论文标题
深度残留3D U-NET，用于肺中结节的关节分割和纹理分类
Deep Residual 3D U-Net for Joint Segmentation and Texture Classification of Nodules in Lung
论文作者
论文摘要
在这项工作中，我们提出了一种用于肺结节分割的方法，它们的纹理分类以及随后的肺CT图像的后续建议。我们的方法由基于流行的U-NET体系结构家族的神经网络模型组成，但针对联合结节分段及其纹理分类任务进行了修改，以及基于合奏的后续建议模型。在LNDB医学成像挑战中评估了该解决方案，并在最终排行榜上产生了最佳的结节分段结果。
In this work we present a method for lung nodules segmentation, their texture classification and subsequent follow-up recommendation from the CT image of lung. Our method consists of neural network model based on popular U-Net architecture family but modified for the joint nodule segmentation and its texture classification tasks and an ensemble-based model for the follow-up recommendation. This solution was evaluated within the LNDb medical imaging challenge and produced the best nodule segmentation result on the final leaderboard.
