论文标题
通过元学习预测医学成像分割方法的得分
Predicting Scores of Medical Imaging Segmentation Methods with Meta-Learning
论文作者
论文摘要
深度学习导致了许多医学成像任务的最新结果，例如分割不同的解剖结构。随着深度学习出版物的数量增加和公开可用的代码，为新任务选择模型的方法变得更加复杂，而时间和（计算）资源受到限制。有效选择模型的可能解决方案是元学习方法，一种学习方法，其中使用模型的先前性能来预测新任务的性能。我们研究了对不同器官和模式的十个数据集进行分割的元学习。我们提出了四种通过元功能来表示每个数据集的方法：一种基于图像的统计特征，三个基于深度学习功能。我们使用支持向量回归和深层神经网络来了解元功能与先前模型性能之间的关系。在三个外部测试数据集上，这些方法给出了真实性能的0.10以内的骰子得分。这些结果证明了元学习在医学成像中的潜力。
Deep learning has led to state-of-the-art results for many medical imaging tasks, such as segmentation of different anatomical structures. With the increased numbers of deep learning publications and openly available code, the approach to choosing a model for a new task becomes more complicated, while time and (computational) resources are limited. A possible solution to choosing a model efficiently is meta-learning, a learning method in which prior performance of a model is used to predict the performance for new tasks. We investigate meta-learning for segmentation across ten datasets of different organs and modalities. We propose four ways to represent each dataset by meta-features: one based on statistical features of the images and three are based on deep learning features. We use support vector regression and deep neural networks to learn the relationship between the meta-features and prior model performance. On three external test datasets these methods give Dice scores within 0.10 of the true performance. These results demonstrate the potential of meta-learning in medical imaging.
