论文标题
D-Lema：深度学习合奏从多个注释 - 应用到皮肤病变细分
D-LEMA: Deep Learning Ensembles from Multiple Annotations -- Application to Skin Lesion Segmentation
论文作者
论文摘要
由于人类注释者和模棱两可的边界的固有差异，医疗图像分割注释即使是专家之间的观察者间和观察者内变异的变化也是如此。利用注释者对图像的意见集合是估计黄金标准的有趣方法。尽管在有监督的环境中具有单个图像的单个注释的训练模型已经进行了广泛的研究，从而将其培训推广到使用包含每个图像多个注释的数据集工作仍然是一个相当尚未探索的问题。在本文中，我们提出了一种在训练深层模型时处理注释者分歧的方法。为此，我们提出了一个贝叶斯完全卷积网络（FCN）的合奏，以实现分割任务，以考虑聚集多个基础真理注释的两个主要因素：（1）在训练数据中处理矛盾的注释，培训数据源自通量间分歧，并从基础模型模型的融合来提高信心校准，并提高基础模型的预测。我们证明了我们的方法在ISIC档案中的出色性能，并通过对PH2和Dermofit数据集的跨数据库评估来探讨我们提出的方法的概括性能。
Medical image segmentation annotations suffer from inter- and intra-observer variations even among experts due to intrinsic differences in human annotators and ambiguous boundaries. Leveraging a collection of annotators' opinions for an image is an interesting way of estimating a gold standard. Although training deep models in a supervised setting with a single annotation per image has been extensively studied, generalizing their training to work with datasets containing multiple annotations per image remains a fairly unexplored problem. In this paper, we propose an approach to handle annotators' disagreements when training a deep model. To this end, we propose an ensemble of Bayesian fully convolutional networks (FCNs) for the segmentation task by considering two major factors in the aggregation of multiple ground truth annotations: (1) handling contradictory annotations in the training data originating from inter-annotator disagreements and (2) improving confidence calibration through the fusion of base models' predictions. We demonstrate the superior performance of our approach on the ISIC Archive and explore the generalization performance of our proposed method by cross-dataset evaluation on the PH2 and DermoFit datasets.
