprocal Adversarial Learning for Brain Tumor Segmentation: A Solution to BraTS Challenge 2021 Segmentation Task
论文作者
论文摘要
本文提出了一种基于对抗性学习的培训方法，用于脑肿瘤分割任务。在这个概念中，3D分割网络从双重互惠对手学习方法中学习。为了增强整个分割预测的概括并使分割网络稳健，我们通过在原始患者数据上添加一些噪音来遵守虚拟对抗训练方法。通过将批评家纳入定量主观裁判，分割网络可以从与分割结果相关的不确定性信息中学习。我们在RSNA-ASNR-MICCAI BRATS 2021数据集上培训和评估了网络体系结构。我们在在线验证数据集上的性能如下：骰子相似性得分为81.38％，90.77％和85.39％； 21.83毫米的Hausdorff距离（95 \％）分别用于增强肿瘤，整个肿瘤和肿瘤核的8.56毫米。同样，我们的方法达到了最终测试数据集中的13.48 mm，6.32 mm，6.32 mm和16.98 mm的骰子相似性评分，以及84.55％，90.46％和85.30％的骰子分数。总体而言，我们提出的方法在每个肿瘤子区域的分割准确性方面产生了更好的性能。我们的代码实现可在https://github.com/himashi92/vizviva_brats_2021上公开获得
This paper proposes an adversarial learning based training approach for brain tumor segmentation task. In this concept, the 3D segmentation network learns from dual reciprocal adversarial learning approaches. To enhance the generalization across the segmentation predictions and to make the segmentation network robust, we adhere to the Virtual Adversarial Training approach by generating more adversarial examples via adding some noise on original patient data. By incorporating a critic that acts as a quantitative subjective referee, the segmentation network learns from the uncertainty information associated with segmentation results. We trained and evaluated network architecture on the RSNA-ASNR-MICCAI BraTS 2021 dataset. Our performance on the online validation dataset is as follows: Dice Similarity Score of 81.38%, 90.77% and 85.39%; Hausdorff Distance (95\%) of 21.83 mm, 5.37 mm, 8.56 mm for the enhancing tumor, whole tumor and tumor core, respectively. Similarly, our approach achieved a Dice Similarity Score of 84.55%, 90.46% and 85.30%, as well as Hausdorff Distance (95\%) of 13.48 mm, 6.32 mm and 16.98 mm on the final test dataset. Overall, our proposed approach yielded better performance in segmentation accuracy for each tumor sub-region. Our code implementation is publicly available at https://github.com/himashi92/vizviva_brats_2021
