论文标题
W-NET：使用多任务深神经网络同时分割多动物视网膜结构
W-net: Simultaneous segmentation of multi-anatomical retinal structures using a multi-task deep neural network
论文作者
论文摘要
在医学图像分析中，多个解剖结构的分割非常重要。在这项研究中，我们提出了一个$ \ MATHCAL {W} $ - 基于多任务学习（MTL）方案的视网膜图像中的视盘（OD）和渗出液。我们引入了班级平衡的损失和多任务加权损失，以减轻不平衡的问题并提高$ \ Mathcal {W} $ - NET的稳健性和概括属性。我们通过在两个公共数据集e \ _ophtha \ _ex和diaretdb1上应用五倍的交叉验证实验来证明方法的有效性。对于OD分段，我们获得了94.76 \％和95.73 \％的F1得分，而渗出率分割为92.80 \％和94.14 \％。为了进一步证明所提出的方法的概括属性，我们在DRIONS-DB数据集上应用了训练有素的模型以进行OD分割，并在Messidor数据集上应用了渗出液分割。我们的结果表明，通过选择每个任务的最佳权重，基于MTL的$ \ MATHCAL {W} $ - NET优于在每个任务上单独训练的单独型号。代码和预训练的模型将在以下网址提供：\ url {https://github.com/fundusresearch/mtl_for_od_od_and_and_exudates.git}。
Segmentation of multiple anatomical structures is of great importance in medical image analysis. In this study, we proposed a $\mathcal{W}$-net to simultaneously segment both the optic disc (OD) and the exudates in retinal images based on the multi-task learning (MTL) scheme. We introduced a class-balanced loss and a multi-task weighted loss to alleviate the imbalanced problem and to improve the robustness and generalization property of the $\mathcal{W}$-net. We demonstrated the effectiveness of our approach by applying five-fold cross-validation experiments on two public datasets e\_ophtha\_EX and DiaRetDb1. We achieved F1-score of 94.76\% and 95.73\% for OD segmentation, and 92.80\% and 94.14\% for exudates segmentation. To further prove the generalization property of the proposed method, we applied the trained model on the DRIONS-DB dataset for OD segmentation and on the MESSIDOR dataset for exudate segmentation. Our results demonstrated that by choosing the optimal weights of each task, the MTL based $\mathcal{W}$-net outperformed separate models trained individually on each task. Code and pre-trained models will be available at: \url{https://github.com/FundusResearch/MTL_for_OD_and_exudates.git}.
