论文标题
一个通用合奏的深度卷积神经网络，用于半监督医学图像分割
A generic ensemble based deep convolutional neural network for semi-supervised medical image segmentation
论文作者
论文摘要
基于深度学习的图像细分已经在许多医学应用（例如病变量化，器官检测等）中实现了最先进的性能。但是，大多数方法都依赖于有监督的学习，这需要大量高质量的标记数据。数据注释通常是一个非常耗时的过程。为了解决这个问题，我们提出了一个基于深卷积神经网络（DCNN）的图像分割的通用半监督学习框架。最初使用一些带注释的培训样本对基于编码器的DCNN进行培训。然后将最初训练的模型复制到子模型中，并使用未标记数据的随机子集与先前迭代中训练的模型生成的伪标签进行了迭代。在最终迭代中，子模型的数量逐渐减少到一个。我们在公共大挑战数据集上评估了针对皮肤病变细分的方法。通过合并未标记的数据，我们的方法能够在完全监督的模型学习之外显着改善。
Deep learning based image segmentation has achieved the state-of-the-art performance in many medical applications such as lesion quantification, organ detection, etc. However, most of the methods rely on supervised learning, which require a large set of high-quality labeled data. Data annotation is generally an extremely time-consuming process. To address this problem, we propose a generic semi-supervised learning framework for image segmentation based on a deep convolutional neural network (DCNN). An encoder-decoder based DCNN is initially trained using a few annotated training samples. This initially trained model is then copied into sub-models and improved iteratively using random subsets of unlabeled data with pseudo labels generated from models trained in the previous iteration. The number of sub-models is gradually decreased to one in the final iteration. We evaluate the proposed method on a public grand-challenge dataset for skin lesion segmentation. Our method is able to significantly improve beyond fully supervised model learning by incorporating unlabeled data.
