Sourati Jamshid, Gholipour Ali, Dy Jennifer G, Kurugol Sila, Warfield Simon K
Radiology Department, Boston Children's Hospital, 300 Longwood Avenue, Boston MA 02115.
Department of Electrical and Computer Engineering, Northeastern University, 360 Huntington Avenue, Boston MA 02115.
Deep Learn Med Image Anal Multimodal Learn Clin Decis Support (2018). 2018 Sep;11045:83-91. doi: 10.1007/978-3-030-00889-5_10. Epub 2018 Sep 20.
Deep learning with convolutional neural networks (CNN) has achieved unprecedented success in segmentation, however it requires large training data, which is expensive to obtain. Active Learning (AL) frameworks can facilitate major improvements in CNN performance with intelligent selection of minimal data to be labeled. This paper proposes a novel diversified AL based on Fisher information (FI) for the first time for CNNs, where gradient computations from backpropagation are used for efficient computation of FI on the large CNN parameter space. We evaluated the proposed method in the context of newborn and adolescent brain extraction problem under two scenarios: (1) semi-automatic segmentation of a particular subject from a different age group or with a pathology not available in the original training data, where starting from an inaccurate pre-trained model, we iteratively label small number of voxels queried by AL until the model generates accurate segmentation for that subject, and (2) using AL to build a universal model generalizable to all images in a given data set. In both scenarios, FI-based AL improved performance after labeling a small percentage (less than 0.05%) of voxels. The results showed that FI-based AL significantly outperformed random sampling, and achieved accuracy higher than entropy-based querying in transfer learning, where the model learns to extract brains of newborn subjects given an initial model trained on adolescents.
使用卷积神经网络（CNN）的深度学习在分割方面取得了前所未有的成功，然而它需要大量的训练数据，而获取这些数据成本高昂。主动学习（AL）框架可以通过智能选择最少的待标注数据来显著提升CNN的性能。本文首次为CNNs提出了一种基于Fisher信息（FI）的新型多样化主动学习方法，其中利用反向传播的梯度计算在大型CNN参数空间上高效计算FI。我们在两种场景下针对新生儿和青少年脑提取问题评估了所提出的方法：（1）从不同年龄组或具有原始训练数据中没有的病理学特征的特定受试者进行半自动分割，从一个不准确的预训练模型开始，我们迭代标注AL查询的少量体素，直到模型为该受试者生成准确的分割结果；（2）使用AL构建一个可推广到给定数据集中所有图像的通用模型。在这两种场景中，基于FI的主动学习在标注了一小部分（小于0.05%）体素后提高了性能。结果表明，基于FI的主动学习显著优于随机采样，并且在迁移学习中实现了高于基于熵的查询的准确率，即在给定一个在青少年上训练的初始模型的情况下，模型学习提取新生儿受试者的脑。
