Laiz Pablo, Vitrià Jordi, Wenzek Hagen, Malagelada Carolina, Azpiroz Fernando, Seguí Santi
Department of Mathematics and Computer Science, Universitat de Barcelona, Barcelona, Spain.
Department of Mathematics and Computer Science, Universitat de Barcelona, Barcelona, Spain.
Comput Med Imaging Graph. 2020 Dec;86:101794. doi: 10.1016/j.compmedimag.2020.101794. Epub 2020 Oct 3.
Wireless capsule endoscopy is a medical procedure used to visualize the entire gastrointestinal tract and to diagnose intestinal conditions, such as polyps or bleeding. Current analyses are performed by manually inspecting nearly each one of the frames of the video, a tedious and error-prone task. Automatic image analysis methods can be used to reduce the time needed for physicians to evaluate a capsule endoscopy video. However these methods are still in a research phase. In this paper we focus on computer-aided polyp detection in capsule endoscopy images. This is a challenging problem because of the diversity of polyp appearance, the imbalanced dataset structure and the scarcity of data. We have developed a new polyp computer-aided decision system that combines a deep convolutional neural network and metric learning. The key point of the method is the use of the Triplet Loss function with the aim of improving feature extraction from the images when having small dataset. The Triplet Loss function allows to train robust detectors by forcing images from the same category to be represented by similar embedding vectors while ensuring that images from different categories are represented by dissimilar vectors. Empirical results show a meaningful increase of AUC values compared to state-of-the-art methods. A good performance is not the only requirement when considering the adoption of this technology to clinical practice. Trust and explainability of decisions are as important as performance. With this purpose, we also provide a method to generate visual explanations of the outcome of our polyp detector. These explanations can be used to build a physician's trust in the system and also to convey information about the inner working of the method to the designer for debugging purposes.
无线胶囊内镜检查是一种用于可视化整个胃肠道并诊断肠道疾病（如息肉或出血）的医疗程序。目前的分析是通过人工检查视频的几乎每一帧来进行的，这是一项繁琐且容易出错的任务。自动图像分析方法可用于减少医生评估胶囊内镜视频所需的时间。然而，这些方法仍处于研究阶段。在本文中，我们专注于胶囊内镜图像中的计算机辅助息肉检测。这是一个具有挑战性的问题，因为息肉外观多样、数据集结构不均衡且数据稀缺。我们开发了一种新的息肉计算机辅助决策系统，该系统结合了深度卷积神经网络和度量学习。该方法的关键点是使用三元组损失函数，目的是在数据集较小时改进从图像中提取特征。三元组损失函数通过迫使同一类别的图像由相似的嵌入向量表示，同时确保不同类别的图像由不同的向量表示，从而能够训练出强大的检测器。实证结果表明，与现有方法相比，AUC值有显著提高。在考虑将这项技术应用于临床实践时，良好的性能并不是唯一的要求。决策的可信度和可解释性与性能同样重要。为此，我们还提供了一种方法来生成我们的息肉检测器结果的视觉解释。这些解释可用于建立医生对系统的信任，也可用于向设计者传达有关方法内部工作原理的信息，以便进行调试。
